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mbsConsulting Environmental Soil Science A Marketing Research Study Prepared By: Steven Diamond Bryce Youngquist Mariella Horna 4ARRIOTT CHOOL OF MA.NAGEMENT Table of Contents Preliminaries Acknowledgments 3 Letter of Completion 4 Executive Summary 5 Research Objectives 8 Methodology 10 Limitations 13 General Survey Findings 15 Environmental Soil Science Survey Findings 36 Conclusions & Recommendations Conclusions 71 Recommendations 71 Appendices Appendix A: Statistically Significant Cross Tabulations 75 Part 1  General Survey 76 Part 2  Environmental Soil Science Survey 118 Appendix B: Statically Insignificant Cross Tabulations 135 Appendix C: Transcript from Focus Group 145 Appendix D: Surveys 154 Part 1  GeneraL 155 Part 2  Environmental Soil Science Majors 157 Part 3  High School 160 Appendix E: Responses to OpenEnded Question 6 in the General Survey 162 Appendix F: Letter of Engagement. 169 2 Acknowledgments We would like to thank Richard Terry from the Plant and Animal Sciences Department for his assistance in the project. He was very responsive and accommodating throughout the process. We would like to thank him for providing us with this educational experience. Also, we would like to thank Dr. Geurts, Sheldon Nelson, Von Jolley, Bruce Webb, and Levi Jackson for their help. Dr. Geurts and Levi Jackson assisted us through the fine details of the project while Sheldon Nelson, Von Jolley, and Bruce Webb helped us in formulating our survey. We are pleased to present to you the marketing research report for the Plant and Animal Sciences Department. We appreciate the opportunity we were given to work you during the life of the research. We have strived to meet your expectations as outlined in the letter of engagement and we are confident that the results will be valuable to the future direction of the Environmental Soil Science program. Letter of Completion March 23, 2006 Richard Terry Brigham Young University Plant and Animal Sciences 259 WIDB Provo, UT 84602 Dear Mr. Terry, avu MAltRlOTT SCHOOL Of MANAGEMENT Contained in this report are the following main sections: .:. Executive Summary .:. Research Objectives .:. Research Methodology .:. Research Limitations .:. Findings .:. Conclusion & Recommendations .:. Appendices We enjoyed the educational experience this project afforded us. Thank you for the time and effort that you have imparted throughout this project. We wish you the best in your future endeavors. Sincerely, Steven Diamond MarielJa Hoena Bryce Youngquist 4 Executive Summary 5 Executive Summary Research Objectives Before we commenced the research process, three main objectives were set forth by mbsConsulting. The research objectives are as follows: .:. Determine the potential future path of the Environmental Soil Science program .:. Determine the overall appeal of changing the name of the Environmental Soil Science program .:. Obtain an understanding of a student's mindset when selecting a major Conclusions Based on our flndings, we came to the following conclusions: Out of 349 respondents, 211 (60.5%) agreed that the name Environmental Science is more appealing than the current name of Environmental Soil Science. Also this was a concern brought up in the focus group, implying that it was too narrow of a study (see Appendix C). Almost 60% of the students responded by saying they have not thoroughly searched all the majors offered at BYU. In fact, 50% of the respondents only "looked" into 1 to 2 different majors before selecting the one they are in now. On a 7point scale (1 =Completely Agree, 7 =Completely Disagree) 75% of the respondents completely agreed that future career plans influence the selection of their major. This goes handinhand with the 60% response that job availability after graduation is very important on a separate 7point scale (1= Very Important, 7 = Not at all). In the general survey, there was great importance placed on the variety of classes offered with 55% of the respondents assigning a 1 or 2 on the 7point scale (1 =Very Important, 7 = Not at all). Also in the ESS survey, 50% of the students expressed interest in more Plant and Animal Sciences courses offered during spring and summer terms. 6 Recommendations Based on our findings and conclusions, we recommend the following actions to be taken: • We recommend that the Plant and Animal Sciences department changes the name of the Environmental Soil Science program to Environmental Science. We feel that by making this simple change, the program will be able to increase its enrollment by portraying a broader scope of study. • Considering that majority of BYU students do not thoroughly search all of the majors offered, the Plant and Animal Sciences department should increase awareness of freshman and sophomores at BYU. The department needs to take a proactive stance towards attracting more underclassmen as they search majors. We also recommend analyzing the results of the High School survey. • As the Plant and Animal Sciences department increases awareness they should also place emphasis on informing students about the future career possibilities. Our research shows that the students placed the most importance on job availability following graduation. We recommend that the Plant and Animal Sciences department look into the methods used by other departments on campus for internship opportunities and job recruiting. • We recommend that the department look into the possibilities of offering more spring and summer courses. In addition, half of the responding Environmental Soil Science students said they thought of leaving the program. By having a wider array of time offerings and variety of classes offered, this may help to increase the retention and overall program numbers in the end. 7 Objectives 8 Research Objectives Before we commenced the research process, three main objectives were set forth by mbsConsulting. The research objectives are as follows: .:. Determine the potential future path of the Environmental Soil Science program .:. Determine the overall appeal of changing the name of the Environmental Soil Science program .:. Obtain an understanding of a student's mindset when selecting a major Exploratory Research The exploratory research for this project was performed to determine the best methods for completing our project in the most accurate manner possible. We conducted an indepth focus group to gain more insight into the mindset of students when determining their area of study. Focus Group As part of our exploratory research, we conducted a focus group with some BYU students: Jenny Cox, Russell Memory, Baxter Oliphant, and Blaine Bateman. Our primary objective was to determine which questions would be most appropriate for the survey. Initial responses from the students were mixed. It was a very productive discussion. An overall consensus of selecting a major was what the student's personal interest and what they want to do for a career. The students seemed to all agree that the name of the Environmental Soil Science major should be changed. A transcript of the focus group discussion is included in the appendices of this report. Primary Research Our primary research consisted of survey design, determjning the sample size, and statistical testing. Surveys Our primary method of research was conducted though two separate surveys. Our surveys were distributed using SurveyZ, an online program used to send the surveys and compile the results. One survey was sent to the Marriott School students and the Biology and Agriculture students. Our results on this survey are based on the approximately 345 respondents. The second survey was given to the 15 students currently studying Environmental Soil Science. Our results on this survey are based on 10 respondents. From the results of these two surveys, we were able to compile and analyze the results used in this study. In addition to these two surveys, our original intention was to conduct a third survey of hjgh school students. Due to time constraints, we were unable to analyze the results of this survey. We did, however, distribute it, and the responses will be included in supplemental material along with this study. Survey Design As noted above, a focus group was used to aid in the creation of our surveys. From the responses of our focus group, we compiled an initial preliminary survey. This survey was used to determine the clarity and usefulness of the initial questions. 11 Our survey was designed to analyze data from the different groups we sampled. The questions included respondents' general attitudes toward their current and past areas of study, toward potential name changes to the Environmental Soil Science program, and demographics. The survey's design and questions were reviewed by Dr. Geurts, Richard Terry and Levi Jackson. The final surveys, including the unanalyzed high school survey, are contained in the appendices of this report. Determining Sample Size It is important to have a correct sample size in order to get the most accurate results. We were able to obtain sample email addresses from approximately 1500 students in the Marriott School of Business and another 698 emails from the College of Biology and Agriculture. The 698 emails from the College of Biology and Agriculture were limited to freshmen and sophomores. In the case of this study, the sample size (the number of emails) was provided by the client. In addition to these two sample groups, we also conducted another survey of those students studying Environmental Soil Science. There are currently 15 total students in the program, 13 of which are in the undergraduate program. Statistical Testing After inputting data from the results of our surveys, we used SPSS software as our primary method for statistical testing. SPSS is software that allows one to analyze data using Chisquare, correlation, regression, as well as through other means. ChiSquare Analysis The principal method of analysis for this project was Chisquare analysis through SPSS software. Chisquare analysis is a method to determine the association between two variables. The output shows whether the association is statistically significant or not. If a level of significance is given at .1, we would observe that there is a 10% chance that the results are not true, or in other words, a 90% chance that they are true. For this project, we used a 90% confidence level. The Chisquared analysis results are contained in the appendices of this report. 12 Limitations 13 Limitations of Our Research While we endeavored to conduct this project with as little error as possible, limitations still exist to this study. The primary limitations were nonsampling error and sampling error. NonSampling Error Nonsampling error signifies a human error in research. While we endeavored to conduct the research with as few errors as possible, a few errors unfortunately occurred. Representative Error In the general survey distributed to the students in the Marriott School and the College of Biology and Agriculture, the sample provided to us for the College of Biology and Agriculture included only freshmen and sophomores. No juniors or seniors were represented. Also, this survey was not distributed to representatives of all the different colleges and programs at BYU. NonResponse/Response Error Nonresponse error is the omission of an answer by the respondent, while response error is an untruthful answer by the respondent. We found a small number of surveys in which nonresponse and/or response error occurred. We did not use this data in our analysis. Sampling Error Sampling error signifies that a sample may not be representative due to random chance. For this project we used a 90% confidence level, meaning that there is a 10% chance of sampling error. From the data collected, 210 were males and 137 were females. This could be due to the fact that the members in the two groups sampled have a higher propensity to be male than female due to the professions found therein. 1 General Survey Findings 15 Question 1a: Please rank the following factors in selecting your major. (1mo~t influential to 5least influential)  Personal Interest Personal Interest 350 .'.". 300 cCP 250 'C C 200 0'c". 150 CP a:: 100 0 50 == 0 1 2 3 4 5 1 =Most Influential, 5 =Least Influential Note: Overall this was the highest ranked attribute out of the five. Out of the 384 responses, 74.7% agreed that personal interest has the most influence in selecting a major. 16 Question 1b: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Popularity of major Popularity of Major 160 .,...............           ..............,.,..",, 140 +~ ~ 120 +                      ~o 100+ c. 80  f            U) ~ 60  1       . . . : .      '0 40 + == 20l=.. o 1 2 3 4 5 1 =Most Influential, 5 =Least Influential Note: The popularity of the major seemed to be the least important factor overall with 36.5% choosing it as "least influential." However, we found statistical significance between popularity and gender (chisquared value of .029), and also between popularity and department (chisquared value of .000). See graphs below. 17 q1b 50.0% 45.0% 40.0% 35.0% 30.0% • Male 25.0% • Ferrale 20.0% 15.0% 10.0% 5.0% 0.0% 1 2 3 4 5 q1b 540.0%0"[ '0%tjllllll • Marriott • BioAg 2 3 4 5 18 Question 1c: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Family influences Family Influences 180 ~~~~~~, 160 + 1rI            l ~ 140 11 c~ 120 11 g 100 1 1 Q, ~ 80 k........=o1 a: '0 60 '**' 40 +1 20 +=11=1 11 01L_L,LL,LJL.,..lJL.,..lJL....j 2 3 4 5 1 = Most Influential, 5 = Least Influential Question 1d: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Scholarship Availability Scholarship Availability 2 3 4 5 1~o ,.....,:r..,.....,..,..,.,.....,......."..,..".......,..",..,..,.,..,~,.,.,.,..,..,.~....,.....=..,....,....,....,....,......"",.,, 1/1 160 +.,........,,::,=': C 140 +=' 8 120 1...".== g 100 I~';~ 80 I~~"aG: l 60 1:'' '0 40 1 '* 20 o 1 =Most Influential, 5 =Least Influential Note: Perhaps one of the most interesting results was to find that scholarship availability was one of the least influential factors with 44.3% ranking it as the least influential. As shown in the graph below. underclassman placed a bit more importance on this factor than did the upperclassman (chisquared value of .001). q1d 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 2 3 4 5 • FreshJSoph. • ~perclass 20 Question 1e: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Potential Income Potential Income 250 III 200 C Gl 'C 150 c0 Co III aG:l 100 '0 '*I: 50 0 2 3 4 5 1 =Most Influential, 5 = Least Influential Note: Potential income was the second most influential factor when choosing a major. In the graph below, male 'respondents are shown to place more importance on this factor than female respondents, perhaps due to future family considerations (chisquared value of .007). q1e 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 2 3 4 5 ~ ~ 21 Questions 2a _ 2i: Rate the importance you place on each of the following characteristics of a major (1 =Very Important, 7 = Not at all). 2a General Atmosphere/Environment 2 3 4 5 6 7 140 ~:"_.,..., CUl 120 1 ~ 100 8. 80 l3 60 a: 40 ',0., 20 o 1 = Very Important, 7 = Not at all 2b Academic Qualifications of Professors 2 34 5 6 7 1 =Very Important, 7 = Not at all 160 ........           . , .  ,  . .     . " ~ 140 ~ 120 1 c 100 &. 80 Ul ~ 60 '0 40 ,., 20 o 22 2c .Availability of Professors for Consultations 120 .I.I. 100 c: CI) 'C 80 c: 0c. 60 III CI) a: 40 0 20 '*" 0 2 3 1 =Very Important, 7 = Not at all 2d Starting Base Salary After Graduation 2 3 .4 5 6 7 1 = Very Important, 7 = Not at all 140 ..,......~.,.,.,....,....,...",._ _....,." ,..",..,.,,.....,..,.,......,..........,,...,.,""7"""~,...., S 120 l Iii 100+ i : '5 40 '#: 20 o 23 2e National Reputation of Program 2 3 140 120 III 100 c~c 80 i CII 60 a: '0 40 '# 29 0 1 =Very Important, 7 = Not at all 2f Variety of Class Offerings 2 3456 7 1 = Very Important, 7 =Not at all 24 2h 2 34 5 6 7 1 :: Very ,mportant,7 :: Not at aU 2 3 4 5 6 7 1 :: Very Important, 7:: Not at all Times c'asses are Offered 250 .1C!l 200 Q) "c 150 0 Q. l/I 100. Q) ct '0 50 =*I: 0 Job Availability Following Graduation 2g 90 I'"~...,...,..".~.,...,.......,..,....~.......,..., 80 r .1!l 70+ ~ 60 t' 8. 50+ ~ 40 30 '0~ 20 10 o 120 111 100 c CIl 80 'Cc 0D. 60 111 ! 40 0 :It 20 0 2i Preparation for Graduate School 2 345 6 7 1 =Very Important, 7 = Not at all No~e: The characteristics upon which the most importance was placed were job availability and natIOnal reputation" of program. The next most important characteristics were preparation for graduate school, general atmosphere/environment, and starting base salary. 26 Questions 3a" 3e: Thinking of different majors, rate your agreement with the following statements based on your experience (1 = Completely Agree, 7 = Completely Disagree) 3a " A Bad Teacher Will Influence Your Selection of Major 120 Ul 100 Ct 80 60 Ul Q) a: 40 15 =I*: 20 0 1 2 3 "4 5 6 7 1 = Completely Agree, 7 = Completely Disagree 3b " A Good Teacher Will Influence Your Selection of Major 180 .0.. 160 C 140 Q) 1cJ 120 0 100 Q. 0 80 .! 60 '0 40 * 20 0 1 2 3 4 5 6 7 1 =Completely Agree, 7 =Completely Disagree 27 3c . Would You Say You·ve Thoroughly Searched All the Majors Offered at BYU 90 80 70 60 50 40 30 '0:tI: 20 10 0 1 2 3 4 5 6 7 1 = Completely Agree, 7 =Completely Disagree Notes: 41 % of the respondents ranked this question with a 6 and a 7 on a 7point scale, saying that they have not thoroughly searched all the majors offered at BYU. Compare these results with th'ose from qu.estion 4 where 50% of respondents "looked" into 12 different majors. 3d The Nam~ of a ProgramIMajor Can Influence Its Popularity 120 J'I!:i 100 C1) "c: 80 8. 60 i 40 0 20 :tI: 0 1 2 3 4 5 6 7 1 = Completely Agree, 7 = Completely Disagree 28 3e Your Future Career Plans Influence Your Selection of a Major 300 In 250 ,l: ~ 200 [ 150 In aG:l 100 '0 50 * 0 1 2 3 4 5 6 7 1,= Completely Agree, 7 =Completely Disagree Note: This was the most highlyranked factor in influencing major selection, with 75% of the respondents completely agreeing. 29 Question 4: How many times have you II lookedII into different majors (Taken different classes to see if you want to go with a certain major)? IILooked ll into Different Majors 200 180 .0.. 160 c(1) 140 .'tJ C 120 0 100 C. 0 80 (1) .a..:. 60 0 40 ~ 20 0 0 12 34 56 7+ Times Note: There were some significant chisquared values we found as we tested this question with questions: q2a, q3a, and q3c. 30 Question 5: How many times have you "officially" changed your major on the books? (Changed it with the school records) "Officially" Changed Your Major on the Books 180 th 160 ~ 140 'cC 120 8 100 m 80 ~ 60 '0 40 #: 20 . 0 0 12 34 56 7+ Times 31 Question 7: Which program/major name has more appeal: Which ProgramlMajor Name has More Appeal 250 I/) c 200 CI.l 'Cc 150 0cen. CI.l 100 a: 0 50 "*' 0 Em,;ronmental Soil Science Em,;ronmental Science Natural Resource Conservation No Difference Note: A strong response of 60.5% students agreed that the name Environmental Science is more appealing than the others. 32 Question 8: What department/school are you in? What DepartmentlSchool Are You In? 180 160 t/) 140 c 120 CD "0 C 100 0 Q. ~ 80  60 0 # 40 20 0 1 2 3 4 5 6 7 8 9 Legend 1  School of Accountancy & ISys 2  Marriott School of Management 3  Plant and Animal Sciences 4  Integrative Biology 5 Biology . 6 PD Bio 7  Microbiology and Molecular Biology 8  Nutrition, Dietetics, and Food Science 9  Other 33 Question 9: Gender .tI) 200 + C II) 'g 150 + 8 tI) aI:I:) 100 +,;: '0 == 50  l      0+ Male Gender Female 34 Question 10: What is your age? 2325 Question 11: Year in school Year In School 140 120 I/) c 100 CI) 'C C 80 0c. I/) 60 CI) a: 0 40 # 20 0 Freshman Sophomore Junior 2628 Senior 29+ Graduate 35 Environmental Soil Science Survey Findings 36 Question 1a: Please rank the followin'g factors in selecting your major. (1most influential to 5least influential)  Personal Interest Personal Interest 12 . .I.I.) 10 I: Q) 8 "C I: c0 . 6 lI) aQ::) 4 0 2 :11: 0 1 2 3 4 5 1 =Most Influential, 5 =Least Influential Note: Similar to the general survey, this was the highest ranked attribute out of the five. 37 Question 1b: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Popularity of major Popularity of Major 9............... 8+....".:.. .!!7+==': 0C: (1)6+''.:. 'C S5+:~ ~4+ £3+...:.. ~2+'=.:..:.:"=...::":.....:: # 1o 1 2 3 4 5 1 =Most Influential, 5 =Least Influential 38 Question 1c: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Family influences Family Influences 2 3 4 5 1 =Most Influential, 5 =Least Influential 39 Question 1d: Please rank the following factors in selecting your major. (1mo~t influential to 5least influential)  Scholarship Availability Scholarship Availability 7 6 en C 5 Q) g 4 0 ~3 Q) ~ 2 0 *1 0 1 2 3 4 5 1 = Most Influential, 5 = Least Influential Note: As with the general survey this factor was ranked as less influential when selecting a major. 40 Que~tion 1e: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Potential Income Potential Income 1 =Most Influential, 5 =Least Influential 6 5 en i 4 "0 co 3 Co en CI) a: 2 0 '*I: 1 0 1 2 3 4 5 L Note: While in the general survey respondents considered potential income as one of the higher influential factors in selecting a major, ESS students responded in ranking it as a less influential factor. 41 ,'" .........~'.. ',,'1' , ~. l~·~i~:~~~:~:~ ~,: ::,~~~t~'~ Question 2: t:lave you ever been in a major other than Environmental Soil Science at BYU? Have You Ever Been In a Major Other Than ESS ~ 6.5 ,~.........:~.............., C CD 6 t,.,."""" ~"C o C 5.5 tJ,: =#:0 ~ 5 t'fl aCD: 4.5 t.L.:o.~ 1 Yes = 1, No = 2 2 42 Question 3: What was your previous major, if applicable? Responses: Business, Civil Engineering, Horticulture Management Communications Geology Geology • Music performance Wildlife and Range Question 4: If you answered yes to the previous question, what led you to switch majors? Responses: I ~idn't want to have ajob in that major, I really like science. I hke the range of courses that are required in this major and I like the fact that few people choose this major. • It was the mentored research and the diversity of the education. TLihkeesdmthailsl soinzee boefttthere.major and I felt like I got to know the professors and the other students in the major quickly. There were also many scholarships, travel, and work opportunities available during my bachelor work. 43 'uestion 5: What do you like about the Environmental Soil Science 'rogram? lesponses: The variety of courses and the research opportunities The faculty are friendly, helpful, and accessible. The research, professors, and the freedom of choosing classes great instructors, hands on You get to know the faculty members well and there are lots of opportunities for undergraduate research. The professors care about the students. I like the small class sizes, the interest the teachers take in the students, and the friendliness the students. I enjoy learning about earth's processes and all of the professor's I have had in this program are very supportive and approachable. The range of subjects is innumerable and I enjoy learning about many different aspects of environmental issues. I like the program because I like agriculture and farming. ] get to study and learn about what I love. I like the program because I enjoy agriculture. In ESS I get to understand and learn about the heart of what I love. I like how small it is and how I feel at home with all the people in the major. Also, it allowed me to take many electives giving me a broad and enjoyable learning experience. I got to take everything from geology and landscaping classes to land use law 44 Question 6: What do you dislike about the Environmental Soil Science Program? Responses: the low availability of courses It would be nice to have a slightly larger enrollment in some of the classes. The name of the major. It isn't flattering for most and I believe that is a major draw back for people looking for an environment related major physics requirement There aren't very many students in the program so it makes it hard to have a club or very many field trips how small it is Nothing, right now! The lack of direction in terms of employment, there should be emphasis on getting students into law school, education or a better focus on job placement post undergraduate education. Ijust started so I can't say much in dislike. I would hope that some of the chern. classes that are now required that do not have much to do with ESS could be dropped from the required classes. I just started the program so I don't have too many complaints yet. I do wish I did not have to take so many chern. classes. I didn't like how some classes seemed to be agriculturally based. I was initially looking for more environmental science where I would study air, water, and energy, etc. There was none of that. I don't feel prepared for any specific job either. 45 ~~ .' ':. , "~ ~.~.:.. _..~• • _~J .~. ' _ Question 7: Have you ever thought of leaving the ESS program for another major at BYU? Have You Ever Thought of Leaving the ESS Program S 6.5 ,.......,... c: ~ 6+'::~., :oc:t 5.5 CD a: 5 +__ o * 4.5 + 1 1 =Yes, 2 =No 2 Question 8: Why did you consider leaving the program? Responses: accounting or info system, something more lucrative and in demand I am minoring in modem dance and I considered changing it to my major, but it would take way too much time and I love science. I really liked my marketing class. I thought it was really interesting. However, I was almost done when I took it and didn't want to spend my whole life in school. Not sure if that's what I want to do. There was another program in the InBio department that I thought might fit my interests better, but I decided to stay because I love the PAS department. 46 Question 9a: Rate the importance of the following in regards to your major: The academic qualifications of the Professors Academic Qualifications of the Professors 6 U) ccu: 5 "cC 4 ~3 U) aG:) 2 o 1 ~ 0 1 2 3 4 5 6 7 Very Important = 1, Very Unimportant = 7 47 Question 9b: Rate the importance of the following in regards to your major: Availability of professors for consultations Availability of Professors for Consultations 8,..,................_...., 7 Jc!! 6 c8 5 8.4 rn ~ 3 '0 2 =I*: 1o +~.L~ 1 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 48 Question 9c: Rate the importance of the following in regards to your major: National reputation of the program National Reputation of Program 5 Jc!! 4 C1) "'C C 3 0 Q. tn C1) 2 a: 0 1 # 0 1 2 3 4 5 6 7 Ve ry Important = 1, Ve ry Unimportant = 7 49 Question 9d: Rate the importance of the following in regards to your major: General Atmospherel Environment of the Plant and Animal Sciences Department General AtmospherelEnvironment of the PAS Department 5 .I.n. s:::: 4 (1) "0 S 3 c.. ~ 2 II: '0 1 '"' o    f r 1 T1 1 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 50 Question ge: Rate the importance of the following in regards to your major: Job availability following graduation Job Availability Following Graduation 6., E 5 +      : :                   1 CD ~ 4 +f,""'·~1___1 8. 3 tIJ,:;.i'A~I Ie!n 2 o 1 :tI: 1 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 51 Question 9g: Rate the importance of the following in regards to your major: Variety of Environmental Science core courses Variety of Environmental Science Core Courses 6 C1l C 5 Q) g 4 &.3 C1l ~ 2 o 1 '#; o       I I' I I I 1 2 3 4 5 6 7 Very Important = 1, Very Unimportant = 7 53 Question 9h: Rate the importance of the following in regards to your major: The inclusion of basic science courses in the Environmental Soil Science curriculum Inclusion of Basic Science Courses in the ESS Curriculum f/) 4 r::::: ~ 3 r::::: o Q. 2 f/) (1) a:: 1 '0 :f*: 0 1 , e I I 1 2 3 4 5 6 7 Very Important = 1, Very Unimportant =7 54 Question 9i: Rate the importance of the following in regards to your major: The number of faculty and students in the ESS program Number of Faculty and Students in the ESS Program 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 1 J!!4,~=:,..,.,,,._~. c~ 3  1   '      Co 0.2 l/) Q) a: 1 '0 * 0 +J=L.,. 55 IQ a Importance oftha fallowing In regards to your m_J· Co m'unlcatlon amtc»na _tu and faculty n the program Com~Imunlcltkl.n Among Students ,lind Faculty Itt Pro ram 56 a 4 5 6 7 ,on n1 =1, Very Unimportant::;; 1 o 1 ~ Importllnce ,ot the following In regards to your __ ' hi I Opportunities 57 2 3 ,4 5 6 7 ry I portent = 1, Very Unlrnportant = 7 'Question 91: Rate 'the ImportBnce of the following in regards to your major: Reid trips with students and facu1lty Field Trip with Students and Faculty 5 ~.. 4 "C&3 t~ ~ 2 o 1 o 2 3 4 5 6 7 Very Important = 1, Very Unimportant = 7 58 QuesUon 9m: Rate the Importance of the following in regards to your major: PreparaUon for postgraduate education PreparatJon for Postgraduate Education 7 J! 6 ~ 'C c: 4 8. I 3 a: 2 0 1 0 , 2 3 4 5 '6 7 Very Impo,rtant = 1, Very Unimportant = 7 59 Question lOa: On a scale of 17, how satisfied are you with the avallablli1y ot: eta' 8 offered by the Plant and Animal Sciences Department S,atisfled with Classes Offered by the PAS Department !J 5 1~~__............, I 4 I~t o ":  0 1 2 345 6 7 Very Satisfied =1, Very Dissatisfied =7 60 61 \AlH~_ B Ie Science Classes by I r' 0 p rtments on Campus 234 5 6 7 ry 58tJ18fMtd =1 Very Dlssat'sfled =7 of 1·7, how tlsfled are you with the __ c 8C'lanc~ C 8 offered by other departments on 1 6,::;~~~~ 5 "1 Que :1lon 11: What semesterslterms should more Plant and Animal Science cia be O'ffered? SemestersTerms More PAS Classes Should Be Offered CD 6 y:, i 5 t 'ca 4 &. 3 a: 2, o o F Wi fer Spring Surrrrer ~need ot : ,\Ill u tJ II r rnBY ,thl" t lJ hI. ttUtI ~ n .In :umm~r I"'nns w re used for intern hip. away h h 1.:\, ttwt ttl si l ali ~nd I s during lhis time. 62 63 7 7 6 6 5 5 .. .. •• Be I nl 1, ,Poor 7 ......nt '. Poor 7 ~ ch ng Faculty _ 101, owl M.,ntored A arch Opportunities TeachI QM 64 4 5 6 7 •• 3 etc...nt.: 1, Poor = 7 ComlllJnIClltkHl1 RANlRen Students VVlthln the Program 11C81t1CK1 Ibe1WIMIn a1hK1.,,1t8 within th Environmental Soil 4 5 6 7 • I:ICc• •nt _ 1, Poor 7 COIrmlunlC8tJon Betweef" Student and Faculty E Program ~mmlunliC8t:'on DetWtMm ••,""""'I",t and faculty within the SCienc:e DlroG.lIlm UUlt8tICHI 12 : RaI~· I d cully of the Environmental UlfrICUr_1V of tESS Major o o "l~__,"""!,,, 4 5 6 7 Dlmicun~. ", Not DIffIcult == 7 66 chllnalng ~ nem. of the program Selene. En ronmentBl SCience N8I1r'1 of the ESS Progr.m Wallft .ft1a,... lI,nM~ Students 5 7 6 7 that changing the low;arm attl'lictin more tud nts. This n raj ~urv y. 67 68 and . ultuJ1e or  iii m y u ant M\,iro oW i U you to 8 student ? • • • 69  001 Graduate 7 11 Conclusions Based on our findings, we came to the following conclusions: • Out of 349 respondents, 211 (60.5%) agreed that the name Environmental Science is more appealing than the current name of Environmental Soil Science. • Almost 60% of the students responded by saying they have not thoroughly searched all the majors offered at BYU. In fact, 50% of the respondents only "looked" into I to 2 different majors before selecting the one they are in now. • On a 7point scale (l =Completely Agree, 7 =Completely Disagree) 75% ofthe re pondents completely agreed that future career plans influence the selection of their major. This goes handinhand with the 60% response that job availability after graduation is very important on a separate 7point scale (1= Very Important, 7 = Not at all). • In the general survey, there was great importance placed on the variety of classes offered with 55% of the respondents assigning a 1 or 2 on the 7point scale (I =Very Important, 7 = ot at all). Also in the ESS survey, 50% of the students expressed interest in more Plant and Animal Sciences courses offered during spring and summer terms. Recommendations Based on our findings and conclusions, we recommend the following actions to be taken: • Change the name of the Environmental Soil Science program to Environmental Science. We feel that by making this simple change, the program will be able to increase its enrollment by portraying a broader scope of study. • Con idering that majority of BYU students do not thoroughly search all of the majors offered, the Plant and Animal Sciences department should increase awareness of fre hman and sophomores at BYU. Perhaps this could be achieved through the New Student Orientation. We also recommend analyzing the results of the High School urvey. • As the Plant and Animal Sciences department increases awareness they should also place emphasis on informing students about the future career possibilities. Our research shows that the students placed the most importance on job availability following graduation. We recommend that the PAS department look into the methods used by other departments on campus for internship and job recruiting. 72 • We recommend that the department look into the possibilities of offering more spring and summer courses. Half of the responding ESS students said they thought of leaving their major. By having a wider array of time offerings and variety of classes offered, this may help to increase the retention and overall program numbers in the end. 73 Appendices 7 Appendix A Statistically Signi~icant Cross Tabulations Part 1: General Survey Part 2: Environmental Soil Science Survey 75 Part 1: General Survey Questions Q 1) Please rank the following factors in selecting your major. (lmost influential to 5least influential) Qla  Personal Interest Qlb  Popularity of major Qlc  Family influences Qld  Scholarship Availability Q Ie  Potential Income Q2) Rate the importance you place on each of the following characteristics of a major: Q2a  General atmosphere/environment Q2b  The academic qualifications of the professors Q2c  Availability of professors for consultations Q2d  Starting base salary after graduation Q2e  National reputation of the program Q2f  Variety of class offerings Q2g  Times classes offered Q2h  Job availability following graduation Q2i  Preparation for graduate school Q3) Thinking of different majors, rate your agreement with the following statements based on your experience: Q3a  A bad teacher will influence your selection of major Q3b  A good teacher will influence your selection of major Q3c  Would you say you've thoroughly searched all the majors offered at BYU Q3d  The name of a program/major can influence its popularity Q3e  Your future career plans influence your selection of a major Q4) How many times have you "looked" into different majors (taken different classes to see if you want to go with a certain major)? Q5) How many times have you "officially" changed your major on the books? (changed it with the school records) Q6) How could your current major improve, if needed? Q7) Which program/major name has more appeal: Environmental Soil Science Environmental Science Natural Resource Conservation No difference Q8) What department/school are you in? Q9) Gender: Q10) What is your age? Q11) Year in school 76 Year In School (q11) * q1b ChiSquare Tests a 6 cells (24.0%) have expected count less than 5. The minimum expected count is 1.47. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 49.049(a) 16 .000 Likelihood Ratio 50.235 16 .000 LinearbyLinear Association 1.717 1 .190 N of Valid Cases 347 . . Year In School (q11) * q1d ChiSquare Tests a 8 cells (32.0%) have expected count less than 5. The minimum expected count is 1.96. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 41.240(a) 16 .001 Likelihood Ratio 43.115 16 .000 LinearbyLinear 12.459 1 .000 Association N of Valid Cases 347 .. Year In School (q11) * q2b ChiSquare Tests a 17 cells (48.6%) have expected count less than 5. The minimum expected count is .29. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 36.731 (a) 24 .047 Likelihood Ratio 37.406 24 .040 LinearbyLinear .005 1 .941 Association N of Valid Cases 347 . . Year In School (q11) * q2d ChiSquare Tests a 17 cells (48.6%) have expected count less than 5. The minimum expected count is .29. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 62.456(a) 24 .000 Likelihood Ratio 54.787 24 .000 LinearbyLinear 10.072 1 .002 Association N of Valid Cases 346 . . 77 Year In School (q11) * q2e ChiSquare Tests a 18 cells (51.4%) have expected count less than 5. The minimum expected count is .29. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 66.600(a) 24 .000 Likelihood Ratio 63.039 24 .000 LinearbyLinear Association 14.426 1 .000 N of Valid Cases 347 . . Year In School (q11) * q2h ChiSquare Tests Asymp. Sig. Value df (2sided\ Pearson ChiSquare 66.602(a) 24 .000 Likelihood Ratio 67.159 24 .000 LinearbyLinear 7.115 1 .008 Association N of Valid Cases 346 a 23 cells (65.7%) have expected count less than 5. The minimum expected count is .39. Year In School (q11) * q5 ChiSquare Tests Asymp. Sig. Value df 12sided\ Pearson ChiSquare 14.085(a) 8 .080 Likelihood Ratio 16.212 8 .039 LinearbyLinear 4.481 1 .034 Association N of Valid Cases 347 a 3 cells (20.0%) have expected count less than 5. The minimum expected count is 2.16. Year In School (q11) * Department (q8) ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 343.587(a) 32 .000 Likelihood Ratio 344.857 32 .000 LinearbyLinear 147.488 1 .000 Association N of Valid Cases 347 a 32 cells (71.1 %) have expected count less than 5. The minimum expected count is .59. Age (q10) * q1a ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 24.735(a) 16 .075 Likelihood Ratio 31.391 16 .012 LinearbyLinear Association .253 1 .615 N of Valid Cases 347 a 14 cells (56.0%) have expected count less than 5. The minimum expected count is .14. Age (q10) * q1 b ChiSquare Tests a 10 cells (40.0%) have expected count less than 5. The minimum expected count is .26. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 54.165(a) 16 .000 Likelihood Ratio 57.128 16 .000 LinearbyLinear .598 1 .439 Association N of Valid Cases 347 . . Age (q10) * q1d ChiSquare Tests a 9 cells (36.0%) have expected count less than 5. The minimum expected count is .35. Asymp. Sig. Value df (2sided) Pearson ChiSquare 38.871 (a) 16 .001 Likelihood Ratio 37.222 16 .002 LinearbyLinear 10.452 1 .001 Association N of Valid Cases 347 .. Age (q10) * q1e ChiSquare Tests a 12 cells (48.0%) have expected count less than 5. The minimum expected count is .17. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 33.031 (a) 16 .007 Likelihood Ratio 40.388 16 .001 LinearbyLinear 6.562 1 .010 Association N of Valid Cases 347 . . 79 Age (q10) * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 40.163(a) 24 .021 Likelihood Ratio 35.784 24 .058 LinearbyLinear 8.629 1 Association .003 N of Valid Cases 346 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .07. Age (q10) * q2b ChiSquare Tests Asymp. Sig. Value df (2sided)" Pearson ChiSquare 35.650(a) 24 .059 Likelihood Ratio 27.613 24 .277 LinearbyLinear 1.800 1 .180 Association N of Valid Cases 347 a 20 cells (57.1%) have expected count less than 5. The minimum expected count is .05. Age (q10) * q2d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.541 (a) 24 .000 Likelihood Ratio 61.403 24 .000 LinearbyLinear 12.993 1 .000 Association N of Valid Cases 346 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .05. Age (q10) * q2e ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 45.752(a) 24 .005 Likelihood Ratio 45.225 24 .005 LinearbyLinear 14.199 1 .000 Association N of Valid Cases 347 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .05 Age (q10) * q2h ChiSquare Tests a 24 cells (68.6%) have expected count less than 5. The minimum expected count is .07. Asymp. Sig. Value df (2sided\ Pearson ChiSquare 65.644(a) 24 .000 Likelihood Ratio 54.088 24 .000 LinearbyLinear Association 5.866 1 .015 N of Valid Cases 346 . . Age (q10) * q5 ChiSquare Tests a 5 cells (33.3%) have expected count less than 5. The minimum expected count is .38. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 20.838(a) 8 .008 Likelihood Ratio 21.433 8 .006 LinearbyLinear 6.318 1 .012 Association N of Valid Cases 347 . . Age (q1 0) * Department (q8) ChiSquare Tests a 32 cells (71.1 %) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value df (2sided) Pearson ChiSquare 216.713(a) 32 .000 Likelihood Ratio 231.101 32 .000 LinearbyLinear 94.227 1 .000 Association N of Valid Cases 347 . . Age (q10) * Gender (q9) ChiSquare Tests a 2 cells (20.0%) have expected count less than 5. The minimum expected count is 2.37. Asymp. Sig. Value df l2sided) Pearson ChiSquare 109.770(a) 4 .000 Likelihood Ratio 118.885 4 .000 LinearbyLinear 97.015 1 .000 Association N of Valid Cases 347 . . 81 Age (q10) * Year In School (q11) ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 329.255(a) 16 .000 Likelihood Ratio 329.077 16 .000 LinearbyLinear Association 192.990 1 .000 N of Valid Cases 347 a 8 cells (32.0%) have expected count less than 5. The minimum expected count is .59. Gender (q9) * q1a ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 8.020(a) 4 .091 Likelihood Ratio 8.580 4 .073 LinearbyLinear 2.529 1 .112 Association N of Valid Cases 347 a 2 cells (20.0%) have expected count less than 5. The minimum expected count is 3.16. Gender (q9) * q1 b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.823(a) 4 .029 Likelihood Ratio 10.867 4 .028 LinearbyLinear .629 1 .428 Association N of Valid Cases 347 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.92. Gender (q9) * q1 C ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.123(a) 4 .038 Likelihood Ratio 10.575 4 .032 LinearbyLinear .019 1 .889 Association N of Valid Cases 347 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.53. Gender (q9) * q1e ChiSquare Tests a 1 cells (10.0%) have expected count less than 5. The minimum expected count is 3.95 Asymp. Sig. Value df 12sidedl Pearson ChiSquare 13.982(a) 4 .007 Likelihood Ratio 14.106 4 .007 LinearbyLinear Association 5.912 1 .015 N of Valid Cases 347 · . Gender (q9) * q2d ChiSquare Tests a 4 cells (28.6%) have expected count less than 5. The minimum expected count is 1.18. Asymp. Sig. Value df 12sidedl Pearson ChiSquare 33.123(a) 6 .000 Likelihood Ratio 33.210 6 .000 LinearbyLinear 17.995 1 .000 Association N of Valid Cases 346 · . Gender (q9) * q2e ChiSquare Tests a 4 cells (28.6%) have expected count less than 5. The minimum expected count is 1.18. Asymp. Sig. Value df (2sided)' Pearson ChiSquare 30.986(a) 6 .000 Likelihood Ratio 30.935 6 .000 LinearbyLinear 18.288 1 .000 Association N of Valid Cases 347 · . Gender (q9) * q2h ChiSquare Tests a 8 cells (57.1%) have expected count less than 5. The minimum expected count is 1.57. Asymp. Sig. Value df (2sided) Pearson ChiSquare 26.108(a) 6 .000 Likelihood Ratio 27.402 6 .000 LinearbyLinear 6.411 1 .011 Association N of Valid Cases 346 · . 83 Gender (q9) * q2i ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 20.390(a) 6 .002 Likelihood Ratio 20.352 6 .002 LinearbyLinear Association 15.551 1 .000 N of Valid Cases 346 a 3 cells (21.4%) have expected count less than 5. The minimum expected count is 1.58. Gender (q9) * q3e ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 12.297(a) 6 .056 Likelihood Ratio 13.545 6 .035 LinearbyLinear 7.661 1 .006 Association N of Valid Cases 346 a 10 cells (71.4%) have expected count less than 5. The minimum expected count is .39. Gender (q9) * q7 ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 8.037(a) 3 .045 Likelihood Ratio 8.262 3 .041 LinearbyLinear 7.561 1 .006 Association N of Valid Cases 346 a 1 cells (12.5%) have expected count less than 5. The minimum expected count is 3.54. Gender (q9) * Department (q8) ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 62.592(a) 8 .000 Likelihood Ratio 68.804 8 .000 LinearbyLinear 36.439 1 .000 Association N of Valid Cases 347 a 5 cells (27.8%) have expected count less than 5. The minimum expected count is 2.37. 84 Gender (q9) * Year In School (q11) ChiSquare Tests a 0 cells (.0%) have expected count less than 5. The minimum expected count is 13.42. Asymp. Sig. Value df (2sided) Pearson ChiSquare 54.952(a) 4 .000 Likelihood Ratio 54.891 4 .000 LinearbyLinear Association 40.880 1 .000 N of Valid Cases 347 . . Department (q8) * q1 b ChiSquare Tests a 33 cells (73.3 ~o) have expected count less than 5. The minimum expected count is .26. Asymp. Sig. Value df (2sided) Pearson ChiSquare 70.239(a) 32 .000 Likelihood Ratio 79.039 32 .000 LinearbyLinear 1.016 1 .314 Association N of Valid Cases 347 0 · . Department (q8) * q1d ChiSquare Tests a 30 cells (66.7 Yo) have expected count less than 5. The minimum expected count is .35. Asymp. Sig. Value df (2sided) Pearson ChiSquare 78.413(a) 32 .000 Likelihood Ratio 73.178 32 .000 LinearbyLinear 14.316 1 .000 Association N of Valid Cases 347 0 · . Department (q8) * q2c ChiSquare Tests a 51 cells (81.0Yo) have expected count less than 5. The minimum expected count is .12. ,.... Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.824(a) 48 .063 LikelihOod Ratio 66.130 48 .042 LinearbyLinear .000 1 .995 Association N of Valid Cases 343 0 · . 85 Department (q8) * q2d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 104.680(a) 48 .000 Likelihood Ratio 82.999 48 .001 LinearbyLinear 15.957 1 Association .000 N of Valid Cases 346 a 50 cells (79.4%) have expected count less than 5. The minimum expected count is .05. Department (q8) * q2e ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 111.456(a) 48 .000 Likelihood Ratio 79.047 48 .003 LinearbyLinear 13.243 1 .000 Association N of Valid Cases 347 a 50 cells (79.4%) have expected count less than 5. The minimum expected count is .05. Department (q8) * q2h ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 77.906(a) 48 .004 Likelihood Ratio 66.529 48 .039 LinearbyLinear 8.962 1 .003 Association N of Valid Cases 346 a 51 cells (81.0%) have expected count less than 5. The minimum expected count is .07. Department (q8) * q2i ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 75.649(a) 48 .007 Likelihood Ratio 83.750 48 .001 LinearbyLinear .108 1 .742 Association N of Valid Cases 346 a 49 cells (77.8%) have expected count less than 5. The minimum expected count is .07. 86 Department (q8) * q3e ChiSquare Tests a 53 cells (84.1 %) have expected count less than 5. The minimum expected count is .02. Asymp. Sig. Value df (2sided) Pearson ChiSquare 100.945(a) 48 .000 Likelihood Ratio 49.014 48 .432 LinearbyLinear .051 1 .821 Association N of Valid Cases 346 . . Department (q8) * Year In School (q11) ChiSquare Tests a 32 cells (71.1 Yo) have expected count less than 5. The minimum expected count is .59 Asymp. Sig. Value df (2sided) Pearson ChiSquare 343.587(a) 32 .000 Likelihood Ratio 344.857 32 .000 LinearbyLinear 147.488 1 .000 Association N of Valid Cases 347 0 .. q5 * q2b ChiSquare Tests a 9 cells (42.9 Yo) have expected count less than 5. The minimum expected count is .19. Asymp. Sig. Value df (2sidedf "Pearson ChiSquare 28.134(a) 12 .005 Likelihood Ratio 19.516 12 .077 LinearbyLinear .336 1 .562 Association N of Valid Cases 350 0 .. q5 * q2i ChiSquare Tests a 6 cells (28.6 Yo) have expected count less than 5. The minimum expected count is .25. ,.... Asymp. Sig. Value df (2sidedl Pearson ChiSquare 25.120(a) 12 .014 Likelihood Ratio 19.016 12 .088 LinearbyLinear 3.343 1 .068 Association N of Valid Cases 349 0 .. 87 q5 *q4 ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 101.363(a) 8 .000 Likelihood Ratio 82.692 8 .000 LinearbyLinear 60.495 Association 1 .000 N of Valid Cases 350 a 5 cells (33.3%) have expected count less than 5. The minimum expected count is .50. q4 * q2a ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 35.272(a) 24 .064 Likelihood Ratio 31.328 24 .145 LinearbyLinear .181 1 .670 Association N of Valid Cases 349 a 22 cells (62.9%) have expected count less than 5. The minimum expected count is .09. q4 * q3a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 38.014(a) 24 .035 Likelihood Ratio 45.766 24 .005 LinearbyLinear 1.981 1 .159 Association N of Valid Cases 348 a 20 cells (57.1 %) have expected count less than 5. The minimum expected count is .23. q4 * q3c ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 82.530(a) 24 .000 Likelihood Ratio 80.182 24 .000 LinearbyLinear 53.777 1 .000 Association N of Valid Cases 349 a 17 cells (48.6%) have expected count less than 5. The minimum expected count is .23. 88 q3e * q1a ChiSquare Tests a 26 cells (74.3%) have expected count less than 5. The minimum expected count is .02. Asymp. Sig. Value df 12sided)· Pearson ChiSquare 36.171 (a) 24 .053 Likelihood Ratio 21.484 24 .610 LinearbyLinear 4.877 1 .027 Association N of Valid Cases 354 · . q3e * q1b ChiSquare Tests a 26 cells (74.3%) have expected count less than 5. The minimum expected count is .05. Asymp. Sig. Value df (2sided) Pearson ChiSquare 36.420(a) 24 .050 Likelihood Ratio 26,252 24 .341 LinearbyLinear 1.716 1 Association .190 N of Valid Cases 354 · . q3e * q1c ChiSquare Tests a 27 cells (77.1 "io) have expected count less than 5. The minimum expected count is .04.  Asymp. Sig. Value df 12sidedf Pearson ChiSquare 35.314(a) 24 .064 Likelihood Ratio 32.705 24 .110 LinearbyLinear 1.257 1 .262 Association N of Valid Cases 354 · . q3e * q1d ChiSquare Tests a 26 cells (74.37"0) have expected count less than 5. The minimum expected count is .06. .... Asymp. Sig. Value df 12sidedf ~earson ChiSquare 38.175(a) 24 .033 Likelihood Ratio 23.478 24 .492 LinearbyLinear 3.572 1 .059 Association N of Valid Cases 354 0 · . 89 q3e * q1e ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 47.975(a) 24 .003 Likelihood Ratio 36.691 24 .047 LinearbyLinear Association 5.061 1 .024 N of Valid Cases 354 a 26 cells (74.3%) have expected count less than 5. The minimum expected count is .03. q3e * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 111.715(a) 36 .000 Likelihood Ratio 38.486 36 .358 LinearbyLinear 6.789 1 .009 Association N of Valid Cases 353 a 40 cells (81.6%) have expected count less than 5. The minimum expected count is .01. q3e * q2b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 48.264(a) 36 .083 Likelihood Ratio 29.667 36 .763 LinearbyLinear 1.246 1 .264 Association N of Valid Cases 354 a 40 cells (81.6%) have expected count less than 5. The minimum expected count is .01. q5 * q3c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 24.430(a) 12 .018 Likelihood Ratio 22.643 12 .031 LinearbyLinear 15.508 1 .000 Association N of Valid Cases 349 a 9 cells (42.9%) have expected count less than 5. The minimum expected count is .63 90 q3e * q2d ChiSquare Tests a 39 cells (79.6 Yo) have expected count less than 5. The minimum expected count is .01. Asymp. Sig. Value df /2sidedf Pearson ChiSquare 85.993(a) 36 .000 Likelihood Ratio 42.533 36 .210 LinearbyLinear 5.454 1 .020 Association N of Valid Cases 353 0 · . q3e * q2e ChiSquare Tests a 39 cells (79.6 Yo) have expected count less than 5. The minimum expected count is .01. Asymp. Sig. Value df (2sided) Pearson ChiSquare 69.111 (a) 36 .001 Likelihood Ratio 39.770 36 .306 LinearbyLinear 14.602 1 .000 ASSociation N of Valid Cases 354 0 · . q3e * q2f ChiSquare Tests a 40 cells (81.6 Yo) have expected count less than 5. The minimum expected count is .01. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 54.118(a) 36 .027 LikelihOod Ratio 31.332 36 .690 LinearbyLinear 4.869 1 .027 Association N of Valid Cases 352 0 · . q3e * q2h ChiSquare Tests a 43 cells (87.8 Yo) have expected count less than 5. The minimum expected cou.nt is .01. Asymp. Sig. Value df l2sidedf ~earson ChiSquare 223.388(a) 36 .000 Likelihood Ratio 42.284 36 .218 LinearbyLinear 15.366 1 .000 Association N of Valid Cases 353 0 · . 91 q3e * q2i ChiSquare Tests a 38 cells (77.6%) have expected count less than 5. The minimum expected count is .01 . Asymp. Sig. Value df (2sidedl Pearson ChiSquare 69.762(a) 36 .001 Likelihood Ratio 45.689 36 .129 LinearbyLinear 6.208 Association 1 .013 N of Valid Cases 353 . . q3e * q3a ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 58.637(a) 36 .010 Likelihood Ratio 51.584 36 .045 LinearbyLinear .114 1 .735 Association N of Valid Cases 353 a 38 cells (77.6%) have expected count less than 5. The minimum expected count is .03. q3e * q3b ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 142.578(a) 36 .000 Likelihood Ratio 44.057 36 .168 LinearbyLinear 21.852 1 .000 Association N of Valid Cases 354 a 41 cells (83.7%) have expected count less than 5. The minimum expected count is .00. q3e * q3c ChiSquare Tests a 36 cells (73.5%) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df (2sided) Pearson ChiSquare 54.640(a) 36 .024 Likelihood Ratio 36.542 36 .443 LinearbyLinear 4.251 1 .039 Association N of Valid Cases 354 .. 92 q3d * q2a ChiSquare Tests a 29 cells (59.2 Yo) have expected count less than 5. The minimum expected count is .12. Asymp. Sig. Value df (2sided)' Pearson ChiSquare 50.849(a) 36 .051 Likelihood Ratio 56.304 36 .017 LinearbyLinear .015 1 .902 Association N of Valid Cases 353 0 · . q3d * q2e ChiSquare Tests a 32 cells (65.3 Yo) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value df (2sided) Pearson ChiSquare 50.619(a) 36 .054 Likelihood Ratio 46.587 36 .111 LinearbyLinear 2.282 1 .131 AsSociation N of Valid Cases 354 0 · . q3d * q2f ChiSquare Tests a 30 cells (61.2 Yo) have expected count less than 5. The minimum expected count is .13.  Asymp. Sig. Value dl (2sidedf pearson ChiSquare 64.753(a) 36 .002 Likelihood ~atio 61.473 36 .005 LinearbyUnear 5.689 1 .017 Association N of Valid Cases 352 a · . q3d * q2h ChiSquare Tests a 33 cells (67.3 Yo) have expected count less than 5. The minimum expected count is .14.  Asymp. Sig. Value df (2sidedf pearson ChiSquare 51.636(a) 36 .044 Likelihood ~atio 47.620 36 .093 LinearbyUnear 4.998 1 .025 Association N of Valid Cases 353 a · . 93 q3d * q3a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.614(a) 36 .003 Likelihood Ratio 64.452 36 .002 LinearbyLinear Association 11.407 1 .001 N of Valid Cases 353 a 28 cells (57.1%) have expected count less than 5. The minimum expected count is .31. q3d * q3c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 50.742(a) 36 .053 Likelihood Ratio 55.044 36 .022 LinearbyUnear 3.305 1 .069 Association N of Valid Cases 354 a 27 cells (55.1%) have expected count less than 5. The minimum expected count is .34. q3c * q1c ChiSquare Tests Asymp. Sig. Value df (2sided)" Pearson ChiSquare 34.417(a) 24 .078 Likelihood Ratio 37.582 24 .038 LinearbyUnear .530 1 .466 Association N of Valid Cases 354 a 16 cells (45.7%) have expected count less than 5. The minimum expected count is .42. q3c * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 57.474(a) 36 .013 Likelihood Ratio 51.122 36 .049 UnearbyUnear 2.296 1 .130 Association N of Valid Cases 353 a 30 cells (61.2%) have expected count less than 5. The minimum expected count is .11. 94 q3C * q2b ChiSquare Tests a 31 cells (63.3 Yo) have expected count less than 5. The minimum expected count is .08. Asymp. Sig. Value df (2sided)' Pearson ChiSquare 50.583(a) 36 .054 Likelihood Ratio 47.794 36 .090 LinearbyLinear 2.066 1 .151 Association N of Valid Cases 354 0 · . q3C * q2c ChiSquare Tests p ted count less than 5 The minimum expected count is .23. ChiSquare Tests a 24 cells (49 q3C * q2d Asymp. Sig. Value df (2·sided)' ~earson ChiSquare 51.205(a) 36 .048 Likelihood Ratio 54.996 36 .022 LinearbtLinear 6.983 1 .008 AsSociatIOn N of Valid Cases 350 .0% have ex ec · . ChiSquare Tests a 28 cells (57 ) a e e p ted count less than 5. The minimum expected count is .08. q3C * q2e Asymp. Sig. Value df (2sided) pearson ChiSquare 58.561 (a) 36 .010 LikelihOod ~atio 61.804 36 .005 Linearbylinear .280 1 .597 AsSociation N of Valid Cases 353 .1% h v x ec · . a 30 cells (61.2 Yc) a e expecte 0 t ess than 5 The m n mum expected count is .08.  Asymp. Sig. Value df 12sided) ~earson ChiSquare 72.101(a) 36 .000 LikelihOod ~atio 73.225 36 .000 Linearbylinear 4.276 1 .039 ociation ~f Valid Cases 354 °0 h v de un I i i 95 q3c * q2f ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.598(a) 36 .003 Likelihood Ratio 59.962 36 .007 LinearbyLinear Association 9.797 1 .002 N of Valid Cases 352 a 26 cells (53.1 %) have expected count less than 5. The minimum expected count is .11. q3c * q2h ChiSquare Tests a 35 cells (71.4%) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df (2sided) Pearson ChiSquare 54.896(a) 36 .023 Likelihood Ratio 41.552 36 .242 LinearbyLinear .061 1 .805 Association N of Valid Cases 353 .. q3c * q2i ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 55.468(a) 36 .020 Likelihood Ratio 56.738 36 .015 LinearbyLinear 3.502 1 .061 Association N of Valid Cases 353 a 26 cells (53.1 %) have expected count less than 5. The minimum expected count is .14. q3c * q3a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 50.719(a) 36 .053 Likelihood Ratio 56.873 36 .015 LinearbyLinear 3.245 1 .072 Association N of Valid Cases 353 a 27 cells (55.1 %) have expected count less than 5. The minimum expected count is .28. 96 q3b * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 67.363(a) 36 .001 LikelihOod Ratio 55.079 36 .022 LinearbyLinear 19.294 1 .000 Association N of Valid Cases 353 a 38 cells (77.60Yo) have expected count less than 5. The mi.ni.mum expected count is .01 . q3b It q2b ChiSquare Tests t a pected count is .01. ChiSquare Tests Asymp. Sig. Value df (2sided) P"""pearson ChiSquare 53.332(a) 36 .031 Likelihood ~atio 47.894 36 .089 LJ'nearby. llnear 9.993 1 .002 Association N of valid Cases 354 a 36 cells (73.50Yo) have expected count less h n 5. The minimum ex q3b '* q2c p xpected count is .02. ChiSquare Tests ..... Asymp. Sig. Value df (2sided) ....p. earson ChiSquare 65.204(a) 36 .002 LikelihOod ~atio 42.589 36 .209 L1' 0earby.LlOear 10.888 1 .001 Association N of Valid Cases 350 a 35 cells (71.4% have ex ected count less than 5. The minimum e q3b '* q2d a 36 celiS (73 p xpected count is .01. P""" Asymp. Sig. Value df (2sided) Pearson ChiSquare 52.727(a) 36 .036 . lihood Ratio 45.963 36 .124 uke r byLinear 2.067 1 .150 Linea  . sociatJon ~of valid Cases 353 .5% have ex ected count less than 5. The minimum e 97 q3b * q2e ChiSquare Tests a 37 cells (75.5%) have expected count less than 5. The minimum expected count is .01. Asymp. Si9. Value df 12sided) Pearson ChiSquare 51.843(a) 36 .042 Likelihood Ratio 40.004 36 .297 LinearbyLinear Association 4.731 1 .030 N of Valid Cases 354 .. q3b * q2f ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 83.244(a) 36 .000 Likelihood Ratio 44.282 36 .162 LinearbyLinear 6.314 1 .012 Association N of Valid Cases 352 a 35 cells (71.4%) have expected count less than 5. The minimum expected count is .01. q3b * q2h ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 156.871 (a) 36 .000 Likelihood Ralio 46.056 36 .122 LinearbyLinear 8.660 1 .003 Association N of Valid Cases 353 a 40 cells (81.6%) have expected count less than 5. The minimum expected count is .01. q3b * q2i ChiSquare Tests a 35 cells (71.4%) have expected count less than 5. The minimum expected count is .01 . Asymp. Si9. Value df 12sided) Pearson ChiSquare 76.832(a) 36 .000 Likelihood Ratio 48.654 36 .078 LinearbyLinear 1.775 1 .183 Association N of Valid Cases 353 . . 98 q3b * q3a ChiSquare Tests a 35 cells (71.4 Yo) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df (2sided) Pearson ChiSquare 250.114(a) 36 .000 Likelihood Ratio 203.931 36 .000 LinearbyLinear 79.166 1 .000 Association N of Valid Cases 353 0 · . q3a * q2a ChiSquare Tests pected count less than 5. The minimum expected count is .11, ChiSquare Tests a 32 cells (65 q3a * q2b '""' Asymp. Sig. Value df (2sided\ pearson ChiSquare 67.820(a) 36 .001 Likelihood ~atio 55.557 36 .020 L'Inearby. Llnear 8.709 1 .003 AsSociation N of Valid Cases 352 .3% have ex · . a 30 cells (61. ) ha e expected count less than 5. The minimum expected count is .08.  Asymp. Sig. Value df (2sidedl pearson ChiSquare 53.746(a) 36 .029 Likelihood ~atio 58.671 36 .010 LI'nearby,L1near .707 1 .400 ASsociation N of valid Cases 353 2% v · . q38 * q2c ChiSquare Tests a 29 cells ( p xpected count is .21. r Asymp. Sig. Value df (2sided\ 'Pearson ChiSquare 61.274(a) 36 .005 Likelihood ~atio 57.163 36 .014 Linearb~L1near 3.666 1 ,056 ASsociation N of valid Cases 349 59.2% have ex ected count less than 5. The minimum e 99 q3a * q2d ChiSquare Tests Asymp. Sig. Value df 12sidedl Pearson ChiSquare 59.699(a) 36 .008 Likelihood Ratio 46.529 36 .112 LinearbyLinear Association .432 1 .511 N of Valid Cases 352 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .09. q3a * q2f ChiSquare Tests Asymp. Sig. Value df 12sided)' Pearson ChiSquare 71.920(a) 36 .000 Likelihood Ratio 64.613 36 .002 LinearbyLinear 4.834 1 .028 Association N of Valid Cases 352 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .11. q3a * q2g ChiSquare Tests Asymp. Sig. Value df l2sided) Pearson ChiSquare 68.468(a) 36 .001 Likelihood Ratio 71.773 36 .000 LinearbyLinear 2.032 1 .154 Association N of Valid Cases 351 a 25 cells (51.0%) have expected count less than 5. The minimum expected count is .66. q3a * q2h ChiSquare Tests a 34 cells (69.4%) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df 12sided) Pearson ChiSquare 53.709(a) 36 .029 Likelihood Ratio 39.771 36 .306 LinearbyLinear 3.403 1 .065 Association N of Valid Cases 352 .. 100 101 24 24 1 36 36 1 36 36 1 df 354 Value df Value 353 Value df 354 Value df 56.181(a) 36 45.796 36 3.424 1 352 38.230(a) 43.267 .024 64.015(a) 45.862 12.916 ChiSquare Tests ChiSquare Tests ChiSquare Tests ChiSquare Tests 108.607(a) 77.861 28.442 re re have expected count less than 5. The minimum expected count is .14. re have expected count less than 5. The minimum expected count is .31. have expected count less than 5. The minimum expected count is .06. have expected count less than 5. The minimum expected count is .04. are q3a ." q2i Pearson ChiSqu Ukelihood Ratio unearbyLinear Association N of Valid Cases a 29 cells (59.2%) q2i ." q1d ~ Pearson ChiSqua 'kelihood Ratio LJ. earbYL'Inear un . ASSociation N of Valid Cases a 16 celis (45.7%) q2i ." q2a ,.... r:p: earson ChiSqua ok Iihood Ratio uUneearbtL'Inear AsSociation N of Valid Cases a 31 celis (63.3%) q2i ." q2b ~ ~earson Chi~qua . Iihood RatiO Like r byLinear unea  ° ociatlon ~f valid Cases 1 celiS (63.3%) a3 q2i * q2c ChiSquare Tests a 28 cells (57.1 %) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sided) Pearson ChiSquare 76.062(a) 36 .000 Likelihood Ratio 61.164 36 .006 LinearbyLinear Association 21.294 1 .000 N of Valid Cases 350 .. q2i * q2d ChiSquare Tests a 31 cells (63.3%) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df 12sided\ Pearson ChiSquare 99.968(a) 36 .000 Likelihood Ratio 63.154 36 .003 LinearbyLinear 25.199 1 .000 Association N of Valid Cases 353 .. q2i * q2e ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 134.293(a) 36 .000 Likelihood Ratio 92.301 36 .000 LinearbyLinear 48.796 1 .000 Association N of Valid Cases 354 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .04. q2i * q2f ChiSquare Tests Asymp. Sig. Value df 12sided\ Pearson ChiSquare 77.605(a) 36 .000 Likelihood Ratio 62.209 , 36 .004 LinearbyLinear 18.154 1 .000 Association N of Valid Cases 352 a 31 cells (63.3%) have expected count less than 5. The minimum expected count is .06. 102 q2i * q2g ChiSquare Tests Pearson ChiSquare Likelihood ~atio LinearbyLlnear AsSociation N of Valid Cases Value 71.162(a) 66.333 26.381 352 Asymp. Sig. df 2sided) 36 .000 36 .002 .000 a 21 cells (42.9%) have expected count less than 5. The minimum expected count is .34. q2i * q2h ChiSquare Tests pearso n ChiS.quare L'kelihOod ~atlo u~near_by.LlOear ociatlon ~f Valid Cases Value 161.668(a) 86.130 38.987 353 Asymp. Sig. df 2sided)' 36 .000 36 .000 1 .000 ChiSquare Tests 5 e lls (71.4%) have expected count less than 5. The minimum expected count is .06. a 3 c q2h * q1a On ChiSquare Pears . elihood Ratio Uk r byLinear U· ean iatiOn ~alidcases Value 24.392(a) 23.793 .308 354 Asymp. Sig. df 2sided) 24 .439 24 .474 1 .579 ChiSquare Tests I/S (77.1%) have expected count less than 5. The minimum expected count is .09. a 27 ce q2h * q1b Asymp. Sig. Value df 2sidedf L_n~C~hh1ir:;SScq;u~a~re;r44~1~7.~39~(;;a))1::':~2:44t"~ .014 pea,?,"~ Ratio 42.390 24 .012 LU'kneela1hrby_Linear .232 1 .630 I iaPon ~aJid cases 354 115 (65.7%) have expected count less than 5. The minimum expected count is .19. a 2308 103 q2h * q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 22.996(a) 24 .520 Likelihood Ratio 24.898 24 .411 LinearbyLinear Association .843 1 .358 N of Valid Cases 354 a 23 cells (65.7%) have expected count less than 5. The minimum expected count is .17. q2h * q1d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 57.253(a) 24 .000 Likelihood Ratio 54.797 24 .000 LinearbyLinear 7.883 1 .005 Association N of Valid Cases 354 a 23 cells (65.7%) have expected count less than 5. The minimum expected count is .25. q2h * q1e ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 43.007(a) 24 .010 Likelihood Ratio 40.729 24 .018 LinearbyLinear 11.082 1 .001 Association N of Valid Cases 354 a 24 cells (68.6%) have expected count less than 5. The minimum expected count is .11. q2h * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 139.236(a) 36 .000 Likelihood Ratio 52.017 36 .041 LinearbyLinear 15.644 1 .000 Association N of Valid Cases 353 a 38 cells (77.6%) have expected count less than 5. The minimum expected count is .03. 104 q2h * q2b ChiSquare Tests p xpected count is .03. ChiSquare Tests a 37 cells ( q2h * q2c Asymp. Sig. Value df l2sided) pearson ChiSquare 124.255(a) 36 .000 UkelihOOd ~atio 73.329 36 .000 Unearb¥Llnear 36.847 1 _000 AsSOCiation N of Valid Cases 354 75.5% have ex ected count less than 5. The minimum e p xpected count is .09. ChiSquare Tests a 36 celiS ( q2h * q2d Asymp. Sig. Value df l2sided) ~ son ChiSquare 101.657(a) 36 .000 Pear . lihood Ratio 65.955 36 .002 uke r byLinear 16.661 1 .000 Unea  . ,ASSOCiatiOn N of valid Cases 350 73.5% have ex ected count less than 5. The minimum e xpected count is .02. ChiSquare Tests a 37 celiS ( q2h * q2e ... Asymp. Sig. Value df (2sided) ~ "'"'O'n ChiSquare 408.956(a) 36 .000 Pears . . Iihood RatiO 172.134 36 .000 ~~:ar_b¥Linear 132.107 1 .000 AsSOCiatiOn N of Valid Cases 353 75.5%) have expected count less than 5. The minimum e xpected count is .03. a 3B celiS ( ....  Asymp. Sig. Value df (2sided) i""""'" on ChiSquare 193.185(a) 36 .000 Pears . 122.029 36 .000 'k8lihOOd ~atlO U byLinear 92.305 1 .000 Unear . 'atlOn As5~1 lid Cases 354 Nof a 77.6%) have expected count less than 5. The minimum e 105 q2h * q2f ChiSquare Tests Asymp.8ig. Value df (2sided)' Pearson ChiSquare 136.367(a) 36 .000 U elihood Ratio 76.547 36 .000 Unear·by·Unear 37.991 1 Association .000 of Valid Cases 352 a 37 cells (75.5%) have expected count less than 5. The minimum expected count is .05. q2h * q2g ChiSquare Tests Asymp.8ig. Value df (2sided) Pearson ChiSquare 55.056(a) 36 .022 Likelihood Ratio 52.390 36 .038 Unear·byUnear 16.898 1 .000 Association of Valid Cases 352 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .26. q2g * q2a ChiSquare Tests Asymp.8ig. Value df (2sidedl Pearson ChiSquare 67.394(a) 36 .001 U elihood Ratio 55.704 36 .019 UnearbyUnear 19.087 1 .000 soc aUon of Valid Cases 352 27 cells (55.1%) have expected count less than 5. The minimum expected count is .27. q2g· q2b ChiSquare Tests Asymp.8ig. Value df (2sided) Pearson ChiSquare 49.958(a) 36 .061 U elihood Ralio 52.614 36 .036 Unearby·Unear 12.418 1 .000 ociation of Valid Cases 353 a 30 cells (61.2%) have expected count less than 5. The minimum expected count is .20. 106 9 * q2c ChiSquare Tests pearson Chi·Square UkelihOOd ~atio ljnear_byLlOear AsSOCiation of Valid Cases Value 74.291 (a) 72.269 29.280 349 df 36 36 1 a 23 cells (46.9%) have expected count less than 5. The minimum expected count is .55. q2g * q2e __renn Chisquare p~ t' (Jkefihood ~a 10 _ r_byLinear unea iatiOn As~alid Cases ChiSquare Tests Value df 76.138(a) 73.321 15.842 353 36 36 1 ChiSquare Tests a 29 cells (59.2%) have expected count less than 5. The minimum expected count is .20. q2g * q2f n ChiSquare p_eaf'SihOOOd Aat'10 uLJnkBeIarbyLinear 'ation As;v'alid Cases Value df 193.489(a) 155.198 82.883 351 36 36 1 lis (49.0%) have expected count less than 5. The minimum expected count is .26. a 24C8 * q1d ChiSquare Tests Value df 36.374(a) 24 34.216 24 3.879 1 353 liS (48.6%) have expected count less than 5. The minimum expected count is .25. a 17C8 107 q2f· q1e ChiSquare Tests Asymp. Si9. Value df (2sided) Pearson ChiSquare 36.129(a) 24 .053 U elihood Ratio 34.467 24 .077 UnearbyLinear Association 5.622 1 .018 of Valid Cases 353 a 20 cells (57.1 %) have expected count less than 5. The minimum expected count is .11. q2f· q2a ChiSquare Tests Asymp. Si9. Value df (2sided)' Pearson ChiSquare 94.307(a) 36 .000 Likelihood Ratio 66.300 36 .002 LinearbyUnear 29.944 1 .000 Association of Valid Cases 352 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .05. q2f. q2b ChiSquare Tests Asymp. Si9. Value df (2sided>' Pearson ChiSquare 112.159(a) 36 .000 Li elihood Ratio 77.474 36 .000 n arbyUnear 38.281 1 .000 Associat on of Valid Cases 353 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .03. q2f· q2c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 190.425(a) 36 .000 li elihood Ratio 113.129 36 .000 UnearbyLinear 45.040 1 .000 Association of Valid Cases 349 a 29 cells (59.2%) have expected count less than 5. The minimum expected count is .09. 108 * q2d ChiSquare Tests p unt less than 5. The minimum expected count is .03. Asymp. Big. Value df (2sided) Pearson ChiSquare 95.463(a) 36 .000  ih()Od Ratio 55.211 36 .021 LjnearbyLinear 24.278 1 .000 AssOCiation of Valid Cases 352 0 have ex ected co .. a 33 celiS (67.3 Vo) q2f * q2e ChiSquare Tests __.en" ChiSquare Pt:UP . LjkBIihOOd ~atlO unear_b~L.,"ear AsSOCiatiOn of Valid Cases Value 231.894(a) 101.766 58.636 353 df 36 36 1 cells (67.3%) have expected count less than 5. The minimum expected count is .03. a33 e * q1b ChiSquare Tests xpected count is .14. a 20 oellS (  Asymp. Big. Value df (2sided) ~ChiSquare 43.247(a) 24 .009 P. ihood ~alro 47.614 24 .003 . r_byunear 3.700 1 .054 L,jneS iatjO" ~aJidCases 355 57.1%) have expected count less than 5. The minimum e e * q1d ChiSquare Tests Value df 35.904(a) 24 34.125 24 3.146 1 355 II (54.3%) have expected count less than 5. The minimum expected count is .19. a 19 ce S 109 ChiSquare Tests ChiSquare Tests ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 137.182(a) 36 .000 Li elihood Ratio 86.364 36 .000 UnearbyL1near Association 56.405 1 .000 of Valid Cases 355 q2e· q2b q2e· q1e q2e * q2a a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .08. a 36 cells (73.5%) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df (2sided) Pearson ChiSquare 33.529(a) 24 .093 Li elihood Ratio 34.806 24 .071 UnearbyL1near 6.419 1 .011 Association of Valid Cases 355 Asymp. Sig. Value df (2sided) Pearson ChiSquare 69.168(a) 36 .001 Likelihood Ratio 43.473 36 .183 UnearbyLinear 6.149 1 .013 Association N of Valid Cases 354 .. a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .03. q2e· q2c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 68.015(a) 36 .001 Likelihood Ratio 53.302 36 .032 UnearbyL1near 14.444 1 .000 Association N of Valid Cases 351 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .07. 110 e* q2d ChiSquare Tests 36 36 1 df 354 Value 264.936(a) 161.514 104.371 a 34 cells (69.4%) have expected count less than 5. The minimum expected count is .03. d * q1a ChiSquare Tests ~n ChiSquare P t' _0 __ ..A Aa 10 ljkeI'rK1'""" byLinear L.if1eBJ" " AsSOCiat.on of valid Cases Value 43.810(a) 41.544 .053 354 df 24 24 1 lis (62.9%) have expected count less than 5. The minimum expected count is .07. a 22ce d * q1C ChiSquare Tests Value df 46.031 (a) 24 50.429 24 4.564 1 354 lis (51.4%) have expected count less than 5. The minimum expected count is .13. a 1S ce .. q1e ChiSquare Tests .000 24 24 1 Value df 80.733(a) 84.401 28.412 ChiSquare P~oodAatio .h _Linear L,jrIeBfbY "alion NJBDCIaI"d cases cA V • 354 II (57.1%) have expected count less than 5. The minimum expected count is .08. a 20cB s III q2d * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 169.115(a) 36 .000 Likelihood Ratio 65.009 36 .002 LinearbyLinear Association 2.495 1 .114 N of Valid Cases 353 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .03. q2d * q2b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 134.754(a) 36 .000 Likelihood Ratio 71.059 36 .000 LinearbyLinear 24.332 1 .000 Association N of Valid Cases 354 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .03. q2d * q2c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 78.329(a) 36 .000 Likelihood Ratio 66.271 36 .002 LinearbyLinear 11.782 1 .001 Association N of Valid Cases 350 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .07. q2c * q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 33.311(a) 24 .098 Likelihood Ratio 40.274 24 .020 LinearbyLinear 1.621 1 .203 Association N of Valid Cases 351 a 18 cells (51.4%) have expected count less than 5. The minimum expected count is .34. 11 c '* q2a ChiSquare Tests a 32 celiS (65.3 Yo) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sidedl pearson ChiSquare 97.819(a) 36 .000 UkelihOOd Ratio 70.027 36 .001 Ljneaf_byLinear 25.575 1 .000 AsSOCiation of Valid Cases 350 0 .. 2C'* q2b ChiSquare Tests a 32 celis ( p pected count is .07. ~ Asymp. Sig. Value df (2sidedl ~pearSOn ChiS.quare 224.335(a) 36 .000 'kelihood ~atlo 159,983 36 .000 t;nearb yllnear 73.359 1 .000 iatlon ~alidcases 351 65.3% have ex ected count less than 5. The minimum ex 2b'* q1e ChiSquare Tests a 21 celis ( p xpected count is .08. ~ Asymp. Sig. Value df (2sidedl pop: earSO" ChiS.quare 45.062(a) 24 .006 'kBlihood ~atlo 41.619 24 .014 'nearbVLlnear 3.403 1 .065 ASSOCiation of Valid Cases 355 60.0% have ex ected count less than 5. The minimum e b'* q2a ChiSquare Tests a 34 celiS ( xpected count is .03.  Asymp. Sig. Value df (2sided\ I"p':eaSr On ChiSquare 172.923(a) 36 .000 'kelihood Ratio 106.781 36 .000 Lu!near_byLinear 47.072 1 .000 iation ~aJjdCaSeS 354 69.4%) have expected count less than 5. The minimum e II q2a * q1a ChiSquare Tests a 23 cells (65.7%) have expected countless than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sided) Pearson ChiSquare 33.888(a) 24 .087 Likelihood Ratio 30.597 24 .166 LinearbyLinear Association 2.315 1 .128 N of Valid Cases 354 .. q2a * q1e ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 41.464(a) 24 .015 Likelihood Ratio 38.324 24 .032 LinearbyLinear .031 1 .860 Association N of Valid Cases 354 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .11. q1e*q1a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 503.573(a) 16 .000 Likelihood Ratio 336.788 16 .000 LinearbyLinear 60.371 1 .000 Association N of Valid Cases 384 a 14 cells (56.0%) have expected count less than 5. The minimum expected count is .37. q1e * q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 167.360(a) 16 .000 Likelihood Ratio 200.463 16 .000 LinearbyLinear 69.779 1 .000 Association N of Valid Cases 384 a 10 cells (40.0%) have expected count less than 5. The minimum expected count is .61. 114 e'" q1c ChiSquare Tests ChiSquare Tests u tess than 5 The minimum e pected count is .54. Asymp. Sig. Value df (2sided)' ~earson ChiSquare 205.B79(a) 16 .000 UkelihOOd ~atio 268.610 16 .000 tjneaf_byLlnear 68.841 1 .000 AsSOCiatIon of Valid Cases 384 00 v ex ected co n I .. a 10 cells (40.07<) ha e p x e'" q1d p xpected count is .85. ChiSquare Tests a 10 celiS ( d'" q1a Asymp. Sig. Value df (2sidedl pea,son ChiSquare 164.490(a) 16 .000 L.ikBlihood ~atio 173.372 16 .000 • ear_byllnear 81.247 1 .000 un iation ~aJidcases 384 40.0% have ex ected count less than 5. The minimum e xpected count is .72. ChiSquare Tests a 15 celiS ( 1d It q1b .... Asymp. Sig. Value df (2sidedl ~ SOn ChiSquare 279.870(a) 16 .000 pear . LJkeIihood ~atlo 230.692 16 .000 u•naar_byllnear 175.381 1 .000 jation ~alidcases 384 60.0%) have expected count less than 5. The minimum e a ected count is 1.17. 6 celiS (   Asymp. Sig. Value df (2sided) ~ear.;on ChiSquare 544.250(a) 16 .000 P'kBlihoOd ~atio 489.750 16 .000 ~ ar_byllnear 12.718 1 .000 una iation ~aJidcases 384 24.0%) have expected count less than 5. The minimum exp 11 q1d*q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 146.562(a) 16 .000 Likelihood Ratio 191.284 16 .000 LinearbyLinear 8.687 1 Association .003 N of Valid Cases 384 a 7 cells (28.0%) have expected count less than 5. The minimum expected count is 1.04. q1c*q1a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 85.681 (a) 16 .000 Likelihood Ratio 92.436 16 .000 LinearbyLinear 4.926 1 .026 Association N of Valid Cases 384 a 13 cells (52.0%) have expected count less than 5. The minimum expected count is .46. q1c * q1b ChiSquare Tests Asymp. Sig. Value df 12sided)' Pearson ChiSquare 156.326(a) 16 .000 Likelihood Ratio 223.398 16 .000 LinearbyLinear 12.424 1 .000 Association N of Valid Cases 384 a 8 cells (32.0%) have expected count less than 5. The minimum expected count is .75. q1b * q1a ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 260.047(a) 16 .000 Likelihood Ratio 203.991 16 .000 LinearbyLinear 148.535 1 .000 Association N of Valid Cases 384 a 13 cells (52.0%) have expected count less than 5. The minimum expected count is .52. 116 q7 * q2a ChiSquare Tests a 17 cells (60.7%) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 31.621(a) 18 .024 Likelihood Ratio 32.552 18 .019 LinearbyLinear 1.002 1 .317 Association N of Valid Cases 348 · . q7 * q2e ChiSquare Tests a 17 cells (60.7%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 28.434(a) 18 .056 Likelihood Ratio 27.491 18 .070 LinearbyLinear .131 1 Association .717 N of Valid Cases 349 · . q7 * q2h ChiSquare Tests a 19 cells (67.9 Yo) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df (2sided) pearson ChiSquare 27.802(a) 18 .065 Likelihood Ratio 27.520 18 .070 LinearbyLinear 8.875 1 .003 Association N of Valid Cases 348 0 · . 117 Part 2: Environmental Soil Science Survey Questions Ql) Please rank the following factors in selecting your major. (Imost influential to 5least influential) Qla  Personal Interest Q 1b  Popularity of major Qlc  Family influences Qld  Scholarship Availability Qle  Potential Income Q2) Have you ever been in a major other than Environmental Soil Science at BYU? Q3) What was your previous major, if applicable? Q4) If you answered yes to the previous question, what led you to switch majors? Q5) What do you like about the Environmental Soil Science Program? Q6) What do you dislike about the Environmental Soil Science Program? Q7) Have you ever thought of leaving the ESS program for another major at BYU? Q8) Why did you consider leaving the program? Q9) Rate the importance of the following in regards to your major: Q9a  The academic qualifications of the Professors Q9b  Availability of professors for consultations Q9c  National reputation of the program Q9d  General Atmosphere/Environment of the Plant and Animal Sciences Department Qge  Job availability following graduation Q9f  Starting base salary after graduation Q9g  Variety of Environmental Science core courses Q9h  The inclusion of basic science courses in the Environmental Soil Science curriculum Q9i  The number of faculty and students in the Environmental Soil Science program Q9j  Communication among students and faculty in the program Q9k  Internship opportunities Q91  Field trips with students and faculty Q9m  Preparation for postgraduate education Q10) On a scale of 17l how satisfied are you with the availability of: QI0a  classes offered by the Plant and Animal Sciences Department? QI0b  basic science classes offered by other departments on campus? Qll) What semesters/terms should more Plant and Animal Science clas es be offered? Ql1aFall Q11b  Winter Q11c  Spring Ql1d  Summer Q11e  No need Q 12) Rate the following: Q12a  Teaching Faculty Q12b  Mentored research opportunities Q12c  Communication between students within the Environmental Soil Science program Ql2d  Communication between student and faculty within the Environmental Soil Science program Q13) How would you rate the difficulty of the Environmental Soil Science major? Q 14) In your opinion, changing the name of the program from Environmental Soil Sciellce t Environmental Science would help attract more tudents? Q15) What advice would you give to a student thinking of tudying Environm ntal Q16) Gender Q17) What is your age? Q18) Year in School II Year in School (q18) * q1 C ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 10.000(a) 4 .040 Likelihood Ratio 9.780 4 .044 LinearbyLinear Association .004 1 .947 N of Valid Cases 10 a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .20. Year in School (q18) * q9b ChiSquare Tests Asymp. Sig. Value df 12sided)' Pearson ChiSquare 6.032(a) 2 .049 Likelihood Ratio 6.811 2 .033 LinearbyLinear 5.161 1 .023 Association N of Valid Cases 10 a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .60. Age (q17) * q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 15.143(a) 9 .087 Likelihood Ratio 11.032 9 .273 LinearbyLinear .621 1 .431 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Age (q17) * q1d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 14.933(a) 9 .093 Likelihood Ratio 11.090 9 .270 LinearbyLinear 2.647 1 .104 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. 120 Age (q17) * q9c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 19.000{a) 12 .089 Likelihood Ratio 18.867 12 .092 LinearbyLinear Association .717 1 .397 N of Valid Cases 10 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Age (q17) * qge ChiSquare Tests a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .20 Asymp. Sig. Value df (2sided) Pearson ChiSquare 13.600{a) 6 .034 Likelihood Ratio 13.863 6 .031 LinearbyLinear .043 1 .835 Association N of Valid Cases 10 .. Gender (q16) * q7 ChiSquare Tests Asymp. Sig. Exact Sig. Exact Sig. Value df (2sided) (2sided) (lsided) Pearson ChiSquare 3.600(b) 1 .058 Continuity 1.600 1 .206 Correction{a) Likelihood Ratio 3.855 1 .050 Fisher's Exact Test .206 .103 LinearbyLinear Association 3.240 1 .072 N of Valid Cases 10 a Computed only for a 2x2 table b 4 cells (100.0%) have expected count less than 5. The minimum expected count is 2.50. 121 Gender (q16) * q9h ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 8.000(a) 4 .092 Likelihood Ratio 11.090 4 .026 LinearbyLinear Association 2.194 1 .139 N of Valid Cases 10 a 10 cells (100.0%) have expected count less than 5. The minimum expected count is .50. q14 * q9k ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 15.333(a) 9 .082 Likelihood Ratio 16.774 9 .052 LinearbyLinear 2.326 1 .127 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q14 * q9m ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 17.167(a) 9 .046 Likelihood Ratio 14.001 9 .122 LinearbyLinear .987 1 .321 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12d * q7 ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 6.800(a) 2 .033 Likelihood Ratio 8.859 2 .012 LinearbyLinear .111 1 .739 Association N of Valid Cases 10 a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .50. q12d * q12a ChiSquare Tests a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 7.917(a) 4 .095 Likelihood Ratio 9.641 4 .047 LinearbyLinear Association 3.025 1 .082 N of Valid Cases 10 . . q12d * q12b ChiSquare Tests Asymp. Sig. Value Of (2sided) Pearson ChiSquare 13.025(a) 4 .011 Likelihood Ratio 9.364 4 .053 LinearbyLinear 5.818 1 .016 Association N of Valid Cases 10 a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12d * q12c ChiSquare Tests Asymp. Sig. Value Of (2sided) Pearson ChiSquare 16.625(a) 6 .011 Likelihood Ratio 14.368 6 .026 LinearbyLinear 7.402 1 .007 Association N of Valid Cases 10 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12c * q9d ChiSquare Tests Asymp. Sig. Value Of (2sidedl Pearson ChiSquare 16.667(a) 9 .054 Likelihood Ratio 14.507 9 .105 LinearbyLinear 3.640 1 .056 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. 12 q12c * q12a ChiSquare Tests , Asymp. Sig. Value df /2sided)' Pearson ChiSquare 15.000(a) 6 .020 Likelihood Ratio 12.414 6 .053 LinearbyLinear Association 4.551 1 .033 N of Valid Cases 10 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12c*q12b ChiSquare Tests ChiSquare Tests ChiSquare Tests q12b * q7 a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .50. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 6.800(a) 2 .033 Likelihood Ratio 8.859 2 .012 LinearbyLinear .818 1 .366 Association N of Valid Cases 10 . . a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .40. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 5.000(a) 2 .082 Likelihood Ratio 6.730 2 .035 LinearbyLinear .136 1 .712 Association N of Valid Cases 10 q12b * q2 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 13.250(a) 6 .039 Likelihood Ratio 9.870 6 .130 LinearbyLinear 2.979 1 .084 Association N of Valid Cases 10 124 q12b * q9d ChiSquare Tests a 12 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value Of (2.sidedf Pearson ChiSquare 14.375(a) 6 .026 Likelihood Ratio 11.596 6 .072 LinearbyLinear .818 1 .366 Association N of Valid Cases 10 0 · . q12a * q9f ChiSquare Tests a 12 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .10. r Asymp. Sig. Value Of (2sided) ~earson ChiSquare 12.222(a) 6 .057 Likelihood Ratio 14.140 6 .028 LinearbyLinear .034 1 .855 Association N of Valid Cases 10 0 · . q12a * q10b ChiSquare Tests a 15 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .10.  Asymp. Sig. Value Of (2sided) pearson ChiSquare 16.000(a) 8 .042 Likelihood ~atio 12.955 8 .113 LinearbyLlnear .610 1 .435 Association N of Valid Cases 10 0 · . q10b * q1a ChiSquare Tests a 10 cells ( p count less than 5. The minimum expected count is .10.  Asymp. Sig. Value Of (2sidedf ~rson ChiSquare 10.000(a) 4 .040 LikelihOod ~atio 6.502 4 .165 Linearbylinear 5.976 1 .015 Association N of Valid Cases 10 100.0% have ex ected · . 12 q10b * q7 ChiSquare Tests a 10 cells (100.0%) have expected count less than 5. The minimum expected count is .50. Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.000(a) 4 .040 Likelihood Ratio 13.863 4 .008 LinearbyLinear 1.098 1 Association .295 N of Valid Cases 10 . . q10b * q9a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 14.600(a) 8 .067 Likelihood Ratio 12.137 8 .145 LinearbyLinear 2.831 1 .092 Association N of Valid Cases 10 a 15 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q10a * q1d ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 27.333(a) 15 .026 Likelihood Ratio 20.593 15 .150 LinearbyLinear .064 1 .800 Association N of Valid Cases 10 a 24 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q9m * q9a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 25.575(a) 12 .012 Likelihood Ratio 17.251 12 .140 LinearbyLinear 8.412 1 .004 Association N of Valid Cases 11 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. 126 q9m * q9b ChiSquare Tests a 15 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 15.016(a) 8 .059 Likelihood Ratio 10.740 8 .217 LinearbyLinear Association 6.587 1 .010 N of Valid Cases 11 · . q9m * q9d ChiSquare Tests a 25 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 27.5OO(a) 16 .036 Likelihood Ratio 21.209 16 .171 LinearbyLinear 4.891 1 .027 Association N of Valid Cases 11 · . q9m * qge ChiSquare Tests a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 28.111 (a) 12 .005 Likelihood Ratio 21.888 12 .039 LinearbyLinear 5.521 1 .019 Association N of Valid Cases 11 · . q9m * q9g ChiSquare Tests a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value Of (2sidedf Pearson ChiSquare 22.889(a) 12 .029 Likelihood Ratio 15.727 12 .204 LinearbyLinear 3.960 1 .047 Association N of Valid Cases 10 · . 127 q9m * q91 ChiSquare Tests Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value 19.250(a) 15.664 5.620 11 df 12 12 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q91 * q9a ChiSquare Tests Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value df 16.913(a) 13.799 5.162 11 9 9 1 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q91 * q9b ChiSquare Tests Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value 12.833(a) 8.875 4.360 11 df 66 1 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q91 * q9f ChiSquare Tests a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value 19.250(a) 16.710 .652 11 df 12 12 1 128 q9k * q9d ChiSquare Tests a 20 cells (100.0 Yo) have expected count less than 5. The minImum expected count is .18. Asymp. Sig. Value Of (2sided) pearson ChiSquare 21.389(a) 12 .045 Likelihood Ratio 21.888 12 .039 LinearbyLinear 5.614 1 .018 Association N of Valid Cases 11 0 . . q9k * q9j ChiSquare Tests a 16 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .18. Asymp. Sig. Value Of (2sided)" pearson ChiSquare 21.389(a) 9 .011 Likelihood Ratio 21.888 9 .009 LinearbyLinear Association 6.986 1 .008 N of Valid Cases 11 0 · . q9j * q1e ChiSquare Tests a 12 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .27. .. Asymp. Sig. Value Of (2sided) pearson ChiSquare 12.222(a) 6 .057 Likelihood Ratio 14.112 6 .028 LinearbyLinear 4.607 1 .032 Association N of Valid Cases 11 0 · . q9j * q9a ChiSquare Tests a 16 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .09.  Asymp. Sig. Value Of (2sidedf '"Pearson ChiSquare 17.188(a) 9 .046 Likelihood Ratio 13.432 9 .144 LinearbyLinear 4.206 1 .040 Association N of Valid Cases 11 0 · . J2 ChiSquare Tests q9i * q9c a 25 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df l2sided) Pearson ChiSquare 23.681 (a) 16 .097 Likelihood Ratio 21.888 16 .147 LinearbyLinear .900 Association 1 .343 N of Valid Cases 11 . . q9h * q1a ChiSquare Tests Asymp. Sig. Value df l2sided) Pearson ChiSquare 11.000{a) 4 .027 Likelihood Ratio 6.702 4 .152 LinearbyLinear 2.815 1 .093 Association N of Valid Cases 11 a 10 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q9g * q1a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.000{a) 3 .019 Likelihood Ratio 6.502 3 .090 LinearbyLinear .455 1 .500 Association N of Valid Cases 10 a 8 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q9g * q1e ChiSquare Tests Asymp. Sig. Value df l2sided) Pearson ChiSquare 14.444{a) 6 .025 Likelihood Ratio 16.774 6 .010 LinearbyLinear 5.432 1 .020 Association N of Valid Cases 10 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .20. ChiSquare Tests ChiSquare Tests ChiSquare Tests 131 ChiSquare Tests a 10 cells (100.0%) have expected countless than 5. The minimum expected count is .45• a 16 cells (100.0%) have expected count less than 5. The minimum expected count Is .09. qge * q9a Asymp. ~\g. Value Of 12sided Pearson ChiSquare 16.885(a) 9 .051 Likelihood Ratio 12.247 9 .200 LinearbyLinear Association 5.935 1 .015 N of Valid Cases 11 a 16 cells (100.0%) have expected countless than 5. The minimum expected count is .10. Asymp. Sig. Value Of 12sidedl Pearson ChiSquare 8.983(a) 4 .062 Likelihood Ratio 12.386 4 .015 LinearbyLinear .774 1 .379 Association N of Valid Cases 11 . . q9f * q2 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q9g * qge Asymp. Sig. Value Of 12~sidedl Pearson ChiSquare 16.400(a) 9 .059 Likelihood Ratio 12.816 9 .171 LinearbyLinear 6.848 1 .009 Association N of Valid Cases 10 q9g * q9b Asymp. Sig. Value Of (2sidedl Pearson ChiSquare 12.286(a) 6 .056 Likelihood Ratio 9.306 6 .157 LinearbyLinear 5.998 1 .014 Association N of Valid Cases 10 .. qge * q9b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 11.698(a) 6 .069 Likelihood Ratio 7.324 6 .292 LinearbyLinear 7.900 1 .005 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. qge * q9d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 27.133(a) 12 .007 Likelihood Ratio 21.750 12 .040 LinearbyLinear 7.621 1 Association .006 N of Valid Cases 11 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q9d * q9b ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 15.452(a) 8 .051 Likelihood Ratio 11.648 8 .168 LinearbyLinear 7.519 1 .006 Association N of Valid Cases 11 a 15 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q9c * q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 22.000(a) 12 .038 Likelihood Ratio 16.710 12 .161 LinearbyUnear .303 1 .582 Association N of Valid Cases 11 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. 132 q9b * q9a ChiSquare Tests a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 15.714(a) 6 .015 Likelihood Ratio 12.189 6 .058 LinearbyLinear Association 7.060 1 .008 N of Valid Cases 11 .. q7 * q1c ChiSquare Tests a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .45. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 5.622(a) 2 .060 Likelihood Ratio 7.520 2 .023 LinearbyLinear .014 1 .905 Association N of Valid Cases 11 .. q1e * q1c ChiSquare Tests Asymp. Sig. Value Of (2sided) Pearson ChiSquare 8.800(a) 4 .066 Likelihood Ratio 10.660 4 .031 LinearbyLinear .011 1 .915 Association N of Valid Cases 11 a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .27. q1e*q1d ChiSquare Tests Asymp. Sig. Value Of (2sidedl Pearson ChiSquare 11.000(a) 6 .088 Likelihood Ratio 15.158 6 .019 LinearbyLinear 2.143 1 .143 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .27. q1d * q1b ChiSquare Tests Asymp. Sig. Value df (2sided\ Pearson ChiSquare 22.688(a) 9 .007 Likelihood Ratio 14.076 9 .120 LinearbyLinear .365 1 Association .546 N of Valid Cases 11 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q1d*q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 15.125(a) 6 .019 Likelihood Ratio 11.844 6· .066 LinearbyLinear 1.077 1 .299 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q1c*q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 13.406(a) 6 .037 Likelihood Ratio 9.577 6 .144 LinearbyLinear .579 1 .447 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q1b * q1a ChiSquare Tests Asymp. Sig. Value df (2sided\' Pearson ChiSquare 11.000(a) 3 .012 Likelihood Ratio 6.702 3 .082 LinearbyLinear 5.147 1 .023 Association N of Valid Cases 11 a 7 cells (87.5%) have expected count less than 5. The minimum expected count is .09. 134 Appendix B Statically Insignificant Cross Tabulations General Survey Question Chlsquared value Year In School (a11) • a1a 0.730 Year In School (a11) • a1c 0.143 Year In School (a11) • a1 e 0.215 Year In School (a11) • q2a 0.128 Year In School (q11) • a2c 0.479 Year In School (a11) • a2f 0.138 Year In School (a11) • a2Q 0.609 Year In School (a11) • a2i 0.379 Year In School (a11) • a3a 0.546 Year In School (a11) • a3b 0.679 Year In School (a11) • a3c 0.719 Year In School (q11) • q3d 0.261 Year In School (a11) • a3e 0.175 Year In School (a11) • q4 0.806 Year In School (a11) • a7 0.408 AQe (q10) • q1C 0.273 Aae (a10) • a2c 0.160 AQe (q10) • q2f 0.921 Age (q10) • q2g 0.376 Aae (a10\' a2i 0.131 AQe (a10)' 03a 0.428 Aae (a10\' a3b 0.961 Ace (a10)' a3c 0.842 Age (q10)' q3d 0.397 Age (q10) • 03e 0.196 Aae (a10)' a4 0.182 AQe (a10) • a7 0.857 Gender (a9)' a1d 0.112 Gender (q9) • a2a 0.271 Gender (09) • a2b 0.455 Gender (a9) • a2c 0.736 Gender (a9) • a2f 0.133 Gender (a9) • 020 0.901 Gender (a9) • a3a 0.592 Gender (a9) • a3b 0.377 Gender (09) • 03c 0.394 Gender (a9) • a3d 0.409 Gender (q9) • q4 0.309 Question Chisquared value Gender (a9) • a5 0.515 Department (q8) • a1a 0.690 Department (a8) • a1c 0.515 Department (a8)' a1e 0.311 Department (a8) • q2a 0.666 Department (a8)' 02b 0.196 Department (a8) • a2f 0.320 Department (a8) • 020 0.324 Department (a8) • a3a 0.569 Department (a8) • 03b 1.000 Department (a8) • a3c 0.433 Department (08) • a3d 0.199 Department (08) • a4 0.710 Department (q8) • a5 0.490 Department (a8) • a7 0.208 a1a' a5 0.860 a5 • a1b 0.552 q5 • a1c 0.721 q5' a1d 0.498 as' a1e 0.931 as' a2a 0.384 as' a2c 0.778 as' a2d 0.445 q5' 02e 0.579 05' a2f 0.257 as' a2Q 0.120 a5' a2h 0.648 a5' a3a 0.229 as' a3b 0.804 as' a3d 0.600 q5' q3e 0.903 a4' a1a 0.163 a4' a1b 0.768 a4' a1c 0.615 04' q1d 0.345 a4' a1e 0.506 q4' q2b 0.522 a4' a2c 0.813 136 Question Chlsquared value 04· a2d 0.441 a4· a2e 0.891 a4· q2f 0.977 q4· a2Q 0.725 a4· q2h 0.269 q4· a2i 0.270 q4· a3b 0.585 a4· a3d 0.929 04· a3e 0.876 a3e * a2c 0.254 03e * a2a 0.928 a3e * a3d 0.261 a3d * Q1a 0.645 a3d * a1b 0.314 a3d * a1c 0.558 a3d * a1d 0.228 a3d * Q1e 0.440 a3d * a2b 0.754 a3d * Q2c 0.258 a3d * a2d 0.112 a3d * a2a 0.111 a3d * Q21 ~ . 0.118 a3d * a3b 0.420 a3c * Q1a 0.429 a3c*a1b 0.174 a3c * a1d 0.246 a3c * a1e . 0.966 a3c * a2a 0.183 a3c * a3b 0.481 q3b· a1a 0.957 q3b· Q1b 0.282 a3b· Q1c 0.586 a3b· a1d 0.564 q3b • a1e 0.803 q3b· a2g 0.286 a3a * Q1a 0.991 a3a • a1b 0.545 a3a· Q1c 0.298 Question Chi· squared value a3a· q1d 0.412 q3a· Q1e 0.679 a3a· a2e 0.139 q2i * q1a 0.231 q2i· q1b 0.607 q2i • q1c 0.744 q2i· q1e 0.661 a2a * a1a 0.340 a2q * a1b 0.976 a2a * a1c 0.607 a2q * a1d 0.706 Q2q· Q1e 0.450 a2a * a2d 0.174 a2f*a1a 0.444 Q2f*Q1b 0.833 a2f * a1c 0.308 Q2e· Q1a 0.952 a2e*a1c 0.131 Q2d * Qlb 0.162 a2d * ald 0.188 Q2c * a1a 0.316 Q2c * Q1b 0.257 a2c * a1d 0.326 Q2c· a1e 0.304 a2b * ala 0.127 a2b * alb 0.216 Q2b· a1c 0.775 Q2b * 01d 0.331 a2a*a1b 0.663 02a * 01c 0.911 Q2a * old 0.802 q7· q1a 0.431 Q7·01b 0.401 a7 * a1c 0.735 07 * a1d 0.141 07 * a1e 0.201 07 * 02b 0.543 07 * 02c 0.762 137 Question Chisquared value 07· a2d 0.219 07· a2f 0.168 07· q2q 0.363 07·02i 0.235 07· q3a 0.116 07· a3b 0.119 07· a3c 0.205 07· a3d 0.321 07' a3e 0.701 q7' 04 0.697 07· q5 0.893 138 Environmental Soil Science Survey  Chi· Question squared value Year in School (018) • ala 0.690 Year in School (018)· alb 0.171 Year in School (018) * old 0.125 Year in School (018) * ale \ 0.695 Year in School (018) * 02 0.108 Year in School (018) * 07 0.264 Year in School (018)' a9a 0.255 Year in School (018)· 09c 0.167 Year in School (018) * a9d 0.277 Year in School (018) * 0ge 0.695 Year in School (018) * 09t 0.301 Year in School (q18) * q9g 0.844 Year in School (018) * 09h 0.352 Year in School (018)' 09i 0.697 Year in School (q18) • q9j 0.353 Year in School (018)' 09k 0.617 Year in School (q18) * 091 0.645 Year in School (q18) * q9m 0.411 Year in School (018) * al0a 0.371 Year in School (q18) * 010b 0.793 Year In School (018) * 012a 0.797 Year in School (018) * a12b 0.287 Year in School (018) * a12c 0.215 Year in School (018) * 012d 0.287 Year in School (018) * 013 0.228 Year in School (q18) * q14 0.353 Year in School (q18) * Gender (016) 0.264 Year in School (018)' Aqe (017) 0.238 Aqe (017) * ala 0.774 Aae (017) * alc 0.156 Aoe (q17) * ale 0.753 AQe (017) • 02 /" 0.405 Aoe (017) • 07 0.753 Ace (017) * 09a 0.412 Aoe (017) * q9b 0.283 Aoe (q17) * 09d 0.209 Aqe (017) * 09t 0.773 Age (017) * q9g 0.879 Age (017) * 09h 0.558 Chi· Question squared value Ace (017) * 09i 0.419 Age (017) * 091 0.350 Aoe (017) * aSk 0.122 Aqe (017) * 091 0.710 Age (017) * q9m 0.213 Age (q17) * ql0a 0.182 Age (q17)' a10b 0.777 Aae (017) ·012a 0.744 Age (017) * 012b 0.910 Aae (017) * 012c 0.689 Aoe (017\* 012d 0.910 Age (017) * 013 0.357 Aae (017\ * 014 0.475 Age (017) * Gender (016\ 0.362 Gender (016\ • 01a 0.292 Gender (016\ • alb 0.370 Gender (016) • 01c 0.368 Gender (016) • old 0.469 Gender (016) • ale 0.282 Gender (016\ • 02 1.000 Gender (016) • 09a 0.333 Gender (016) • 09b 0.490 Gender (016) * 09c 0.406 Gender (016) • Q9d 0.506 Gender (016\* 0ge 0.766 Gender (016) • 09f 0.343 Gender (016) • 09a 0.487 Gender (016) * a9i 0.504 Gender (016) * q9j 0.343 Gender (016) * Q9k 0.572 Gender (016) * a9I 1.000 Gender (016) * a9m 0.572 Gender (016) • olDa 0.549 Gender (016\* olOb 0.308 Gender (016) * 012a 0.513 Gender (016) * 012b 0.333 Gender (016) * 012c 0.572 Gender (016) * 012d 0.333 Gender (016) * 013 0.615 139 Chi Question squared valued Gender la16) * a14 0.753 a14*ala 0.774 a14*alb 0.892 014"01e 0.887 014"qld 0.766 q14"019 0.233 014 * 02 0.405 014" q7 0.753 a14*09a 0.385 q14*a9b 0.103 a14 * 0ge 0.215 q14 * q9d 0.182 q14 * age 0.179 a14 * q9f 0.437 q14 * a90 0.154 014 * q9h 0.572 a14 * q9i 0.357 014 * a9i 0.228 a14 * q91 0.370 014 * 010a 0.902 014 * al0b 0.777 014 * 012a 0.887 q14  012b 0.412 014 * 012e 0.509 014  012d 0.412 014 * q13 0.394 a13 * ala 0.628 a13*01b 0.163 a13*alc 0.211 01301d 0.154 013*01e 0.895 013 * a2 0.349 q13 * 07 0.323 a13*a9a 0.425 q13 * 09b 0.349 a13 * age 0.260 q13*09d 0.555 q13*age 0.495 q13 * 09f 0.273 Chi Question squared valued q13 * q90 0.857 a13 * a9h 0.648 013 * q9i 0.389 013 * q9j 0.385 a13 * a9k 0.345 q13 * q91 0.167 q13 * 09m 0.736 q13 * al0a 0.133 013 * 010b 0.599 q13 * a12a 0.352 q13 * q12b 0.371 013 * a12c 0.543 q13*q12d 0.371 a12d * ala 0.574 q12dqlb 0.715 q12d  qlc 0.263 q12d * old 0.596 a12d * ale 0.231 q12d * q2 0.392 q12d * q9a 0.541 a12d * a9b 0.270 q12d*q9c 0.619 a12d * a9d 0.115 q12d*qge 0.612 q12d * 091 0.288 a12d * a9a 0.487 q12d * a9h 0.749 q12d*q9i 0.425 a12d * 09j 0.370 q12d * 09k 0.632 q12d*a91 0.263 012d * q9m 0.298 a12d * 010a 0.783 012d * ql0b 0.238 012c * ola 0.644 012c * olb 0.834 012c * ole 0.396 012e * old 0.740 a12c*ale 0.378 140 Chl Question squared I valued a12c * a2 0.290 a12c * a7 0.261 o12c*a9a 0.298 a12c * 09b ~ 0.414 o12c * age 0.724 o12e * age 0.321 o12c * a9f 0.350 a12c * 09g 0.663 o12c * 09h 0.689 a12c * a9i 0.375 a12c * q9j 0.233 a12e * 09k 0.350 a12c * 091 i, 0.333 a12c * q9m I 0.501 a12c * 010a 0.407 a12c * 010b 0.150 a12b * a1a 0.435 a12b * a1b 0.619 a12b * 01e I 0.263 a12b * a1d L 0.731 a12b*a1e 0.231 a12b * a9a 0.541 a12b * 09b 0.270 a12b * age ~ 0.846 a12b * age 0.213 a12b * a9! 0.160 a12b * a9a n 0.487 a12b * 09h I, 0.749 a12b * a9i 0.893 a12b * 09i 0.370 a12b * q9k 0.632 a12b * 091 " 0.579 q12b * 09m 0.257 a12b * 010a 0.794 Q12b * q10b , 0.238 a12b * 012a 0.225 Q12a * a1a 0.690 q128 * 01b 0.702 012a * a1c i 0.504 Chi· Question squared valued Q12a * Q1d 0.399 a12a * a1a 0.827 012a*02 0.435 Q12a * a7 0.368 a12a*09a 0.675 a12a· a9b 0.788 Q12a·09c 0.442 a12a· a9d , 0.301 a12a * age 0.406 a12a*a9g 0.844 a12a· a9h 0.604 a12a· a9l • 0.265 a12a*a9j 0.580 012a*a9k 0.475 a12a*a91 0.212 a128 * a9m 0.898 a12a· a1Da 0.132 a10b· a1b 0.234 a1Ob· a1c 0.417 a1Ob· a1d 0.535 a10b * a1e 0.617 a1(lb * Q2 0.287 a10b * a9b 0.369 a10b * a9c 0.543 a10b * a9d 0.446 Q10b * qge 0.408 Q10b * Q9f 0.487 a10b * a9a 0.213 a10b * a9h " 0.375 a10b * a9! 0.465 a10b * a91 0.106 a10b * a9k 0.394 a10b * a91 0.248 a1Ob* a9m 0.191 Q10b * a1Da 0.412 Q1Da· Q1a 0.893 a1Da * a1b 0.124 Q1Da * Q1c 0.185 Q1Da * Qle 0.419 141 Chi· Question squared valued a10a*a2 0.323 a10a * a7 0.549 a10a * a9a 0.485 a10a * q9b 0.543 a10a * age 0.158 a10a * a9d 0.673 a10a*age 0.419 a10a * a91 0.497 a10a * a9g 0.668 a10a * a9h 0.538 a10a * a91 0.158 a10a * a9i 0.545 a10a * a9k 0.451 a1Ga * a91 0.132 a10a * a9m 0.793 a9m * a1a I 0.292 a9m*a1b 0.880 a9m· a1e 0.846 a9m * a1d 0.835 a9m * a1e 0.319 a9m * a2 0.462 a9m * a7 0.569 a9m * aSC 0.272 a9m * a9f 0.350 a9m * a9h 0.398 a9m * a91 0.729 a9m * a91 0.140 a9m * a9k 0.278 a91 * a1a 0.588 a91*a1b 0.509 a91 • a1e i 0.272 a91 • a1d 0.465 a91 * a1e 0.233 a91* q2 0.588 a91* a7 0.268 a91* a9c ' . 0.434 a91* a9d 0.302 a91* age .' ( 0.106 a91* a9g i 0.107 Chi· Question squared valued a91* a9h 0.490 a91* a91 0.211 a91* a9i 0.326 q91* a9k 0.382 a9k • a1a 0.402 a9k • a1b 0.422 a9k * a1e 0.200 a9k * a1d 0.369 a9k * a1e 0.251 a9k * a2 0.821 a9k * a7 0.402 a9k * a9a 0.422 a9k * a9b 0.189 a9k * age 0.297 a9k *age 0.188 a9k * a9f 0.490 a9k * a9a 0.427 a9k *a9h 0.145 a9k * a9i 0.428 a9i * a1a • 0.402 a91*a1b 0.650 a9i * a1e 0.131 a9i * a1d 0.276 a9i * a2 0.662 a9j * a7 0.233 a9i * a9b 0.299 a9i * aSC 0.484 a9j * a9d 0.173 a91*age 0.459 a9j * a9f 0.542 a9i * a90 0.350 a91* a9h 0.307 a9j * a9i 0.609 a91 * a1a 0.292 a91*a1b , 0.200 a9i * a1e 0.517 a9i * a1d 0.211 a91 *a1e 0.247 a91 * a2 0.811 ChIQueallon aquared velued 09;" 07 0.462 091" a9a 0.553 091" a9b 0.751 091 "Q9d 0.560 a9i" age 0.488 a9i" Q9t 0.629 a9;" Q9Q 0.191 a9;" a9h 0.378 a9h" Qlb 0.169 a9h" ale 0.381 a9h" Qld 0.452 a9h" ale 0.122 a9h" Q2 0.462 a9h" Q7  0.462 a9h" a9a ~ 0.282 a9h" Q9b 0.366 a9h" Qge 0.241 a9h" a9d 0.696 a9h" Qge 0.550 a9h" a9t 0.227 a9h' Q90 ~ 0.116 a90" alb 0.198 ago' Qle o.en a9a" old 0.350 a90 "Q2 0.528 a9a" 07 0.469 Q90 "Q9a 0.178 a9a" Qge 0.596 Q90" 09d 0.226 a90" Q9t 0.273 Q9t" Qla 0.750 Q9t"alb 0.679 a9t" Qle 0.374 Q9t" old 0.609 091" Qle 0.488 a9t "07 0.688 a9l' Q9a 0.258 091" a9b 0.153 091' a9c 0.586 ChIQuestion aqU8Rd valued 091" Q9d 0.252 Q9I" Qge 0.198 0ge" ala 0.402 Qge "Qlb 0.789 age "Qle 0.590 age" Old 0.660 Qge "Qle 0.148 age" 02 0.693 Qge" Q7 0.693 age" a9c 0.467 Q9d"Qla 0.750 a9d" alb 0.843 Q9d "Qle 0.558 a9d" old I 0.765 Q9d "Qle 0.319 Q9d' Cl2 0.514 09d' 07 0.514 Q9d'age 0.120 Q9d"a9c 0.414 a9c "Qla 0.292 a9c'Qle 0.105 a9c" old 0.135 a9c' Qle 0.488 a9c" Cl2 0.688 a9c" a7 0.688 a9c"age 0.452 a9c'a9b 0.797 a9b'ala 0.730 a9b" alb 0.686 a9b" ale 0.338 a9b" aId 0.651 a9b' ale 0.299 a9b" Cl2 0.280 a9b" a7 0.497 a9a "ala 0.724 age' alb 0.878 a9a" ale 0.333 age 'aId 0.718 age "al. 0.408 143 Question Chisquared valued 0.371 0.547 0.251 0.329 0.402 0.176 0.122 0.338 0.402 0.273 0.520 0.676 0.231 0.481 0.821 0.632 144 Appendix C Transcript from Focus Group Transcript from Focus Group Transcript for the focus group held February 15, 2006 in the Jesse Knight Building on BYU campus. The group was moderated by Bryce Youngquist. Participation by: Jenny Cox, Blaine Bateman, Russell Memory, and Baxter Oliphant. Those speaking will be referred to by their initials accordingly. BY: Welcome to the focus group everyone. First we'll start with Blaine. Say your name, how old you are, where are you from, what's your major, hobbies. BB: My name's Blaine Bateman. I'm 22 years old, I'm a sophomore here at BYU, I come from Calleville, WA. And my hobbies are golf, fishing, boating, baseball, that sort of stuff. My major is landscape management. JC: My name is Jenny Cox, I'm from Orem, UT. I'm a senior in Environmental Soil Science. And I like to read and do outdoorsy type stuff, ski and hike. BO: My name is Baxter Oliphant, I am a senior majoring in political science. I'm 24 years old. I'm from Arlington, VA, outside of DC, therefore I am into politics, I'm a news junkie. I like to read, write, and to study, I'm one of those losers. RM: My name is Russell Memory, I'm an exercise science major. I'm a senior. Like Baxter I'm also from VA. My hobbies include running and working out and I like to play the guitar. BY: Let me introduce myself, I'm Bryce Youngquist, I'm a business management marketing emphasis major. I'm from Minneapolis, MN. Welcome to the focus group. We want open ideas. We want to hear the positives and the negatives. We want to understand what make you tick. Let's get a discussion going and see what you have to say and go from there. Let us know your feelings. Tell us what's on your mind. Don't worry about hurting our feelings. Russell, how did you select your major? RM: I started out up at Ricks College my freshman year, I was a sports medicine major. I liked to work out. I didn't know quite what I wanted to do with that major. So I went on my mission, I decided to be a business major. I took some business classes, after a while I decided I couldn't stand the business classes. Then I bounced around from major to major and didn't know what I wanted to do as a career. I'm going to study what I like to do. And will see what I do for a profession later. Study what I'm interested in. We'll see how it works out in the end. But I'll study what I'm interested in. BY: Anyone can answer. Anything else? Is it all interests? How did you pick your major? BB: I think a lot of people pick their major by how much they're going to make later. I know deciding for me was affected by what the pay is later. What's the payoff for what I'm studying later. BO: I think everybody considers that, but I don't know if everyone makes a decision based on that. A lot of people do. I'm doing political science, which is useless, but I really like it. I know I'll have to go to graduate school. There are times I wished I was doing something that's a little more practical, something that I wouldn't have to go to graduate school. But I'm still I'm doing what I love. 146 JC: It was pretty hard for me. There are a lot of things I like to do, but cience intere t m th m t. And I took bunch of classes and that helped out. RM: I also was going to add. One of the reasons why I was reluctant to elect a major, b au e 1didn't want to limit myself in my profession, whatever I wanted to do in the future. But like Baxter aid, a bachelor's degree doesn't have to limit what you do later. Exercise science is not a big money maker by itself, you have to go to graduate school. Like what Baxter said, it's something I enjoy doing. BY: Now we kind of touched on this, what specific elements do you look for in a major, what' in tore later, job potential? BO: If I can do it, I didn't think of physics, I can't do physics. I picked the major I good do well at. Sometime I could get A's and do well and enjoy it at the same time. BY: Do you or others do you think choose a major even though the c1as e aren't that iting, but ba. d more on what the end result is? BO: I think some people are better than I at seeing the end result. They know they hav to tak hard, basic courses. I know some guys really into physics that do that, they don't njoy th ut they're willing to put up with the tough classes for the end re ult . They can ee p t that. BY: Jenny, taking your classes in your major, did that affect you? JC: It has so far. I've enjoyed the classes so far, they're not alway th mo t \tlO ju llik with any other major. I kind of like having the major small, then you ha e the maH clas ire. Th re'. lik I r 5 in my classes. BY: And political science classes ... ? BO: Once you get to the upper division classes they get down to 30 or o. BY: Now, how many times have you switched your major 0 far? BB: Oh, I don't know, maybe 5 or 6 times. JC: I was actually a music major for three years. I only witched m maj other majors. BO: I've switched my major officially three times. But in my plan pr babl a ul7 r tim . I started with international studies, then econ, some political cience, th n did ome ngli h tuf, lh n 1 went back to political science. And that's the degree that I'm ending up with. RM: I started out with sports medicine, then went business, and no I m e different majors, but in my mind I would take different clas es in a cordan them out. So I probably switched mentally 5 or 6 times. rei ith h k BY: Now Jenny, did you take different classes while you were a mu i m j r1 JC: Yeah, I took a lot of chemistry, some soil science, some biolo BY: Anything about professors, did that change anyone' major? 147 BO: Well, it never changed my major, but it reinforced my decision in a major. I got to know some professors, then got into some interesting classes, it got me to settle down. BY: Any professors ruined it for you? BO: I took an econ class and I didn't like the professor very much. I just didn't like how it was setup. Not the students, but just the environment, the attitude. It wasn't terrible, I didn't hate it. I realized it wasn't for me it wasn't where I wanted to be. RM: I think it wasn't the professor, it was just the atmosphere and the class. I considered accounting for a while. I enjoy accounting. We'd sit there in class, even the accounting majors would come in and would bag on their major. "Yeah, we have the most boring major out there ..." There wasn't a very enthusiastic feeling in the room. The teacher was great, I still like accounting. Maybe it was just the atmosphere that turned me off. BY: So, in general, would just being around the majors or being in the building, not that it would decide your major, but would that help inform you and help you decide? BB: I think being around the people who are taking those classes or those that are in their last semester and just being around them, seeing how they feel, see what they say. How do they feel now that they're almost done. What their experience was. BY: Do you like the Widstoe building? IC: It's kind of old, kind of junky, it's got a homely feel. BY: Ok, now let's go through your future career plans? BB: My future career plans are to graduate from BYU and then go get a masters in landscape architecture somewhere. Then work for myself or for another firm designing parks or golf courses, neighborhoods. stuff like that. BY: So your future plans really influenced your decision on a major? BB: Yeah, I thought I wanted to do landscape architecture. I talked to some people. And I talked to some professors. And I liked landscape architecture and I knew landscape management was a good way to prepare for a masters degree in landscape architecture. I like being outside, I grew up on a farm. You can run your own business or work for someone. IC: Future plans, it's kind of up in the air. No one really knows. I'd like to be a professor or work for the bureau of land management. BO: My plan is to go to law school. And possibly get a joint ID/PhD. But that's a lot of school. But I want to get involved with politics, public policy and legal issues, social debate. All those things somehow. RM: I would like to go into something health related. Podiatry, physician's assistant, physical therapist, something like that. I still haven't ruled out business, even though I still don't enjoy the classes. My roommate's dad was a zoology major and was in dental school, realized he didn't like, backed out and got 148 his MBA and now he works for circuit city. So he's actually a real big motivation to me 10 gel a "deadend" bachelor degree. Because there really are no "deadend" bachelor degrees. BY: Alright, this kind of leads to my next question, would you all agree you ju t need to get a bachelor degree? BO: It's important to do something you like. But also, you just can't pick anything. There are some majors that really are pointless, or are very hard to do something with. Like a music dance thealer major for example, I don't how much writing and reading they learn how to do. I've found it important; it's not necessarily the subjects I learn, but the skills I have learned. I've learned how to read and write well and to think for myself. And so that had to be in the major, so if I would have picked interpretive dance, I wouldn't have learned that. RM: On the other hand though, sometimes these graduate schools do like the "deadend" majors. They like variety and diversity. Because in law school, if everyone's political science, history, or Engli h majors. Then there's a stigma attached to the lawyers. They're looking for people who are accounting majors or business majors. I have some friends who are Chinese majors going to medical school. That's why they make you take the prerequisites to go to these graduate programs, to prove that you can learn what they are going to teach you. Within reason though. There are majors that maybe they truly are deadend, but basically what people are looking for is that you're able to learn and complete a task, regardless of what that task is. BO: But I've talked to some people that have taken the "deadend" majors and they fell like it' been a real disadvantage to them. Because then they get into the graduate program and find them elves behind the curve. I know someone that went to law school, after being an econ major, but he really !ruggled to keep up with the writing. That's why it's important to keep in mind the field you want 10 go into. You can't say, "I want learn calculus inside and out," then go to law school. It will help you of course, it' not a dead end, but you also need to keep in mind the skills you want 10 learn. RM: I think that's where a minor comes in hand. BY: Now, I want you guys to think of words or characteristics that make majors attractive? RM: Interesting. BB: I really think the paycheck plays a big role. JC: The doability. If you really stink at something, you probably shouldn't get into it. BY: So we have interesting, doability, paycheck... BO: I would say one of the things is "
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Permanent URL  http://hdl.lib.byu.edu/1877/1193 
Title  Environmental Soil Science: A Marketing Research Strategy 
BYU Creator 
Diamond, Steven Youngquist, Bryce Horna, Mariella 
Keywords  Environmental Soil Science; future; name; major selection 
Description/Abstract  Before we commenced the research process, three main objectives were set forth by MBS Consulting. The research objectives are as follows: Determine the potential future path of the Environmental Soil Science program. Determine the overall appeal of changing the name of the Environmental Soil Science program. Obtain an understanding of a student's mindset when selecting a major. MBS Consulting is comprised of Steven Diamond, Bryce Youngquist and Mariella Horna. 
Source  HBLL Call Number HD 8998.B75x 2006a 
Date Original  2006 
Language 
English eng en 
Publication Type  Reports 
Type  Text 
Format  application/pdf 
Owning Institution 
Brigham Young University 
BYU College 
Marriott School of Management 
Copyright Status  © 2006 Brigham Young University 
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Full Text  mbsConsulting Environmental Soil Science A Marketing Research Study Prepared By: Steven Diamond Bryce Youngquist Mariella Horna 4ARRIOTT CHOOL OF MA.NAGEMENT Table of Contents Preliminaries Acknowledgments 3 Letter of Completion 4 Executive Summary 5 Research Objectives 8 Methodology 10 Limitations 13 General Survey Findings 15 Environmental Soil Science Survey Findings 36 Conclusions & Recommendations Conclusions 71 Recommendations 71 Appendices Appendix A: Statistically Significant Cross Tabulations 75 Part 1  General Survey 76 Part 2  Environmental Soil Science Survey 118 Appendix B: Statically Insignificant Cross Tabulations 135 Appendix C: Transcript from Focus Group 145 Appendix D: Surveys 154 Part 1  GeneraL 155 Part 2  Environmental Soil Science Majors 157 Part 3  High School 160 Appendix E: Responses to OpenEnded Question 6 in the General Survey 162 Appendix F: Letter of Engagement. 169 2 Acknowledgments We would like to thank Richard Terry from the Plant and Animal Sciences Department for his assistance in the project. He was very responsive and accommodating throughout the process. We would like to thank him for providing us with this educational experience. Also, we would like to thank Dr. Geurts, Sheldon Nelson, Von Jolley, Bruce Webb, and Levi Jackson for their help. Dr. Geurts and Levi Jackson assisted us through the fine details of the project while Sheldon Nelson, Von Jolley, and Bruce Webb helped us in formulating our survey. We are pleased to present to you the marketing research report for the Plant and Animal Sciences Department. We appreciate the opportunity we were given to work you during the life of the research. We have strived to meet your expectations as outlined in the letter of engagement and we are confident that the results will be valuable to the future direction of the Environmental Soil Science program. Letter of Completion March 23, 2006 Richard Terry Brigham Young University Plant and Animal Sciences 259 WIDB Provo, UT 84602 Dear Mr. Terry, avu MAltRlOTT SCHOOL Of MANAGEMENT Contained in this report are the following main sections: .:. Executive Summary .:. Research Objectives .:. Research Methodology .:. Research Limitations .:. Findings .:. Conclusion & Recommendations .:. Appendices We enjoyed the educational experience this project afforded us. Thank you for the time and effort that you have imparted throughout this project. We wish you the best in your future endeavors. Sincerely, Steven Diamond MarielJa Hoena Bryce Youngquist 4 Executive Summary 5 Executive Summary Research Objectives Before we commenced the research process, three main objectives were set forth by mbsConsulting. The research objectives are as follows: .:. Determine the potential future path of the Environmental Soil Science program .:. Determine the overall appeal of changing the name of the Environmental Soil Science program .:. Obtain an understanding of a student's mindset when selecting a major Conclusions Based on our flndings, we came to the following conclusions: Out of 349 respondents, 211 (60.5%) agreed that the name Environmental Science is more appealing than the current name of Environmental Soil Science. Also this was a concern brought up in the focus group, implying that it was too narrow of a study (see Appendix C). Almost 60% of the students responded by saying they have not thoroughly searched all the majors offered at BYU. In fact, 50% of the respondents only "looked" into 1 to 2 different majors before selecting the one they are in now. On a 7point scale (1 =Completely Agree, 7 =Completely Disagree) 75% of the respondents completely agreed that future career plans influence the selection of their major. This goes handinhand with the 60% response that job availability after graduation is very important on a separate 7point scale (1= Very Important, 7 = Not at all). In the general survey, there was great importance placed on the variety of classes offered with 55% of the respondents assigning a 1 or 2 on the 7point scale (1 =Very Important, 7 = Not at all). Also in the ESS survey, 50% of the students expressed interest in more Plant and Animal Sciences courses offered during spring and summer terms. 6 Recommendations Based on our findings and conclusions, we recommend the following actions to be taken: • We recommend that the Plant and Animal Sciences department changes the name of the Environmental Soil Science program to Environmental Science. We feel that by making this simple change, the program will be able to increase its enrollment by portraying a broader scope of study. • Considering that majority of BYU students do not thoroughly search all of the majors offered, the Plant and Animal Sciences department should increase awareness of freshman and sophomores at BYU. The department needs to take a proactive stance towards attracting more underclassmen as they search majors. We also recommend analyzing the results of the High School survey. • As the Plant and Animal Sciences department increases awareness they should also place emphasis on informing students about the future career possibilities. Our research shows that the students placed the most importance on job availability following graduation. We recommend that the Plant and Animal Sciences department look into the methods used by other departments on campus for internship opportunities and job recruiting. • We recommend that the department look into the possibilities of offering more spring and summer courses. In addition, half of the responding Environmental Soil Science students said they thought of leaving the program. By having a wider array of time offerings and variety of classes offered, this may help to increase the retention and overall program numbers in the end. 7 Objectives 8 Research Objectives Before we commenced the research process, three main objectives were set forth by mbsConsulting. The research objectives are as follows: .:. Determine the potential future path of the Environmental Soil Science program .:. Determine the overall appeal of changing the name of the Environmental Soil Science program .:. Obtain an understanding of a student's mindset when selecting a major Exploratory Research The exploratory research for this project was performed to determine the best methods for completing our project in the most accurate manner possible. We conducted an indepth focus group to gain more insight into the mindset of students when determining their area of study. Focus Group As part of our exploratory research, we conducted a focus group with some BYU students: Jenny Cox, Russell Memory, Baxter Oliphant, and Blaine Bateman. Our primary objective was to determine which questions would be most appropriate for the survey. Initial responses from the students were mixed. It was a very productive discussion. An overall consensus of selecting a major was what the student's personal interest and what they want to do for a career. The students seemed to all agree that the name of the Environmental Soil Science major should be changed. A transcript of the focus group discussion is included in the appendices of this report. Primary Research Our primary research consisted of survey design, determjning the sample size, and statistical testing. Surveys Our primary method of research was conducted though two separate surveys. Our surveys were distributed using SurveyZ, an online program used to send the surveys and compile the results. One survey was sent to the Marriott School students and the Biology and Agriculture students. Our results on this survey are based on the approximately 345 respondents. The second survey was given to the 15 students currently studying Environmental Soil Science. Our results on this survey are based on 10 respondents. From the results of these two surveys, we were able to compile and analyze the results used in this study. In addition to these two surveys, our original intention was to conduct a third survey of hjgh school students. Due to time constraints, we were unable to analyze the results of this survey. We did, however, distribute it, and the responses will be included in supplemental material along with this study. Survey Design As noted above, a focus group was used to aid in the creation of our surveys. From the responses of our focus group, we compiled an initial preliminary survey. This survey was used to determine the clarity and usefulness of the initial questions. 11 Our survey was designed to analyze data from the different groups we sampled. The questions included respondents' general attitudes toward their current and past areas of study, toward potential name changes to the Environmental Soil Science program, and demographics. The survey's design and questions were reviewed by Dr. Geurts, Richard Terry and Levi Jackson. The final surveys, including the unanalyzed high school survey, are contained in the appendices of this report. Determining Sample Size It is important to have a correct sample size in order to get the most accurate results. We were able to obtain sample email addresses from approximately 1500 students in the Marriott School of Business and another 698 emails from the College of Biology and Agriculture. The 698 emails from the College of Biology and Agriculture were limited to freshmen and sophomores. In the case of this study, the sample size (the number of emails) was provided by the client. In addition to these two sample groups, we also conducted another survey of those students studying Environmental Soil Science. There are currently 15 total students in the program, 13 of which are in the undergraduate program. Statistical Testing After inputting data from the results of our surveys, we used SPSS software as our primary method for statistical testing. SPSS is software that allows one to analyze data using Chisquare, correlation, regression, as well as through other means. ChiSquare Analysis The principal method of analysis for this project was Chisquare analysis through SPSS software. Chisquare analysis is a method to determine the association between two variables. The output shows whether the association is statistically significant or not. If a level of significance is given at .1, we would observe that there is a 10% chance that the results are not true, or in other words, a 90% chance that they are true. For this project, we used a 90% confidence level. The Chisquared analysis results are contained in the appendices of this report. 12 Limitations 13 Limitations of Our Research While we endeavored to conduct this project with as little error as possible, limitations still exist to this study. The primary limitations were nonsampling error and sampling error. NonSampling Error Nonsampling error signifies a human error in research. While we endeavored to conduct the research with as few errors as possible, a few errors unfortunately occurred. Representative Error In the general survey distributed to the students in the Marriott School and the College of Biology and Agriculture, the sample provided to us for the College of Biology and Agriculture included only freshmen and sophomores. No juniors or seniors were represented. Also, this survey was not distributed to representatives of all the different colleges and programs at BYU. NonResponse/Response Error Nonresponse error is the omission of an answer by the respondent, while response error is an untruthful answer by the respondent. We found a small number of surveys in which nonresponse and/or response error occurred. We did not use this data in our analysis. Sampling Error Sampling error signifies that a sample may not be representative due to random chance. For this project we used a 90% confidence level, meaning that there is a 10% chance of sampling error. From the data collected, 210 were males and 137 were females. This could be due to the fact that the members in the two groups sampled have a higher propensity to be male than female due to the professions found therein. 1 General Survey Findings 15 Question 1a: Please rank the following factors in selecting your major. (1mo~t influential to 5least influential)  Personal Interest Personal Interest 350 .'.". 300 cCP 250 'C C 200 0'c". 150 CP a:: 100 0 50 == 0 1 2 3 4 5 1 =Most Influential, 5 =Least Influential Note: Overall this was the highest ranked attribute out of the five. Out of the 384 responses, 74.7% agreed that personal interest has the most influence in selecting a major. 16 Question 1b: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Popularity of major Popularity of Major 160 .,...............           ..............,.,..",, 140 +~ ~ 120 +                      ~o 100+ c. 80  f            U) ~ 60  1       . . . : .      '0 40 + == 20l=.. o 1 2 3 4 5 1 =Most Influential, 5 =Least Influential Note: The popularity of the major seemed to be the least important factor overall with 36.5% choosing it as "least influential." However, we found statistical significance between popularity and gender (chisquared value of .029), and also between popularity and department (chisquared value of .000). See graphs below. 17 q1b 50.0% 45.0% 40.0% 35.0% 30.0% • Male 25.0% • Ferrale 20.0% 15.0% 10.0% 5.0% 0.0% 1 2 3 4 5 q1b 540.0%0"[ '0%tjllllll • Marriott • BioAg 2 3 4 5 18 Question 1c: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Family influences Family Influences 180 ~~~~~~, 160 + 1rI            l ~ 140 11 c~ 120 11 g 100 1 1 Q, ~ 80 k........=o1 a: '0 60 '**' 40 +1 20 +=11=1 11 01L_L,LL,LJL.,..lJL.,..lJL....j 2 3 4 5 1 = Most Influential, 5 = Least Influential Question 1d: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Scholarship Availability Scholarship Availability 2 3 4 5 1~o ,.....,:r..,.....,..,..,.,.....,......."..,..".......,..",..,..,.,..,~,.,.,.,..,..,.~....,.....=..,....,....,....,....,......"",.,, 1/1 160 +.,........,,::,=': C 140 +=' 8 120 1...".== g 100 I~';~ 80 I~~"aG: l 60 1:'' '0 40 1 '* 20 o 1 =Most Influential, 5 =Least Influential Note: Perhaps one of the most interesting results was to find that scholarship availability was one of the least influential factors with 44.3% ranking it as the least influential. As shown in the graph below. underclassman placed a bit more importance on this factor than did the upperclassman (chisquared value of .001). q1d 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 2 3 4 5 • FreshJSoph. • ~perclass 20 Question 1e: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Potential Income Potential Income 250 III 200 C Gl 'C 150 c0 Co III aG:l 100 '0 '*I: 50 0 2 3 4 5 1 =Most Influential, 5 = Least Influential Note: Potential income was the second most influential factor when choosing a major. In the graph below, male 'respondents are shown to place more importance on this factor than female respondents, perhaps due to future family considerations (chisquared value of .007). q1e 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 2 3 4 5 ~ ~ 21 Questions 2a _ 2i: Rate the importance you place on each of the following characteristics of a major (1 =Very Important, 7 = Not at all). 2a General Atmosphere/Environment 2 3 4 5 6 7 140 ~:"_.,..., CUl 120 1 ~ 100 8. 80 l3 60 a: 40 ',0., 20 o 1 = Very Important, 7 = Not at all 2b Academic Qualifications of Professors 2 34 5 6 7 1 =Very Important, 7 = Not at all 160 ........           . , .  ,  . .     . " ~ 140 ~ 120 1 c 100 &. 80 Ul ~ 60 '0 40 ,., 20 o 22 2c .Availability of Professors for Consultations 120 .I.I. 100 c: CI) 'C 80 c: 0c. 60 III CI) a: 40 0 20 '*" 0 2 3 1 =Very Important, 7 = Not at all 2d Starting Base Salary After Graduation 2 3 .4 5 6 7 1 = Very Important, 7 = Not at all 140 ..,......~.,.,.,....,....,...",._ _....,." ,..",..,.,,.....,..,.,......,..........,,...,.,""7"""~,...., S 120 l Iii 100+ i : '5 40 '#: 20 o 23 2e National Reputation of Program 2 3 140 120 III 100 c~c 80 i CII 60 a: '0 40 '# 29 0 1 =Very Important, 7 = Not at all 2f Variety of Class Offerings 2 3456 7 1 = Very Important, 7 =Not at all 24 2h 2 34 5 6 7 1 :: Very ,mportant,7 :: Not at aU 2 3 4 5 6 7 1 :: Very Important, 7:: Not at all Times c'asses are Offered 250 .1C!l 200 Q) "c 150 0 Q. l/I 100. Q) ct '0 50 =*I: 0 Job Availability Following Graduation 2g 90 I'"~...,...,..".~.,...,.......,..,....~.......,..., 80 r .1!l 70+ ~ 60 t' 8. 50+ ~ 40 30 '0~ 20 10 o 120 111 100 c CIl 80 'Cc 0D. 60 111 ! 40 0 :It 20 0 2i Preparation for Graduate School 2 345 6 7 1 =Very Important, 7 = Not at all No~e: The characteristics upon which the most importance was placed were job availability and natIOnal reputation" of program. The next most important characteristics were preparation for graduate school, general atmosphere/environment, and starting base salary. 26 Questions 3a" 3e: Thinking of different majors, rate your agreement with the following statements based on your experience (1 = Completely Agree, 7 = Completely Disagree) 3a " A Bad Teacher Will Influence Your Selection of Major 120 Ul 100 Ct 80 60 Ul Q) a: 40 15 =I*: 20 0 1 2 3 "4 5 6 7 1 = Completely Agree, 7 = Completely Disagree 3b " A Good Teacher Will Influence Your Selection of Major 180 .0.. 160 C 140 Q) 1cJ 120 0 100 Q. 0 80 .! 60 '0 40 * 20 0 1 2 3 4 5 6 7 1 =Completely Agree, 7 =Completely Disagree 27 3c . Would You Say You·ve Thoroughly Searched All the Majors Offered at BYU 90 80 70 60 50 40 30 '0:tI: 20 10 0 1 2 3 4 5 6 7 1 = Completely Agree, 7 =Completely Disagree Notes: 41 % of the respondents ranked this question with a 6 and a 7 on a 7point scale, saying that they have not thoroughly searched all the majors offered at BYU. Compare these results with th'ose from qu.estion 4 where 50% of respondents "looked" into 12 different majors. 3d The Nam~ of a ProgramIMajor Can Influence Its Popularity 120 J'I!:i 100 C1) "c: 80 8. 60 i 40 0 20 :tI: 0 1 2 3 4 5 6 7 1 = Completely Agree, 7 = Completely Disagree 28 3e Your Future Career Plans Influence Your Selection of a Major 300 In 250 ,l: ~ 200 [ 150 In aG:l 100 '0 50 * 0 1 2 3 4 5 6 7 1,= Completely Agree, 7 =Completely Disagree Note: This was the most highlyranked factor in influencing major selection, with 75% of the respondents completely agreeing. 29 Question 4: How many times have you II lookedII into different majors (Taken different classes to see if you want to go with a certain major)? IILooked ll into Different Majors 200 180 .0.. 160 c(1) 140 .'tJ C 120 0 100 C. 0 80 (1) .a..:. 60 0 40 ~ 20 0 0 12 34 56 7+ Times Note: There were some significant chisquared values we found as we tested this question with questions: q2a, q3a, and q3c. 30 Question 5: How many times have you "officially" changed your major on the books? (Changed it with the school records) "Officially" Changed Your Major on the Books 180 th 160 ~ 140 'cC 120 8 100 m 80 ~ 60 '0 40 #: 20 . 0 0 12 34 56 7+ Times 31 Question 7: Which program/major name has more appeal: Which ProgramlMajor Name has More Appeal 250 I/) c 200 CI.l 'Cc 150 0cen. CI.l 100 a: 0 50 "*' 0 Em,;ronmental Soil Science Em,;ronmental Science Natural Resource Conservation No Difference Note: A strong response of 60.5% students agreed that the name Environmental Science is more appealing than the others. 32 Question 8: What department/school are you in? What DepartmentlSchool Are You In? 180 160 t/) 140 c 120 CD "0 C 100 0 Q. ~ 80  60 0 # 40 20 0 1 2 3 4 5 6 7 8 9 Legend 1  School of Accountancy & ISys 2  Marriott School of Management 3  Plant and Animal Sciences 4  Integrative Biology 5 Biology . 6 PD Bio 7  Microbiology and Molecular Biology 8  Nutrition, Dietetics, and Food Science 9  Other 33 Question 9: Gender .tI) 200 + C II) 'g 150 + 8 tI) aI:I:) 100 +,;: '0 == 50  l      0+ Male Gender Female 34 Question 10: What is your age? 2325 Question 11: Year in school Year In School 140 120 I/) c 100 CI) 'C C 80 0c. I/) 60 CI) a: 0 40 # 20 0 Freshman Sophomore Junior 2628 Senior 29+ Graduate 35 Environmental Soil Science Survey Findings 36 Question 1a: Please rank the followin'g factors in selecting your major. (1most influential to 5least influential)  Personal Interest Personal Interest 12 . .I.I.) 10 I: Q) 8 "C I: c0 . 6 lI) aQ::) 4 0 2 :11: 0 1 2 3 4 5 1 =Most Influential, 5 =Least Influential Note: Similar to the general survey, this was the highest ranked attribute out of the five. 37 Question 1b: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Popularity of major Popularity of Major 9............... 8+....".:.. .!!7+==': 0C: (1)6+''.:. 'C S5+:~ ~4+ £3+...:.. ~2+'=.:..:.:"=...::":.....:: # 1o 1 2 3 4 5 1 =Most Influential, 5 =Least Influential 38 Question 1c: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Family influences Family Influences 2 3 4 5 1 =Most Influential, 5 =Least Influential 39 Question 1d: Please rank the following factors in selecting your major. (1mo~t influential to 5least influential)  Scholarship Availability Scholarship Availability 7 6 en C 5 Q) g 4 0 ~3 Q) ~ 2 0 *1 0 1 2 3 4 5 1 = Most Influential, 5 = Least Influential Note: As with the general survey this factor was ranked as less influential when selecting a major. 40 Que~tion 1e: Please rank the following factors in selecting your major. (1most influential to 5least influential)  Potential Income Potential Income 1 =Most Influential, 5 =Least Influential 6 5 en i 4 "0 co 3 Co en CI) a: 2 0 '*I: 1 0 1 2 3 4 5 L Note: While in the general survey respondents considered potential income as one of the higher influential factors in selecting a major, ESS students responded in ranking it as a less influential factor. 41 ,'" .........~'.. ',,'1' , ~. l~·~i~:~~~:~:~ ~,: ::,~~~t~'~ Question 2: t:lave you ever been in a major other than Environmental Soil Science at BYU? Have You Ever Been In a Major Other Than ESS ~ 6.5 ,~.........:~.............., C CD 6 t,.,."""" ~"C o C 5.5 tJ,: =#:0 ~ 5 t'fl aCD: 4.5 t.L.:o.~ 1 Yes = 1, No = 2 2 42 Question 3: What was your previous major, if applicable? Responses: Business, Civil Engineering, Horticulture Management Communications Geology Geology • Music performance Wildlife and Range Question 4: If you answered yes to the previous question, what led you to switch majors? Responses: I ~idn't want to have ajob in that major, I really like science. I hke the range of courses that are required in this major and I like the fact that few people choose this major. • It was the mentored research and the diversity of the education. TLihkeesdmthailsl soinzee boefttthere.major and I felt like I got to know the professors and the other students in the major quickly. There were also many scholarships, travel, and work opportunities available during my bachelor work. 43 'uestion 5: What do you like about the Environmental Soil Science 'rogram? lesponses: The variety of courses and the research opportunities The faculty are friendly, helpful, and accessible. The research, professors, and the freedom of choosing classes great instructors, hands on You get to know the faculty members well and there are lots of opportunities for undergraduate research. The professors care about the students. I like the small class sizes, the interest the teachers take in the students, and the friendliness the students. I enjoy learning about earth's processes and all of the professor's I have had in this program are very supportive and approachable. The range of subjects is innumerable and I enjoy learning about many different aspects of environmental issues. I like the program because I like agriculture and farming. ] get to study and learn about what I love. I like the program because I enjoy agriculture. In ESS I get to understand and learn about the heart of what I love. I like how small it is and how I feel at home with all the people in the major. Also, it allowed me to take many electives giving me a broad and enjoyable learning experience. I got to take everything from geology and landscaping classes to land use law 44 Question 6: What do you dislike about the Environmental Soil Science Program? Responses: the low availability of courses It would be nice to have a slightly larger enrollment in some of the classes. The name of the major. It isn't flattering for most and I believe that is a major draw back for people looking for an environment related major physics requirement There aren't very many students in the program so it makes it hard to have a club or very many field trips how small it is Nothing, right now! The lack of direction in terms of employment, there should be emphasis on getting students into law school, education or a better focus on job placement post undergraduate education. Ijust started so I can't say much in dislike. I would hope that some of the chern. classes that are now required that do not have much to do with ESS could be dropped from the required classes. I just started the program so I don't have too many complaints yet. I do wish I did not have to take so many chern. classes. I didn't like how some classes seemed to be agriculturally based. I was initially looking for more environmental science where I would study air, water, and energy, etc. There was none of that. I don't feel prepared for any specific job either. 45 ~~ .' ':. , "~ ~.~.:.. _..~• • _~J .~. ' _ Question 7: Have you ever thought of leaving the ESS program for another major at BYU? Have You Ever Thought of Leaving the ESS Program S 6.5 ,.......,... c: ~ 6+'::~., :oc:t 5.5 CD a: 5 +__ o * 4.5 + 1 1 =Yes, 2 =No 2 Question 8: Why did you consider leaving the program? Responses: accounting or info system, something more lucrative and in demand I am minoring in modem dance and I considered changing it to my major, but it would take way too much time and I love science. I really liked my marketing class. I thought it was really interesting. However, I was almost done when I took it and didn't want to spend my whole life in school. Not sure if that's what I want to do. There was another program in the InBio department that I thought might fit my interests better, but I decided to stay because I love the PAS department. 46 Question 9a: Rate the importance of the following in regards to your major: The academic qualifications of the Professors Academic Qualifications of the Professors 6 U) ccu: 5 "cC 4 ~3 U) aG:) 2 o 1 ~ 0 1 2 3 4 5 6 7 Very Important = 1, Very Unimportant = 7 47 Question 9b: Rate the importance of the following in regards to your major: Availability of professors for consultations Availability of Professors for Consultations 8,..,................_...., 7 Jc!! 6 c8 5 8.4 rn ~ 3 '0 2 =I*: 1o +~.L~ 1 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 48 Question 9c: Rate the importance of the following in regards to your major: National reputation of the program National Reputation of Program 5 Jc!! 4 C1) "'C C 3 0 Q. tn C1) 2 a: 0 1 # 0 1 2 3 4 5 6 7 Ve ry Important = 1, Ve ry Unimportant = 7 49 Question 9d: Rate the importance of the following in regards to your major: General Atmospherel Environment of the Plant and Animal Sciences Department General AtmospherelEnvironment of the PAS Department 5 .I.n. s:::: 4 (1) "0 S 3 c.. ~ 2 II: '0 1 '"' o    f r 1 T1 1 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 50 Question ge: Rate the importance of the following in regards to your major: Job availability following graduation Job Availability Following Graduation 6., E 5 +      : :                   1 CD ~ 4 +f,""'·~1___1 8. 3 tIJ,:;.i'A~I Ie!n 2 o 1 :tI: 1 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 51 Question 9g: Rate the importance of the following in regards to your major: Variety of Environmental Science core courses Variety of Environmental Science Core Courses 6 C1l C 5 Q) g 4 &.3 C1l ~ 2 o 1 '#; o       I I' I I I 1 2 3 4 5 6 7 Very Important = 1, Very Unimportant = 7 53 Question 9h: Rate the importance of the following in regards to your major: The inclusion of basic science courses in the Environmental Soil Science curriculum Inclusion of Basic Science Courses in the ESS Curriculum f/) 4 r::::: ~ 3 r::::: o Q. 2 f/) (1) a:: 1 '0 :f*: 0 1 , e I I 1 2 3 4 5 6 7 Very Important = 1, Very Unimportant =7 54 Question 9i: Rate the importance of the following in regards to your major: The number of faculty and students in the ESS program Number of Faculty and Students in the ESS Program 2 3 4 5 6 7 Very Important =1, Very Unimportant =7 1 J!!4,~=:,..,.,,,._~. c~ 3  1   '      Co 0.2 l/) Q) a: 1 '0 * 0 +J=L.,. 55 IQ a Importance oftha fallowing In regards to your m_J· Co m'unlcatlon amtc»na _tu and faculty n the program Com~Imunlcltkl.n Among Students ,lind Faculty Itt Pro ram 56 a 4 5 6 7 ,on n1 =1, Very Unimportant::;; 1 o 1 ~ Importllnce ,ot the following In regards to your __ ' hi I Opportunities 57 2 3 ,4 5 6 7 ry I portent = 1, Very Unlrnportant = 7 'Question 91: Rate 'the ImportBnce of the following in regards to your major: Reid trips with students and facu1lty Field Trip with Students and Faculty 5 ~.. 4 "C&3 t~ ~ 2 o 1 o 2 3 4 5 6 7 Very Important = 1, Very Unimportant = 7 58 QuesUon 9m: Rate the Importance of the following in regards to your major: PreparaUon for postgraduate education PreparatJon for Postgraduate Education 7 J! 6 ~ 'C c: 4 8. I 3 a: 2 0 1 0 , 2 3 4 5 '6 7 Very Impo,rtant = 1, Very Unimportant = 7 59 Question lOa: On a scale of 17, how satisfied are you with the avallablli1y ot: eta' 8 offered by the Plant and Animal Sciences Department S,atisfled with Classes Offered by the PAS Department !J 5 1~~__............, I 4 I~t o ":  0 1 2 345 6 7 Very Satisfied =1, Very Dissatisfied =7 60 61 \AlH~_ B Ie Science Classes by I r' 0 p rtments on Campus 234 5 6 7 ry 58tJ18fMtd =1 Very Dlssat'sfled =7 of 1·7, how tlsfled are you with the __ c 8C'lanc~ C 8 offered by other departments on 1 6,::;~~~~ 5 "1 Que :1lon 11: What semesterslterms should more Plant and Animal Science cia be O'ffered? SemestersTerms More PAS Classes Should Be Offered CD 6 y:, i 5 t 'ca 4 &. 3 a: 2, o o F Wi fer Spring Surrrrer ~need ot : ,\Ill u tJ II r rnBY ,thl" t lJ hI. ttUtI ~ n .In :umm~r I"'nns w re used for intern hip. away h h 1.:\, ttwt ttl si l ali ~nd I s during lhis time. 62 63 7 7 6 6 5 5 .. .. •• Be I nl 1, ,Poor 7 ......nt '. Poor 7 ~ ch ng Faculty _ 101, owl M.,ntored A arch Opportunities TeachI QM 64 4 5 6 7 •• 3 etc...nt.: 1, Poor = 7 ComlllJnIClltkHl1 RANlRen Students VVlthln the Program 11C81t1CK1 Ibe1WIMIn a1hK1.,,1t8 within th Environmental Soil 4 5 6 7 • I:ICc• •nt _ 1, Poor 7 COIrmlunlC8tJon Betweef" Student and Faculty E Program ~mmlunliC8t:'on DetWtMm ••,""""'I",t and faculty within the SCienc:e DlroG.lIlm UUlt8tICHI 12 : RaI~· I d cully of the Environmental UlfrICUr_1V of tESS Major o o "l~__,"""!,,, 4 5 6 7 Dlmicun~. ", Not DIffIcult == 7 66 chllnalng ~ nem. of the program Selene. En ronmentBl SCience N8I1r'1 of the ESS Progr.m Wallft .ft1a,... lI,nM~ Students 5 7 6 7 that changing the low;arm attl'lictin more tud nts. This n raj ~urv y. 67 68 and . ultuJ1e or  iii m y u ant M\,iro oW i U you to 8 student ? • • • 69  001 Graduate 7 11 Conclusions Based on our findings, we came to the following conclusions: • Out of 349 respondents, 211 (60.5%) agreed that the name Environmental Science is more appealing than the current name of Environmental Soil Science. • Almost 60% of the students responded by saying they have not thoroughly searched all the majors offered at BYU. In fact, 50% of the respondents only "looked" into I to 2 different majors before selecting the one they are in now. • On a 7point scale (l =Completely Agree, 7 =Completely Disagree) 75% ofthe re pondents completely agreed that future career plans influence the selection of their major. This goes handinhand with the 60% response that job availability after graduation is very important on a separate 7point scale (1= Very Important, 7 = Not at all). • In the general survey, there was great importance placed on the variety of classes offered with 55% of the respondents assigning a 1 or 2 on the 7point scale (I =Very Important, 7 = ot at all). Also in the ESS survey, 50% of the students expressed interest in more Plant and Animal Sciences courses offered during spring and summer terms. Recommendations Based on our findings and conclusions, we recommend the following actions to be taken: • Change the name of the Environmental Soil Science program to Environmental Science. We feel that by making this simple change, the program will be able to increase its enrollment by portraying a broader scope of study. • Con idering that majority of BYU students do not thoroughly search all of the majors offered, the Plant and Animal Sciences department should increase awareness of fre hman and sophomores at BYU. Perhaps this could be achieved through the New Student Orientation. We also recommend analyzing the results of the High School urvey. • As the Plant and Animal Sciences department increases awareness they should also place emphasis on informing students about the future career possibilities. Our research shows that the students placed the most importance on job availability following graduation. We recommend that the PAS department look into the methods used by other departments on campus for internship and job recruiting. 72 • We recommend that the department look into the possibilities of offering more spring and summer courses. Half of the responding ESS students said they thought of leaving their major. By having a wider array of time offerings and variety of classes offered, this may help to increase the retention and overall program numbers in the end. 73 Appendices 7 Appendix A Statistically Signi~icant Cross Tabulations Part 1: General Survey Part 2: Environmental Soil Science Survey 75 Part 1: General Survey Questions Q 1) Please rank the following factors in selecting your major. (lmost influential to 5least influential) Qla  Personal Interest Qlb  Popularity of major Qlc  Family influences Qld  Scholarship Availability Q Ie  Potential Income Q2) Rate the importance you place on each of the following characteristics of a major: Q2a  General atmosphere/environment Q2b  The academic qualifications of the professors Q2c  Availability of professors for consultations Q2d  Starting base salary after graduation Q2e  National reputation of the program Q2f  Variety of class offerings Q2g  Times classes offered Q2h  Job availability following graduation Q2i  Preparation for graduate school Q3) Thinking of different majors, rate your agreement with the following statements based on your experience: Q3a  A bad teacher will influence your selection of major Q3b  A good teacher will influence your selection of major Q3c  Would you say you've thoroughly searched all the majors offered at BYU Q3d  The name of a program/major can influence its popularity Q3e  Your future career plans influence your selection of a major Q4) How many times have you "looked" into different majors (taken different classes to see if you want to go with a certain major)? Q5) How many times have you "officially" changed your major on the books? (changed it with the school records) Q6) How could your current major improve, if needed? Q7) Which program/major name has more appeal: Environmental Soil Science Environmental Science Natural Resource Conservation No difference Q8) What department/school are you in? Q9) Gender: Q10) What is your age? Q11) Year in school 76 Year In School (q11) * q1b ChiSquare Tests a 6 cells (24.0%) have expected count less than 5. The minimum expected count is 1.47. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 49.049(a) 16 .000 Likelihood Ratio 50.235 16 .000 LinearbyLinear Association 1.717 1 .190 N of Valid Cases 347 . . Year In School (q11) * q1d ChiSquare Tests a 8 cells (32.0%) have expected count less than 5. The minimum expected count is 1.96. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 41.240(a) 16 .001 Likelihood Ratio 43.115 16 .000 LinearbyLinear 12.459 1 .000 Association N of Valid Cases 347 .. Year In School (q11) * q2b ChiSquare Tests a 17 cells (48.6%) have expected count less than 5. The minimum expected count is .29. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 36.731 (a) 24 .047 Likelihood Ratio 37.406 24 .040 LinearbyLinear .005 1 .941 Association N of Valid Cases 347 . . Year In School (q11) * q2d ChiSquare Tests a 17 cells (48.6%) have expected count less than 5. The minimum expected count is .29. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 62.456(a) 24 .000 Likelihood Ratio 54.787 24 .000 LinearbyLinear 10.072 1 .002 Association N of Valid Cases 346 . . 77 Year In School (q11) * q2e ChiSquare Tests a 18 cells (51.4%) have expected count less than 5. The minimum expected count is .29. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 66.600(a) 24 .000 Likelihood Ratio 63.039 24 .000 LinearbyLinear Association 14.426 1 .000 N of Valid Cases 347 . . Year In School (q11) * q2h ChiSquare Tests Asymp. Sig. Value df (2sided\ Pearson ChiSquare 66.602(a) 24 .000 Likelihood Ratio 67.159 24 .000 LinearbyLinear 7.115 1 .008 Association N of Valid Cases 346 a 23 cells (65.7%) have expected count less than 5. The minimum expected count is .39. Year In School (q11) * q5 ChiSquare Tests Asymp. Sig. Value df 12sided\ Pearson ChiSquare 14.085(a) 8 .080 Likelihood Ratio 16.212 8 .039 LinearbyLinear 4.481 1 .034 Association N of Valid Cases 347 a 3 cells (20.0%) have expected count less than 5. The minimum expected count is 2.16. Year In School (q11) * Department (q8) ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 343.587(a) 32 .000 Likelihood Ratio 344.857 32 .000 LinearbyLinear 147.488 1 .000 Association N of Valid Cases 347 a 32 cells (71.1 %) have expected count less than 5. The minimum expected count is .59. Age (q10) * q1a ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 24.735(a) 16 .075 Likelihood Ratio 31.391 16 .012 LinearbyLinear Association .253 1 .615 N of Valid Cases 347 a 14 cells (56.0%) have expected count less than 5. The minimum expected count is .14. Age (q10) * q1 b ChiSquare Tests a 10 cells (40.0%) have expected count less than 5. The minimum expected count is .26. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 54.165(a) 16 .000 Likelihood Ratio 57.128 16 .000 LinearbyLinear .598 1 .439 Association N of Valid Cases 347 . . Age (q10) * q1d ChiSquare Tests a 9 cells (36.0%) have expected count less than 5. The minimum expected count is .35. Asymp. Sig. Value df (2sided) Pearson ChiSquare 38.871 (a) 16 .001 Likelihood Ratio 37.222 16 .002 LinearbyLinear 10.452 1 .001 Association N of Valid Cases 347 .. Age (q10) * q1e ChiSquare Tests a 12 cells (48.0%) have expected count less than 5. The minimum expected count is .17. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 33.031 (a) 16 .007 Likelihood Ratio 40.388 16 .001 LinearbyLinear 6.562 1 .010 Association N of Valid Cases 347 . . 79 Age (q10) * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 40.163(a) 24 .021 Likelihood Ratio 35.784 24 .058 LinearbyLinear 8.629 1 Association .003 N of Valid Cases 346 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .07. Age (q10) * q2b ChiSquare Tests Asymp. Sig. Value df (2sided)" Pearson ChiSquare 35.650(a) 24 .059 Likelihood Ratio 27.613 24 .277 LinearbyLinear 1.800 1 .180 Association N of Valid Cases 347 a 20 cells (57.1%) have expected count less than 5. The minimum expected count is .05. Age (q10) * q2d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.541 (a) 24 .000 Likelihood Ratio 61.403 24 .000 LinearbyLinear 12.993 1 .000 Association N of Valid Cases 346 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .05. Age (q10) * q2e ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 45.752(a) 24 .005 Likelihood Ratio 45.225 24 .005 LinearbyLinear 14.199 1 .000 Association N of Valid Cases 347 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .05 Age (q10) * q2h ChiSquare Tests a 24 cells (68.6%) have expected count less than 5. The minimum expected count is .07. Asymp. Sig. Value df (2sided\ Pearson ChiSquare 65.644(a) 24 .000 Likelihood Ratio 54.088 24 .000 LinearbyLinear Association 5.866 1 .015 N of Valid Cases 346 . . Age (q10) * q5 ChiSquare Tests a 5 cells (33.3%) have expected count less than 5. The minimum expected count is .38. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 20.838(a) 8 .008 Likelihood Ratio 21.433 8 .006 LinearbyLinear 6.318 1 .012 Association N of Valid Cases 347 . . Age (q1 0) * Department (q8) ChiSquare Tests a 32 cells (71.1 %) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value df (2sided) Pearson ChiSquare 216.713(a) 32 .000 Likelihood Ratio 231.101 32 .000 LinearbyLinear 94.227 1 .000 Association N of Valid Cases 347 . . Age (q10) * Gender (q9) ChiSquare Tests a 2 cells (20.0%) have expected count less than 5. The minimum expected count is 2.37. Asymp. Sig. Value df l2sided) Pearson ChiSquare 109.770(a) 4 .000 Likelihood Ratio 118.885 4 .000 LinearbyLinear 97.015 1 .000 Association N of Valid Cases 347 . . 81 Age (q10) * Year In School (q11) ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 329.255(a) 16 .000 Likelihood Ratio 329.077 16 .000 LinearbyLinear Association 192.990 1 .000 N of Valid Cases 347 a 8 cells (32.0%) have expected count less than 5. The minimum expected count is .59. Gender (q9) * q1a ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 8.020(a) 4 .091 Likelihood Ratio 8.580 4 .073 LinearbyLinear 2.529 1 .112 Association N of Valid Cases 347 a 2 cells (20.0%) have expected count less than 5. The minimum expected count is 3.16. Gender (q9) * q1 b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.823(a) 4 .029 Likelihood Ratio 10.867 4 .028 LinearbyLinear .629 1 .428 Association N of Valid Cases 347 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.92. Gender (q9) * q1 C ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.123(a) 4 .038 Likelihood Ratio 10.575 4 .032 LinearbyLinear .019 1 .889 Association N of Valid Cases 347 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 5.53. Gender (q9) * q1e ChiSquare Tests a 1 cells (10.0%) have expected count less than 5. The minimum expected count is 3.95 Asymp. Sig. Value df 12sidedl Pearson ChiSquare 13.982(a) 4 .007 Likelihood Ratio 14.106 4 .007 LinearbyLinear Association 5.912 1 .015 N of Valid Cases 347 · . Gender (q9) * q2d ChiSquare Tests a 4 cells (28.6%) have expected count less than 5. The minimum expected count is 1.18. Asymp. Sig. Value df 12sidedl Pearson ChiSquare 33.123(a) 6 .000 Likelihood Ratio 33.210 6 .000 LinearbyLinear 17.995 1 .000 Association N of Valid Cases 346 · . Gender (q9) * q2e ChiSquare Tests a 4 cells (28.6%) have expected count less than 5. The minimum expected count is 1.18. Asymp. Sig. Value df (2sided)' Pearson ChiSquare 30.986(a) 6 .000 Likelihood Ratio 30.935 6 .000 LinearbyLinear 18.288 1 .000 Association N of Valid Cases 347 · . Gender (q9) * q2h ChiSquare Tests a 8 cells (57.1%) have expected count less than 5. The minimum expected count is 1.57. Asymp. Sig. Value df (2sided) Pearson ChiSquare 26.108(a) 6 .000 Likelihood Ratio 27.402 6 .000 LinearbyLinear 6.411 1 .011 Association N of Valid Cases 346 · . 83 Gender (q9) * q2i ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 20.390(a) 6 .002 Likelihood Ratio 20.352 6 .002 LinearbyLinear Association 15.551 1 .000 N of Valid Cases 346 a 3 cells (21.4%) have expected count less than 5. The minimum expected count is 1.58. Gender (q9) * q3e ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 12.297(a) 6 .056 Likelihood Ratio 13.545 6 .035 LinearbyLinear 7.661 1 .006 Association N of Valid Cases 346 a 10 cells (71.4%) have expected count less than 5. The minimum expected count is .39. Gender (q9) * q7 ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 8.037(a) 3 .045 Likelihood Ratio 8.262 3 .041 LinearbyLinear 7.561 1 .006 Association N of Valid Cases 346 a 1 cells (12.5%) have expected count less than 5. The minimum expected count is 3.54. Gender (q9) * Department (q8) ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 62.592(a) 8 .000 Likelihood Ratio 68.804 8 .000 LinearbyLinear 36.439 1 .000 Association N of Valid Cases 347 a 5 cells (27.8%) have expected count less than 5. The minimum expected count is 2.37. 84 Gender (q9) * Year In School (q11) ChiSquare Tests a 0 cells (.0%) have expected count less than 5. The minimum expected count is 13.42. Asymp. Sig. Value df (2sided) Pearson ChiSquare 54.952(a) 4 .000 Likelihood Ratio 54.891 4 .000 LinearbyLinear Association 40.880 1 .000 N of Valid Cases 347 . . Department (q8) * q1 b ChiSquare Tests a 33 cells (73.3 ~o) have expected count less than 5. The minimum expected count is .26. Asymp. Sig. Value df (2sided) Pearson ChiSquare 70.239(a) 32 .000 Likelihood Ratio 79.039 32 .000 LinearbyLinear 1.016 1 .314 Association N of Valid Cases 347 0 · . Department (q8) * q1d ChiSquare Tests a 30 cells (66.7 Yo) have expected count less than 5. The minimum expected count is .35. Asymp. Sig. Value df (2sided) Pearson ChiSquare 78.413(a) 32 .000 Likelihood Ratio 73.178 32 .000 LinearbyLinear 14.316 1 .000 Association N of Valid Cases 347 0 · . Department (q8) * q2c ChiSquare Tests a 51 cells (81.0Yo) have expected count less than 5. The minimum expected count is .12. ,.... Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.824(a) 48 .063 LikelihOod Ratio 66.130 48 .042 LinearbyLinear .000 1 .995 Association N of Valid Cases 343 0 · . 85 Department (q8) * q2d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 104.680(a) 48 .000 Likelihood Ratio 82.999 48 .001 LinearbyLinear 15.957 1 Association .000 N of Valid Cases 346 a 50 cells (79.4%) have expected count less than 5. The minimum expected count is .05. Department (q8) * q2e ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 111.456(a) 48 .000 Likelihood Ratio 79.047 48 .003 LinearbyLinear 13.243 1 .000 Association N of Valid Cases 347 a 50 cells (79.4%) have expected count less than 5. The minimum expected count is .05. Department (q8) * q2h ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 77.906(a) 48 .004 Likelihood Ratio 66.529 48 .039 LinearbyLinear 8.962 1 .003 Association N of Valid Cases 346 a 51 cells (81.0%) have expected count less than 5. The minimum expected count is .07. Department (q8) * q2i ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 75.649(a) 48 .007 Likelihood Ratio 83.750 48 .001 LinearbyLinear .108 1 .742 Association N of Valid Cases 346 a 49 cells (77.8%) have expected count less than 5. The minimum expected count is .07. 86 Department (q8) * q3e ChiSquare Tests a 53 cells (84.1 %) have expected count less than 5. The minimum expected count is .02. Asymp. Sig. Value df (2sided) Pearson ChiSquare 100.945(a) 48 .000 Likelihood Ratio 49.014 48 .432 LinearbyLinear .051 1 .821 Association N of Valid Cases 346 . . Department (q8) * Year In School (q11) ChiSquare Tests a 32 cells (71.1 Yo) have expected count less than 5. The minimum expected count is .59 Asymp. Sig. Value df (2sided) Pearson ChiSquare 343.587(a) 32 .000 Likelihood Ratio 344.857 32 .000 LinearbyLinear 147.488 1 .000 Association N of Valid Cases 347 0 .. q5 * q2b ChiSquare Tests a 9 cells (42.9 Yo) have expected count less than 5. The minimum expected count is .19. Asymp. Sig. Value df (2sidedf "Pearson ChiSquare 28.134(a) 12 .005 Likelihood Ratio 19.516 12 .077 LinearbyLinear .336 1 .562 Association N of Valid Cases 350 0 .. q5 * q2i ChiSquare Tests a 6 cells (28.6 Yo) have expected count less than 5. The minimum expected count is .25. ,.... Asymp. Sig. Value df (2sidedl Pearson ChiSquare 25.120(a) 12 .014 Likelihood Ratio 19.016 12 .088 LinearbyLinear 3.343 1 .068 Association N of Valid Cases 349 0 .. 87 q5 *q4 ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 101.363(a) 8 .000 Likelihood Ratio 82.692 8 .000 LinearbyLinear 60.495 Association 1 .000 N of Valid Cases 350 a 5 cells (33.3%) have expected count less than 5. The minimum expected count is .50. q4 * q2a ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 35.272(a) 24 .064 Likelihood Ratio 31.328 24 .145 LinearbyLinear .181 1 .670 Association N of Valid Cases 349 a 22 cells (62.9%) have expected count less than 5. The minimum expected count is .09. q4 * q3a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 38.014(a) 24 .035 Likelihood Ratio 45.766 24 .005 LinearbyLinear 1.981 1 .159 Association N of Valid Cases 348 a 20 cells (57.1 %) have expected count less than 5. The minimum expected count is .23. q4 * q3c ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 82.530(a) 24 .000 Likelihood Ratio 80.182 24 .000 LinearbyLinear 53.777 1 .000 Association N of Valid Cases 349 a 17 cells (48.6%) have expected count less than 5. The minimum expected count is .23. 88 q3e * q1a ChiSquare Tests a 26 cells (74.3%) have expected count less than 5. The minimum expected count is .02. Asymp. Sig. Value df 12sided)· Pearson ChiSquare 36.171 (a) 24 .053 Likelihood Ratio 21.484 24 .610 LinearbyLinear 4.877 1 .027 Association N of Valid Cases 354 · . q3e * q1b ChiSquare Tests a 26 cells (74.3%) have expected count less than 5. The minimum expected count is .05. Asymp. Sig. Value df (2sided) Pearson ChiSquare 36.420(a) 24 .050 Likelihood Ratio 26,252 24 .341 LinearbyLinear 1.716 1 Association .190 N of Valid Cases 354 · . q3e * q1c ChiSquare Tests a 27 cells (77.1 "io) have expected count less than 5. The minimum expected count is .04.  Asymp. Sig. Value df 12sidedf Pearson ChiSquare 35.314(a) 24 .064 Likelihood Ratio 32.705 24 .110 LinearbyLinear 1.257 1 .262 Association N of Valid Cases 354 · . q3e * q1d ChiSquare Tests a 26 cells (74.37"0) have expected count less than 5. The minimum expected count is .06. .... Asymp. Sig. Value df 12sidedf ~earson ChiSquare 38.175(a) 24 .033 Likelihood Ratio 23.478 24 .492 LinearbyLinear 3.572 1 .059 Association N of Valid Cases 354 0 · . 89 q3e * q1e ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 47.975(a) 24 .003 Likelihood Ratio 36.691 24 .047 LinearbyLinear Association 5.061 1 .024 N of Valid Cases 354 a 26 cells (74.3%) have expected count less than 5. The minimum expected count is .03. q3e * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 111.715(a) 36 .000 Likelihood Ratio 38.486 36 .358 LinearbyLinear 6.789 1 .009 Association N of Valid Cases 353 a 40 cells (81.6%) have expected count less than 5. The minimum expected count is .01. q3e * q2b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 48.264(a) 36 .083 Likelihood Ratio 29.667 36 .763 LinearbyLinear 1.246 1 .264 Association N of Valid Cases 354 a 40 cells (81.6%) have expected count less than 5. The minimum expected count is .01. q5 * q3c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 24.430(a) 12 .018 Likelihood Ratio 22.643 12 .031 LinearbyLinear 15.508 1 .000 Association N of Valid Cases 349 a 9 cells (42.9%) have expected count less than 5. The minimum expected count is .63 90 q3e * q2d ChiSquare Tests a 39 cells (79.6 Yo) have expected count less than 5. The minimum expected count is .01. Asymp. Sig. Value df /2sidedf Pearson ChiSquare 85.993(a) 36 .000 Likelihood Ratio 42.533 36 .210 LinearbyLinear 5.454 1 .020 Association N of Valid Cases 353 0 · . q3e * q2e ChiSquare Tests a 39 cells (79.6 Yo) have expected count less than 5. The minimum expected count is .01. Asymp. Sig. Value df (2sided) Pearson ChiSquare 69.111 (a) 36 .001 Likelihood Ratio 39.770 36 .306 LinearbyLinear 14.602 1 .000 ASSociation N of Valid Cases 354 0 · . q3e * q2f ChiSquare Tests a 40 cells (81.6 Yo) have expected count less than 5. The minimum expected count is .01. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 54.118(a) 36 .027 LikelihOod Ratio 31.332 36 .690 LinearbyLinear 4.869 1 .027 Association N of Valid Cases 352 0 · . q3e * q2h ChiSquare Tests a 43 cells (87.8 Yo) have expected count less than 5. The minimum expected cou.nt is .01. Asymp. Sig. Value df l2sidedf ~earson ChiSquare 223.388(a) 36 .000 Likelihood Ratio 42.284 36 .218 LinearbyLinear 15.366 1 .000 Association N of Valid Cases 353 0 · . 91 q3e * q2i ChiSquare Tests a 38 cells (77.6%) have expected count less than 5. The minimum expected count is .01 . Asymp. Sig. Value df (2sidedl Pearson ChiSquare 69.762(a) 36 .001 Likelihood Ratio 45.689 36 .129 LinearbyLinear 6.208 Association 1 .013 N of Valid Cases 353 . . q3e * q3a ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 58.637(a) 36 .010 Likelihood Ratio 51.584 36 .045 LinearbyLinear .114 1 .735 Association N of Valid Cases 353 a 38 cells (77.6%) have expected count less than 5. The minimum expected count is .03. q3e * q3b ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 142.578(a) 36 .000 Likelihood Ratio 44.057 36 .168 LinearbyLinear 21.852 1 .000 Association N of Valid Cases 354 a 41 cells (83.7%) have expected count less than 5. The minimum expected count is .00. q3e * q3c ChiSquare Tests a 36 cells (73.5%) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df (2sided) Pearson ChiSquare 54.640(a) 36 .024 Likelihood Ratio 36.542 36 .443 LinearbyLinear 4.251 1 .039 Association N of Valid Cases 354 .. 92 q3d * q2a ChiSquare Tests a 29 cells (59.2 Yo) have expected count less than 5. The minimum expected count is .12. Asymp. Sig. Value df (2sided)' Pearson ChiSquare 50.849(a) 36 .051 Likelihood Ratio 56.304 36 .017 LinearbyLinear .015 1 .902 Association N of Valid Cases 353 0 · . q3d * q2e ChiSquare Tests a 32 cells (65.3 Yo) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value df (2sided) Pearson ChiSquare 50.619(a) 36 .054 Likelihood Ratio 46.587 36 .111 LinearbyLinear 2.282 1 .131 AsSociation N of Valid Cases 354 0 · . q3d * q2f ChiSquare Tests a 30 cells (61.2 Yo) have expected count less than 5. The minimum expected count is .13.  Asymp. Sig. Value dl (2sidedf pearson ChiSquare 64.753(a) 36 .002 Likelihood ~atio 61.473 36 .005 LinearbyUnear 5.689 1 .017 Association N of Valid Cases 352 a · . q3d * q2h ChiSquare Tests a 33 cells (67.3 Yo) have expected count less than 5. The minimum expected count is .14.  Asymp. Sig. Value df (2sidedf pearson ChiSquare 51.636(a) 36 .044 Likelihood ~atio 47.620 36 .093 LinearbyUnear 4.998 1 .025 Association N of Valid Cases 353 a · . 93 q3d * q3a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.614(a) 36 .003 Likelihood Ratio 64.452 36 .002 LinearbyLinear Association 11.407 1 .001 N of Valid Cases 353 a 28 cells (57.1%) have expected count less than 5. The minimum expected count is .31. q3d * q3c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 50.742(a) 36 .053 Likelihood Ratio 55.044 36 .022 LinearbyUnear 3.305 1 .069 Association N of Valid Cases 354 a 27 cells (55.1%) have expected count less than 5. The minimum expected count is .34. q3c * q1c ChiSquare Tests Asymp. Sig. Value df (2sided)" Pearson ChiSquare 34.417(a) 24 .078 Likelihood Ratio 37.582 24 .038 LinearbyUnear .530 1 .466 Association N of Valid Cases 354 a 16 cells (45.7%) have expected count less than 5. The minimum expected count is .42. q3c * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 57.474(a) 36 .013 Likelihood Ratio 51.122 36 .049 UnearbyUnear 2.296 1 .130 Association N of Valid Cases 353 a 30 cells (61.2%) have expected count less than 5. The minimum expected count is .11. 94 q3C * q2b ChiSquare Tests a 31 cells (63.3 Yo) have expected count less than 5. The minimum expected count is .08. Asymp. Sig. Value df (2sided)' Pearson ChiSquare 50.583(a) 36 .054 Likelihood Ratio 47.794 36 .090 LinearbyLinear 2.066 1 .151 Association N of Valid Cases 354 0 · . q3C * q2c ChiSquare Tests p ted count less than 5 The minimum expected count is .23. ChiSquare Tests a 24 cells (49 q3C * q2d Asymp. Sig. Value df (2·sided)' ~earson ChiSquare 51.205(a) 36 .048 Likelihood Ratio 54.996 36 .022 LinearbtLinear 6.983 1 .008 AsSociatIOn N of Valid Cases 350 .0% have ex ec · . ChiSquare Tests a 28 cells (57 ) a e e p ted count less than 5. The minimum expected count is .08. q3C * q2e Asymp. Sig. Value df (2sided) pearson ChiSquare 58.561 (a) 36 .010 LikelihOod ~atio 61.804 36 .005 Linearbylinear .280 1 .597 AsSociation N of Valid Cases 353 .1% h v x ec · . a 30 cells (61.2 Yc) a e expecte 0 t ess than 5 The m n mum expected count is .08.  Asymp. Sig. Value df 12sided) ~earson ChiSquare 72.101(a) 36 .000 LikelihOod ~atio 73.225 36 .000 Linearbylinear 4.276 1 .039 ociation ~f Valid Cases 354 °0 h v de un I i i 95 q3c * q2f ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 63.598(a) 36 .003 Likelihood Ratio 59.962 36 .007 LinearbyLinear Association 9.797 1 .002 N of Valid Cases 352 a 26 cells (53.1 %) have expected count less than 5. The minimum expected count is .11. q3c * q2h ChiSquare Tests a 35 cells (71.4%) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df (2sided) Pearson ChiSquare 54.896(a) 36 .023 Likelihood Ratio 41.552 36 .242 LinearbyLinear .061 1 .805 Association N of Valid Cases 353 .. q3c * q2i ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 55.468(a) 36 .020 Likelihood Ratio 56.738 36 .015 LinearbyLinear 3.502 1 .061 Association N of Valid Cases 353 a 26 cells (53.1 %) have expected count less than 5. The minimum expected count is .14. q3c * q3a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 50.719(a) 36 .053 Likelihood Ratio 56.873 36 .015 LinearbyLinear 3.245 1 .072 Association N of Valid Cases 353 a 27 cells (55.1 %) have expected count less than 5. The minimum expected count is .28. 96 q3b * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 67.363(a) 36 .001 LikelihOod Ratio 55.079 36 .022 LinearbyLinear 19.294 1 .000 Association N of Valid Cases 353 a 38 cells (77.60Yo) have expected count less than 5. The mi.ni.mum expected count is .01 . q3b It q2b ChiSquare Tests t a pected count is .01. ChiSquare Tests Asymp. Sig. Value df (2sided) P"""pearson ChiSquare 53.332(a) 36 .031 Likelihood ~atio 47.894 36 .089 LJ'nearby. llnear 9.993 1 .002 Association N of valid Cases 354 a 36 cells (73.50Yo) have expected count less h n 5. The minimum ex q3b '* q2c p xpected count is .02. ChiSquare Tests ..... Asymp. Sig. Value df (2sided) ....p. earson ChiSquare 65.204(a) 36 .002 LikelihOod ~atio 42.589 36 .209 L1' 0earby.LlOear 10.888 1 .001 Association N of Valid Cases 350 a 35 cells (71.4% have ex ected count less than 5. The minimum e q3b '* q2d a 36 celiS (73 p xpected count is .01. P""" Asymp. Sig. Value df (2sided) Pearson ChiSquare 52.727(a) 36 .036 . lihood Ratio 45.963 36 .124 uke r byLinear 2.067 1 .150 Linea  . sociatJon ~of valid Cases 353 .5% have ex ected count less than 5. The minimum e 97 q3b * q2e ChiSquare Tests a 37 cells (75.5%) have expected count less than 5. The minimum expected count is .01. Asymp. Si9. Value df 12sided) Pearson ChiSquare 51.843(a) 36 .042 Likelihood Ratio 40.004 36 .297 LinearbyLinear Association 4.731 1 .030 N of Valid Cases 354 .. q3b * q2f ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 83.244(a) 36 .000 Likelihood Ratio 44.282 36 .162 LinearbyLinear 6.314 1 .012 Association N of Valid Cases 352 a 35 cells (71.4%) have expected count less than 5. The minimum expected count is .01. q3b * q2h ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 156.871 (a) 36 .000 Likelihood Ralio 46.056 36 .122 LinearbyLinear 8.660 1 .003 Association N of Valid Cases 353 a 40 cells (81.6%) have expected count less than 5. The minimum expected count is .01. q3b * q2i ChiSquare Tests a 35 cells (71.4%) have expected count less than 5. The minimum expected count is .01 . Asymp. Si9. Value df 12sided) Pearson ChiSquare 76.832(a) 36 .000 Likelihood Ratio 48.654 36 .078 LinearbyLinear 1.775 1 .183 Association N of Valid Cases 353 . . 98 q3b * q3a ChiSquare Tests a 35 cells (71.4 Yo) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df (2sided) Pearson ChiSquare 250.114(a) 36 .000 Likelihood Ratio 203.931 36 .000 LinearbyLinear 79.166 1 .000 Association N of Valid Cases 353 0 · . q3a * q2a ChiSquare Tests pected count less than 5. The minimum expected count is .11, ChiSquare Tests a 32 cells (65 q3a * q2b '""' Asymp. Sig. Value df (2sided\ pearson ChiSquare 67.820(a) 36 .001 Likelihood ~atio 55.557 36 .020 L'Inearby. Llnear 8.709 1 .003 AsSociation N of Valid Cases 352 .3% have ex · . a 30 cells (61. ) ha e expected count less than 5. The minimum expected count is .08.  Asymp. Sig. Value df (2sidedl pearson ChiSquare 53.746(a) 36 .029 Likelihood ~atio 58.671 36 .010 LI'nearby,L1near .707 1 .400 ASsociation N of valid Cases 353 2% v · . q38 * q2c ChiSquare Tests a 29 cells ( p xpected count is .21. r Asymp. Sig. Value df (2sided\ 'Pearson ChiSquare 61.274(a) 36 .005 Likelihood ~atio 57.163 36 .014 Linearb~L1near 3.666 1 ,056 ASsociation N of valid Cases 349 59.2% have ex ected count less than 5. The minimum e 99 q3a * q2d ChiSquare Tests Asymp. Sig. Value df 12sidedl Pearson ChiSquare 59.699(a) 36 .008 Likelihood Ratio 46.529 36 .112 LinearbyLinear Association .432 1 .511 N of Valid Cases 352 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .09. q3a * q2f ChiSquare Tests Asymp. Sig. Value df 12sided)' Pearson ChiSquare 71.920(a) 36 .000 Likelihood Ratio 64.613 36 .002 LinearbyLinear 4.834 1 .028 Association N of Valid Cases 352 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .11. q3a * q2g ChiSquare Tests Asymp. Sig. Value df l2sided) Pearson ChiSquare 68.468(a) 36 .001 Likelihood Ratio 71.773 36 .000 LinearbyLinear 2.032 1 .154 Association N of Valid Cases 351 a 25 cells (51.0%) have expected count less than 5. The minimum expected count is .66. q3a * q2h ChiSquare Tests a 34 cells (69.4%) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df 12sided) Pearson ChiSquare 53.709(a) 36 .029 Likelihood Ratio 39.771 36 .306 LinearbyLinear 3.403 1 .065 Association N of Valid Cases 352 .. 100 101 24 24 1 36 36 1 36 36 1 df 354 Value df Value 353 Value df 354 Value df 56.181(a) 36 45.796 36 3.424 1 352 38.230(a) 43.267 .024 64.015(a) 45.862 12.916 ChiSquare Tests ChiSquare Tests ChiSquare Tests ChiSquare Tests 108.607(a) 77.861 28.442 re re have expected count less than 5. The minimum expected count is .14. re have expected count less than 5. The minimum expected count is .31. have expected count less than 5. The minimum expected count is .06. have expected count less than 5. The minimum expected count is .04. are q3a ." q2i Pearson ChiSqu Ukelihood Ratio unearbyLinear Association N of Valid Cases a 29 cells (59.2%) q2i ." q1d ~ Pearson ChiSqua 'kelihood Ratio LJ. earbYL'Inear un . ASSociation N of Valid Cases a 16 celis (45.7%) q2i ." q2a ,.... r:p: earson ChiSqua ok Iihood Ratio uUneearbtL'Inear AsSociation N of Valid Cases a 31 celis (63.3%) q2i ." q2b ~ ~earson Chi~qua . Iihood RatiO Like r byLinear unea  ° ociatlon ~f valid Cases 1 celiS (63.3%) a3 q2i * q2c ChiSquare Tests a 28 cells (57.1 %) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sided) Pearson ChiSquare 76.062(a) 36 .000 Likelihood Ratio 61.164 36 .006 LinearbyLinear Association 21.294 1 .000 N of Valid Cases 350 .. q2i * q2d ChiSquare Tests a 31 cells (63.3%) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df 12sided\ Pearson ChiSquare 99.968(a) 36 .000 Likelihood Ratio 63.154 36 .003 LinearbyLinear 25.199 1 .000 Association N of Valid Cases 353 .. q2i * q2e ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 134.293(a) 36 .000 Likelihood Ratio 92.301 36 .000 LinearbyLinear 48.796 1 .000 Association N of Valid Cases 354 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .04. q2i * q2f ChiSquare Tests Asymp. Sig. Value df 12sided\ Pearson ChiSquare 77.605(a) 36 .000 Likelihood Ratio 62.209 , 36 .004 LinearbyLinear 18.154 1 .000 Association N of Valid Cases 352 a 31 cells (63.3%) have expected count less than 5. The minimum expected count is .06. 102 q2i * q2g ChiSquare Tests Pearson ChiSquare Likelihood ~atio LinearbyLlnear AsSociation N of Valid Cases Value 71.162(a) 66.333 26.381 352 Asymp. Sig. df 2sided) 36 .000 36 .002 .000 a 21 cells (42.9%) have expected count less than 5. The minimum expected count is .34. q2i * q2h ChiSquare Tests pearso n ChiS.quare L'kelihOod ~atlo u~near_by.LlOear ociatlon ~f Valid Cases Value 161.668(a) 86.130 38.987 353 Asymp. Sig. df 2sided)' 36 .000 36 .000 1 .000 ChiSquare Tests 5 e lls (71.4%) have expected count less than 5. The minimum expected count is .06. a 3 c q2h * q1a On ChiSquare Pears . elihood Ratio Uk r byLinear U· ean iatiOn ~alidcases Value 24.392(a) 23.793 .308 354 Asymp. Sig. df 2sided) 24 .439 24 .474 1 .579 ChiSquare Tests I/S (77.1%) have expected count less than 5. The minimum expected count is .09. a 27 ce q2h * q1b Asymp. Sig. Value df 2sidedf L_n~C~hh1ir:;SScq;u~a~re;r44~1~7.~39~(;;a))1::':~2:44t"~ .014 pea,?,"~ Ratio 42.390 24 .012 LU'kneela1hrby_Linear .232 1 .630 I iaPon ~aJid cases 354 115 (65.7%) have expected count less than 5. The minimum expected count is .19. a 2308 103 q2h * q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 22.996(a) 24 .520 Likelihood Ratio 24.898 24 .411 LinearbyLinear Association .843 1 .358 N of Valid Cases 354 a 23 cells (65.7%) have expected count less than 5. The minimum expected count is .17. q2h * q1d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 57.253(a) 24 .000 Likelihood Ratio 54.797 24 .000 LinearbyLinear 7.883 1 .005 Association N of Valid Cases 354 a 23 cells (65.7%) have expected count less than 5. The minimum expected count is .25. q2h * q1e ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 43.007(a) 24 .010 Likelihood Ratio 40.729 24 .018 LinearbyLinear 11.082 1 .001 Association N of Valid Cases 354 a 24 cells (68.6%) have expected count less than 5. The minimum expected count is .11. q2h * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 139.236(a) 36 .000 Likelihood Ratio 52.017 36 .041 LinearbyLinear 15.644 1 .000 Association N of Valid Cases 353 a 38 cells (77.6%) have expected count less than 5. The minimum expected count is .03. 104 q2h * q2b ChiSquare Tests p xpected count is .03. ChiSquare Tests a 37 cells ( q2h * q2c Asymp. Sig. Value df l2sided) pearson ChiSquare 124.255(a) 36 .000 UkelihOOd ~atio 73.329 36 .000 Unearb¥Llnear 36.847 1 _000 AsSOCiation N of Valid Cases 354 75.5% have ex ected count less than 5. The minimum e p xpected count is .09. ChiSquare Tests a 36 celiS ( q2h * q2d Asymp. Sig. Value df l2sided) ~ son ChiSquare 101.657(a) 36 .000 Pear . lihood Ratio 65.955 36 .002 uke r byLinear 16.661 1 .000 Unea  . ,ASSOCiatiOn N of valid Cases 350 73.5% have ex ected count less than 5. The minimum e xpected count is .02. ChiSquare Tests a 37 celiS ( q2h * q2e ... Asymp. Sig. Value df (2sided) ~ "'"'O'n ChiSquare 408.956(a) 36 .000 Pears . . Iihood RatiO 172.134 36 .000 ~~:ar_b¥Linear 132.107 1 .000 AsSOCiatiOn N of Valid Cases 353 75.5%) have expected count less than 5. The minimum e xpected count is .03. a 3B celiS ( ....  Asymp. Sig. Value df (2sided) i""""'" on ChiSquare 193.185(a) 36 .000 Pears . 122.029 36 .000 'k8lihOOd ~atlO U byLinear 92.305 1 .000 Unear . 'atlOn As5~1 lid Cases 354 Nof a 77.6%) have expected count less than 5. The minimum e 105 q2h * q2f ChiSquare Tests Asymp.8ig. Value df (2sided)' Pearson ChiSquare 136.367(a) 36 .000 U elihood Ratio 76.547 36 .000 Unear·by·Unear 37.991 1 Association .000 of Valid Cases 352 a 37 cells (75.5%) have expected count less than 5. The minimum expected count is .05. q2h * q2g ChiSquare Tests Asymp.8ig. Value df (2sided) Pearson ChiSquare 55.056(a) 36 .022 Likelihood Ratio 52.390 36 .038 Unear·byUnear 16.898 1 .000 Association of Valid Cases 352 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .26. q2g * q2a ChiSquare Tests Asymp.8ig. Value df (2sidedl Pearson ChiSquare 67.394(a) 36 .001 U elihood Ratio 55.704 36 .019 UnearbyUnear 19.087 1 .000 soc aUon of Valid Cases 352 27 cells (55.1%) have expected count less than 5. The minimum expected count is .27. q2g· q2b ChiSquare Tests Asymp.8ig. Value df (2sided) Pearson ChiSquare 49.958(a) 36 .061 U elihood Ralio 52.614 36 .036 Unearby·Unear 12.418 1 .000 ociation of Valid Cases 353 a 30 cells (61.2%) have expected count less than 5. The minimum expected count is .20. 106 9 * q2c ChiSquare Tests pearson Chi·Square UkelihOOd ~atio ljnear_byLlOear AsSOCiation of Valid Cases Value 74.291 (a) 72.269 29.280 349 df 36 36 1 a 23 cells (46.9%) have expected count less than 5. The minimum expected count is .55. q2g * q2e __renn Chisquare p~ t' (Jkefihood ~a 10 _ r_byLinear unea iatiOn As~alid Cases ChiSquare Tests Value df 76.138(a) 73.321 15.842 353 36 36 1 ChiSquare Tests a 29 cells (59.2%) have expected count less than 5. The minimum expected count is .20. q2g * q2f n ChiSquare p_eaf'SihOOOd Aat'10 uLJnkBeIarbyLinear 'ation As;v'alid Cases Value df 193.489(a) 155.198 82.883 351 36 36 1 lis (49.0%) have expected count less than 5. The minimum expected count is .26. a 24C8 * q1d ChiSquare Tests Value df 36.374(a) 24 34.216 24 3.879 1 353 liS (48.6%) have expected count less than 5. The minimum expected count is .25. a 17C8 107 q2f· q1e ChiSquare Tests Asymp. Si9. Value df (2sided) Pearson ChiSquare 36.129(a) 24 .053 U elihood Ratio 34.467 24 .077 UnearbyLinear Association 5.622 1 .018 of Valid Cases 353 a 20 cells (57.1 %) have expected count less than 5. The minimum expected count is .11. q2f· q2a ChiSquare Tests Asymp. Si9. Value df (2sided)' Pearson ChiSquare 94.307(a) 36 .000 Likelihood Ratio 66.300 36 .002 LinearbyUnear 29.944 1 .000 Association of Valid Cases 352 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .05. q2f. q2b ChiSquare Tests Asymp. Si9. Value df (2sided>' Pearson ChiSquare 112.159(a) 36 .000 Li elihood Ratio 77.474 36 .000 n arbyUnear 38.281 1 .000 Associat on of Valid Cases 353 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .03. q2f· q2c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 190.425(a) 36 .000 li elihood Ratio 113.129 36 .000 UnearbyLinear 45.040 1 .000 Association of Valid Cases 349 a 29 cells (59.2%) have expected count less than 5. The minimum expected count is .09. 108 * q2d ChiSquare Tests p unt less than 5. The minimum expected count is .03. Asymp. Big. Value df (2sided) Pearson ChiSquare 95.463(a) 36 .000  ih()Od Ratio 55.211 36 .021 LjnearbyLinear 24.278 1 .000 AssOCiation of Valid Cases 352 0 have ex ected co .. a 33 celiS (67.3 Vo) q2f * q2e ChiSquare Tests __.en" ChiSquare Pt:UP . LjkBIihOOd ~atlO unear_b~L.,"ear AsSOCiatiOn of Valid Cases Value 231.894(a) 101.766 58.636 353 df 36 36 1 cells (67.3%) have expected count less than 5. The minimum expected count is .03. a33 e * q1b ChiSquare Tests xpected count is .14. a 20 oellS (  Asymp. Big. Value df (2sided) ~ChiSquare 43.247(a) 24 .009 P. ihood ~alro 47.614 24 .003 . r_byunear 3.700 1 .054 L,jneS iatjO" ~aJidCases 355 57.1%) have expected count less than 5. The minimum e e * q1d ChiSquare Tests Value df 35.904(a) 24 34.125 24 3.146 1 355 II (54.3%) have expected count less than 5. The minimum expected count is .19. a 19 ce S 109 ChiSquare Tests ChiSquare Tests ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 137.182(a) 36 .000 Li elihood Ratio 86.364 36 .000 UnearbyL1near Association 56.405 1 .000 of Valid Cases 355 q2e· q2b q2e· q1e q2e * q2a a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .08. a 36 cells (73.5%) have expected count less than 5. The minimum expected count is .03. Asymp. Sig. Value df (2sided) Pearson ChiSquare 33.529(a) 24 .093 Li elihood Ratio 34.806 24 .071 UnearbyL1near 6.419 1 .011 Association of Valid Cases 355 Asymp. Sig. Value df (2sided) Pearson ChiSquare 69.168(a) 36 .001 Likelihood Ratio 43.473 36 .183 UnearbyLinear 6.149 1 .013 Association N of Valid Cases 354 .. a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .03. q2e· q2c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 68.015(a) 36 .001 Likelihood Ratio 53.302 36 .032 UnearbyL1near 14.444 1 .000 Association N of Valid Cases 351 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .07. 110 e* q2d ChiSquare Tests 36 36 1 df 354 Value 264.936(a) 161.514 104.371 a 34 cells (69.4%) have expected count less than 5. The minimum expected count is .03. d * q1a ChiSquare Tests ~n ChiSquare P t' _0 __ ..A Aa 10 ljkeI'rK1'""" byLinear L.if1eBJ" " AsSOCiat.on of valid Cases Value 43.810(a) 41.544 .053 354 df 24 24 1 lis (62.9%) have expected count less than 5. The minimum expected count is .07. a 22ce d * q1C ChiSquare Tests Value df 46.031 (a) 24 50.429 24 4.564 1 354 lis (51.4%) have expected count less than 5. The minimum expected count is .13. a 1S ce .. q1e ChiSquare Tests .000 24 24 1 Value df 80.733(a) 84.401 28.412 ChiSquare P~oodAatio .h _Linear L,jrIeBfbY "alion NJBDCIaI"d cases cA V • 354 II (57.1%) have expected count less than 5. The minimum expected count is .08. a 20cB s III q2d * q2a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 169.115(a) 36 .000 Likelihood Ratio 65.009 36 .002 LinearbyLinear Association 2.495 1 .114 N of Valid Cases 353 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .03. q2d * q2b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 134.754(a) 36 .000 Likelihood Ratio 71.059 36 .000 LinearbyLinear 24.332 1 .000 Association N of Valid Cases 354 a 33 cells (67.3%) have expected count less than 5. The minimum expected count is .03. q2d * q2c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 78.329(a) 36 .000 Likelihood Ratio 66.271 36 .002 LinearbyLinear 11.782 1 .001 Association N of Valid Cases 350 a 32 cells (65.3%) have expected count less than 5. The minimum expected count is .07. q2c * q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 33.311(a) 24 .098 Likelihood Ratio 40.274 24 .020 LinearbyLinear 1.621 1 .203 Association N of Valid Cases 351 a 18 cells (51.4%) have expected count less than 5. The minimum expected count is .34. 11 c '* q2a ChiSquare Tests a 32 celiS (65.3 Yo) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sidedl pearson ChiSquare 97.819(a) 36 .000 UkelihOOd Ratio 70.027 36 .001 Ljneaf_byLinear 25.575 1 .000 AsSOCiation of Valid Cases 350 0 .. 2C'* q2b ChiSquare Tests a 32 celis ( p pected count is .07. ~ Asymp. Sig. Value df (2sidedl ~pearSOn ChiS.quare 224.335(a) 36 .000 'kelihood ~atlo 159,983 36 .000 t;nearb yllnear 73.359 1 .000 iatlon ~alidcases 351 65.3% have ex ected count less than 5. The minimum ex 2b'* q1e ChiSquare Tests a 21 celis ( p xpected count is .08. ~ Asymp. Sig. Value df (2sidedl pop: earSO" ChiS.quare 45.062(a) 24 .006 'kBlihood ~atlo 41.619 24 .014 'nearbVLlnear 3.403 1 .065 ASSOCiation of Valid Cases 355 60.0% have ex ected count less than 5. The minimum e b'* q2a ChiSquare Tests a 34 celiS ( xpected count is .03.  Asymp. Sig. Value df (2sided\ I"p':eaSr On ChiSquare 172.923(a) 36 .000 'kelihood Ratio 106.781 36 .000 Lu!near_byLinear 47.072 1 .000 iation ~aJjdCaSeS 354 69.4%) have expected count less than 5. The minimum e II q2a * q1a ChiSquare Tests a 23 cells (65.7%) have expected countless than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sided) Pearson ChiSquare 33.888(a) 24 .087 Likelihood Ratio 30.597 24 .166 LinearbyLinear Association 2.315 1 .128 N of Valid Cases 354 .. q2a * q1e ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 41.464(a) 24 .015 Likelihood Ratio 38.324 24 .032 LinearbyLinear .031 1 .860 Association N of Valid Cases 354 a 21 cells (60.0%) have expected count less than 5. The minimum expected count is .11. q1e*q1a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 503.573(a) 16 .000 Likelihood Ratio 336.788 16 .000 LinearbyLinear 60.371 1 .000 Association N of Valid Cases 384 a 14 cells (56.0%) have expected count less than 5. The minimum expected count is .37. q1e * q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 167.360(a) 16 .000 Likelihood Ratio 200.463 16 .000 LinearbyLinear 69.779 1 .000 Association N of Valid Cases 384 a 10 cells (40.0%) have expected count less than 5. The minimum expected count is .61. 114 e'" q1c ChiSquare Tests ChiSquare Tests u tess than 5 The minimum e pected count is .54. Asymp. Sig. Value df (2sided)' ~earson ChiSquare 205.B79(a) 16 .000 UkelihOOd ~atio 268.610 16 .000 tjneaf_byLlnear 68.841 1 .000 AsSOCiatIon of Valid Cases 384 00 v ex ected co n I .. a 10 cells (40.07<) ha e p x e'" q1d p xpected count is .85. ChiSquare Tests a 10 celiS ( d'" q1a Asymp. Sig. Value df (2sidedl pea,son ChiSquare 164.490(a) 16 .000 L.ikBlihood ~atio 173.372 16 .000 • ear_byllnear 81.247 1 .000 un iation ~aJidcases 384 40.0% have ex ected count less than 5. The minimum e xpected count is .72. ChiSquare Tests a 15 celiS ( 1d It q1b .... Asymp. Sig. Value df (2sidedl ~ SOn ChiSquare 279.870(a) 16 .000 pear . LJkeIihood ~atlo 230.692 16 .000 u•naar_byllnear 175.381 1 .000 jation ~alidcases 384 60.0%) have expected count less than 5. The minimum e a ected count is 1.17. 6 celiS (   Asymp. Sig. Value df (2sided) ~ear.;on ChiSquare 544.250(a) 16 .000 P'kBlihoOd ~atio 489.750 16 .000 ~ ar_byllnear 12.718 1 .000 una iation ~aJidcases 384 24.0%) have expected count less than 5. The minimum exp 11 q1d*q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 146.562(a) 16 .000 Likelihood Ratio 191.284 16 .000 LinearbyLinear 8.687 1 Association .003 N of Valid Cases 384 a 7 cells (28.0%) have expected count less than 5. The minimum expected count is 1.04. q1c*q1a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 85.681 (a) 16 .000 Likelihood Ratio 92.436 16 .000 LinearbyLinear 4.926 1 .026 Association N of Valid Cases 384 a 13 cells (52.0%) have expected count less than 5. The minimum expected count is .46. q1c * q1b ChiSquare Tests Asymp. Sig. Value df 12sided)' Pearson ChiSquare 156.326(a) 16 .000 Likelihood Ratio 223.398 16 .000 LinearbyLinear 12.424 1 .000 Association N of Valid Cases 384 a 8 cells (32.0%) have expected count less than 5. The minimum expected count is .75. q1b * q1a ChiSquare Tests Asymp. Sig. Value df 12sided) Pearson ChiSquare 260.047(a) 16 .000 Likelihood Ratio 203.991 16 .000 LinearbyLinear 148.535 1 .000 Association N of Valid Cases 384 a 13 cells (52.0%) have expected count less than 5. The minimum expected count is .52. 116 q7 * q2a ChiSquare Tests a 17 cells (60.7%) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 31.621(a) 18 .024 Likelihood Ratio 32.552 18 .019 LinearbyLinear 1.002 1 .317 Association N of Valid Cases 348 · . q7 * q2e ChiSquare Tests a 17 cells (60.7%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df (2sidedf Pearson ChiSquare 28.434(a) 18 .056 Likelihood Ratio 27.491 18 .070 LinearbyLinear .131 1 Association .717 N of Valid Cases 349 · . q7 * q2h ChiSquare Tests a 19 cells (67.9 Yo) have expected count less than 5. The minimum expected count is .11. Asymp. Sig. Value df (2sided) pearson ChiSquare 27.802(a) 18 .065 Likelihood Ratio 27.520 18 .070 LinearbyLinear 8.875 1 .003 Association N of Valid Cases 348 0 · . 117 Part 2: Environmental Soil Science Survey Questions Ql) Please rank the following factors in selecting your major. (Imost influential to 5least influential) Qla  Personal Interest Q 1b  Popularity of major Qlc  Family influences Qld  Scholarship Availability Qle  Potential Income Q2) Have you ever been in a major other than Environmental Soil Science at BYU? Q3) What was your previous major, if applicable? Q4) If you answered yes to the previous question, what led you to switch majors? Q5) What do you like about the Environmental Soil Science Program? Q6) What do you dislike about the Environmental Soil Science Program? Q7) Have you ever thought of leaving the ESS program for another major at BYU? Q8) Why did you consider leaving the program? Q9) Rate the importance of the following in regards to your major: Q9a  The academic qualifications of the Professors Q9b  Availability of professors for consultations Q9c  National reputation of the program Q9d  General Atmosphere/Environment of the Plant and Animal Sciences Department Qge  Job availability following graduation Q9f  Starting base salary after graduation Q9g  Variety of Environmental Science core courses Q9h  The inclusion of basic science courses in the Environmental Soil Science curriculum Q9i  The number of faculty and students in the Environmental Soil Science program Q9j  Communication among students and faculty in the program Q9k  Internship opportunities Q91  Field trips with students and faculty Q9m  Preparation for postgraduate education Q10) On a scale of 17l how satisfied are you with the availability of: QI0a  classes offered by the Plant and Animal Sciences Department? QI0b  basic science classes offered by other departments on campus? Qll) What semesters/terms should more Plant and Animal Science clas es be offered? Ql1aFall Q11b  Winter Q11c  Spring Ql1d  Summer Q11e  No need Q 12) Rate the following: Q12a  Teaching Faculty Q12b  Mentored research opportunities Q12c  Communication between students within the Environmental Soil Science program Ql2d  Communication between student and faculty within the Environmental Soil Science program Q13) How would you rate the difficulty of the Environmental Soil Science major? Q 14) In your opinion, changing the name of the program from Environmental Soil Sciellce t Environmental Science would help attract more tudents? Q15) What advice would you give to a student thinking of tudying Environm ntal Q16) Gender Q17) What is your age? Q18) Year in School II Year in School (q18) * q1 C ChiSquare Tests Asymp. Sig. Value df (2sided)' Pearson ChiSquare 10.000(a) 4 .040 Likelihood Ratio 9.780 4 .044 LinearbyLinear Association .004 1 .947 N of Valid Cases 10 a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .20. Year in School (q18) * q9b ChiSquare Tests Asymp. Sig. Value df 12sided)' Pearson ChiSquare 6.032(a) 2 .049 Likelihood Ratio 6.811 2 .033 LinearbyLinear 5.161 1 .023 Association N of Valid Cases 10 a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .60. Age (q17) * q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 15.143(a) 9 .087 Likelihood Ratio 11.032 9 .273 LinearbyLinear .621 1 .431 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Age (q17) * q1d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 14.933(a) 9 .093 Likelihood Ratio 11.090 9 .270 LinearbyLinear 2.647 1 .104 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. 120 Age (q17) * q9c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 19.000{a) 12 .089 Likelihood Ratio 18.867 12 .092 LinearbyLinear Association .717 1 .397 N of Valid Cases 10 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Age (q17) * qge ChiSquare Tests a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .20 Asymp. Sig. Value df (2sided) Pearson ChiSquare 13.600{a) 6 .034 Likelihood Ratio 13.863 6 .031 LinearbyLinear .043 1 .835 Association N of Valid Cases 10 .. Gender (q16) * q7 ChiSquare Tests Asymp. Sig. Exact Sig. Exact Sig. Value df (2sided) (2sided) (lsided) Pearson ChiSquare 3.600(b) 1 .058 Continuity 1.600 1 .206 Correction{a) Likelihood Ratio 3.855 1 .050 Fisher's Exact Test .206 .103 LinearbyLinear Association 3.240 1 .072 N of Valid Cases 10 a Computed only for a 2x2 table b 4 cells (100.0%) have expected count less than 5. The minimum expected count is 2.50. 121 Gender (q16) * q9h ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 8.000(a) 4 .092 Likelihood Ratio 11.090 4 .026 LinearbyLinear Association 2.194 1 .139 N of Valid Cases 10 a 10 cells (100.0%) have expected count less than 5. The minimum expected count is .50. q14 * q9k ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 15.333(a) 9 .082 Likelihood Ratio 16.774 9 .052 LinearbyLinear 2.326 1 .127 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q14 * q9m ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 17.167(a) 9 .046 Likelihood Ratio 14.001 9 .122 LinearbyLinear .987 1 .321 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12d * q7 ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 6.800(a) 2 .033 Likelihood Ratio 8.859 2 .012 LinearbyLinear .111 1 .739 Association N of Valid Cases 10 a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .50. q12d * q12a ChiSquare Tests a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 7.917(a) 4 .095 Likelihood Ratio 9.641 4 .047 LinearbyLinear Association 3.025 1 .082 N of Valid Cases 10 . . q12d * q12b ChiSquare Tests Asymp. Sig. Value Of (2sided) Pearson ChiSquare 13.025(a) 4 .011 Likelihood Ratio 9.364 4 .053 LinearbyLinear 5.818 1 .016 Association N of Valid Cases 10 a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12d * q12c ChiSquare Tests Asymp. Sig. Value Of (2sided) Pearson ChiSquare 16.625(a) 6 .011 Likelihood Ratio 14.368 6 .026 LinearbyLinear 7.402 1 .007 Association N of Valid Cases 10 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12c * q9d ChiSquare Tests Asymp. Sig. Value Of (2sidedl Pearson ChiSquare 16.667(a) 9 .054 Likelihood Ratio 14.507 9 .105 LinearbyLinear 3.640 1 .056 Association N of Valid Cases 10 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .10. 12 q12c * q12a ChiSquare Tests , Asymp. Sig. Value df /2sided)' Pearson ChiSquare 15.000(a) 6 .020 Likelihood Ratio 12.414 6 .053 LinearbyLinear Association 4.551 1 .033 N of Valid Cases 10 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q12c*q12b ChiSquare Tests ChiSquare Tests ChiSquare Tests q12b * q7 a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .50. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 6.800(a) 2 .033 Likelihood Ratio 8.859 2 .012 LinearbyLinear .818 1 .366 Association N of Valid Cases 10 . . a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .40. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 5.000(a) 2 .082 Likelihood Ratio 6.730 2 .035 LinearbyLinear .136 1 .712 Association N of Valid Cases 10 q12b * q2 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value df (2sidedl Pearson ChiSquare 13.250(a) 6 .039 Likelihood Ratio 9.870 6 .130 LinearbyLinear 2.979 1 .084 Association N of Valid Cases 10 124 q12b * q9d ChiSquare Tests a 12 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value Of (2.sidedf Pearson ChiSquare 14.375(a) 6 .026 Likelihood Ratio 11.596 6 .072 LinearbyLinear .818 1 .366 Association N of Valid Cases 10 0 · . q12a * q9f ChiSquare Tests a 12 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .10. r Asymp. Sig. Value Of (2sided) ~earson ChiSquare 12.222(a) 6 .057 Likelihood Ratio 14.140 6 .028 LinearbyLinear .034 1 .855 Association N of Valid Cases 10 0 · . q12a * q10b ChiSquare Tests a 15 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .10.  Asymp. Sig. Value Of (2sided) pearson ChiSquare 16.000(a) 8 .042 Likelihood ~atio 12.955 8 .113 LinearbyLlnear .610 1 .435 Association N of Valid Cases 10 0 · . q10b * q1a ChiSquare Tests a 10 cells ( p count less than 5. The minimum expected count is .10.  Asymp. Sig. Value Of (2sidedf ~rson ChiSquare 10.000(a) 4 .040 LikelihOod ~atio 6.502 4 .165 Linearbylinear 5.976 1 .015 Association N of Valid Cases 10 100.0% have ex ected · . 12 q10b * q7 ChiSquare Tests a 10 cells (100.0%) have expected count less than 5. The minimum expected count is .50. Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.000(a) 4 .040 Likelihood Ratio 13.863 4 .008 LinearbyLinear 1.098 1 Association .295 N of Valid Cases 10 . . q10b * q9a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 14.600(a) 8 .067 Likelihood Ratio 12.137 8 .145 LinearbyLinear 2.831 1 .092 Association N of Valid Cases 10 a 15 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q10a * q1d ChiSquare Tests Asymp. Sig. Value df (2sidedl Pearson ChiSquare 27.333(a) 15 .026 Likelihood Ratio 20.593 15 .150 LinearbyLinear .064 1 .800 Association N of Valid Cases 10 a 24 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q9m * q9a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 25.575(a) 12 .012 Likelihood Ratio 17.251 12 .140 LinearbyLinear 8.412 1 .004 Association N of Valid Cases 11 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. 126 q9m * q9b ChiSquare Tests a 15 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 15.016(a) 8 .059 Likelihood Ratio 10.740 8 .217 LinearbyLinear Association 6.587 1 .010 N of Valid Cases 11 · . q9m * q9d ChiSquare Tests a 25 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 27.5OO(a) 16 .036 Likelihood Ratio 21.209 16 .171 LinearbyLinear 4.891 1 .027 Association N of Valid Cases 11 · . q9m * qge ChiSquare Tests a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 28.111 (a) 12 .005 Likelihood Ratio 21.888 12 .039 LinearbyLinear 5.521 1 .019 Association N of Valid Cases 11 · . q9m * q9g ChiSquare Tests a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .10. Asymp. Sig. Value Of (2sidedf Pearson ChiSquare 22.889(a) 12 .029 Likelihood Ratio 15.727 12 .204 LinearbyLinear 3.960 1 .047 Association N of Valid Cases 10 · . 127 q9m * q91 ChiSquare Tests Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value 19.250(a) 15.664 5.620 11 df 12 12 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q91 * q9a ChiSquare Tests Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value df 16.913(a) 13.799 5.162 11 9 9 1 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q91 * q9b ChiSquare Tests Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value 12.833(a) 8.875 4.360 11 df 66 1 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q91 * q9f ChiSquare Tests a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Pearson ChiSquare Likelihood Ratio LinearbyLinear Association N of Valid Cases Value 19.250(a) 16.710 .652 11 df 12 12 1 128 q9k * q9d ChiSquare Tests a 20 cells (100.0 Yo) have expected count less than 5. The minImum expected count is .18. Asymp. Sig. Value Of (2sided) pearson ChiSquare 21.389(a) 12 .045 Likelihood Ratio 21.888 12 .039 LinearbyLinear 5.614 1 .018 Association N of Valid Cases 11 0 . . q9k * q9j ChiSquare Tests a 16 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .18. Asymp. Sig. Value Of (2sided)" pearson ChiSquare 21.389(a) 9 .011 Likelihood Ratio 21.888 9 .009 LinearbyLinear Association 6.986 1 .008 N of Valid Cases 11 0 · . q9j * q1e ChiSquare Tests a 12 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .27. .. Asymp. Sig. Value Of (2sided) pearson ChiSquare 12.222(a) 6 .057 Likelihood Ratio 14.112 6 .028 LinearbyLinear 4.607 1 .032 Association N of Valid Cases 11 0 · . q9j * q9a ChiSquare Tests a 16 cells (100.0 Yo) have expected count less than 5. The minimum expected count is .09.  Asymp. Sig. Value Of (2sidedf '"Pearson ChiSquare 17.188(a) 9 .046 Likelihood Ratio 13.432 9 .144 LinearbyLinear 4.206 1 .040 Association N of Valid Cases 11 0 · . J2 ChiSquare Tests q9i * q9c a 25 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value df l2sided) Pearson ChiSquare 23.681 (a) 16 .097 Likelihood Ratio 21.888 16 .147 LinearbyLinear .900 Association 1 .343 N of Valid Cases 11 . . q9h * q1a ChiSquare Tests Asymp. Sig. Value df l2sided) Pearson ChiSquare 11.000{a) 4 .027 Likelihood Ratio 6.702 4 .152 LinearbyLinear 2.815 1 .093 Association N of Valid Cases 11 a 10 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q9g * q1a ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 10.000{a) 3 .019 Likelihood Ratio 6.502 3 .090 LinearbyLinear .455 1 .500 Association N of Valid Cases 10 a 8 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q9g * q1e ChiSquare Tests Asymp. Sig. Value df l2sided) Pearson ChiSquare 14.444{a) 6 .025 Likelihood Ratio 16.774 6 .010 LinearbyLinear 5.432 1 .020 Association N of Valid Cases 10 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .20. ChiSquare Tests ChiSquare Tests ChiSquare Tests 131 ChiSquare Tests a 10 cells (100.0%) have expected countless than 5. The minimum expected count is .45• a 16 cells (100.0%) have expected count less than 5. The minimum expected count Is .09. qge * q9a Asymp. ~\g. Value Of 12sided Pearson ChiSquare 16.885(a) 9 .051 Likelihood Ratio 12.247 9 .200 LinearbyLinear Association 5.935 1 .015 N of Valid Cases 11 a 16 cells (100.0%) have expected countless than 5. The minimum expected count is .10. Asymp. Sig. Value Of 12sidedl Pearson ChiSquare 8.983(a) 4 .062 Likelihood Ratio 12.386 4 .015 LinearbyLinear .774 1 .379 Association N of Valid Cases 11 . . q9f * q2 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .10. q9g * qge Asymp. Sig. Value Of 12~sidedl Pearson ChiSquare 16.400(a) 9 .059 Likelihood Ratio 12.816 9 .171 LinearbyLinear 6.848 1 .009 Association N of Valid Cases 10 q9g * q9b Asymp. Sig. Value Of (2sidedl Pearson ChiSquare 12.286(a) 6 .056 Likelihood Ratio 9.306 6 .157 LinearbyLinear 5.998 1 .014 Association N of Valid Cases 10 .. qge * q9b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 11.698(a) 6 .069 Likelihood Ratio 7.324 6 .292 LinearbyLinear 7.900 1 .005 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. qge * q9d ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 27.133(a) 12 .007 Likelihood Ratio 21.750 12 .040 LinearbyLinear 7.621 1 Association .006 N of Valid Cases 11 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q9d * q9b ChiSquare Tests Asymp. Sig. Value df (2sidedf Pearson ChiSquare 15.452(a) 8 .051 Likelihood Ratio 11.648 8 .168 LinearbyLinear 7.519 1 .006 Association N of Valid Cases 11 a 15 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q9c * q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 22.000(a) 12 .038 Likelihood Ratio 16.710 12 .161 LinearbyUnear .303 1 .582 Association N of Valid Cases 11 a 20 cells (100.0%) have expected count less than 5. The minimum expected count is .09. 132 q9b * q9a ChiSquare Tests a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 15.714(a) 6 .015 Likelihood Ratio 12.189 6 .058 LinearbyLinear Association 7.060 1 .008 N of Valid Cases 11 .. q7 * q1c ChiSquare Tests a 6 cells (100.0%) have expected count less than 5. The minimum expected count is .45. Asymp. Sig. Value Of (2sided) Pearson ChiSquare 5.622(a) 2 .060 Likelihood Ratio 7.520 2 .023 LinearbyLinear .014 1 .905 Association N of Valid Cases 11 .. q1e * q1c ChiSquare Tests Asymp. Sig. Value Of (2sided) Pearson ChiSquare 8.800(a) 4 .066 Likelihood Ratio 10.660 4 .031 LinearbyLinear .011 1 .915 Association N of Valid Cases 11 a 9 cells (100.0%) have expected count less than 5. The minimum expected count is .27. q1e*q1d ChiSquare Tests Asymp. Sig. Value Of (2sidedl Pearson ChiSquare 11.000(a) 6 .088 Likelihood Ratio 15.158 6 .019 LinearbyLinear 2.143 1 .143 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .27. q1d * q1b ChiSquare Tests Asymp. Sig. Value df (2sided\ Pearson ChiSquare 22.688(a) 9 .007 Likelihood Ratio 14.076 9 .120 LinearbyLinear .365 1 Association .546 N of Valid Cases 11 a 16 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q1d*q1c ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 15.125(a) 6 .019 Likelihood Ratio 11.844 6· .066 LinearbyLinear 1.077 1 .299 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q1c*q1b ChiSquare Tests Asymp. Sig. Value df (2sided) Pearson ChiSquare 13.406(a) 6 .037 Likelihood Ratio 9.577 6 .144 LinearbyLinear .579 1 .447 Association N of Valid Cases 11 a 12 cells (100.0%) have expected count less than 5. The minimum expected count is .09. q1b * q1a ChiSquare Tests Asymp. Sig. Value df (2sided\' Pearson ChiSquare 11.000(a) 3 .012 Likelihood Ratio 6.702 3 .082 LinearbyLinear 5.147 1 .023 Association N of Valid Cases 11 a 7 cells (87.5%) have expected count less than 5. The minimum expected count is .09. 134 Appendix B Statically Insignificant Cross Tabulations General Survey Question Chlsquared value Year In School (a11) • a1a 0.730 Year In School (a11) • a1c 0.143 Year In School (a11) • a1 e 0.215 Year In School (a11) • q2a 0.128 Year In School (q11) • a2c 0.479 Year In School (a11) • a2f 0.138 Year In School (a11) • a2Q 0.609 Year In School (a11) • a2i 0.379 Year In School (a11) • a3a 0.546 Year In School (a11) • a3b 0.679 Year In School (a11) • a3c 0.719 Year In School (q11) • q3d 0.261 Year In School (a11) • a3e 0.175 Year In School (a11) • q4 0.806 Year In School (a11) • a7 0.408 AQe (q10) • q1C 0.273 Aae (a10) • a2c 0.160 AQe (q10) • q2f 0.921 Age (q10) • q2g 0.376 Aae (a10\' a2i 0.131 AQe (a10)' 03a 0.428 Aae (a10\' a3b 0.961 Ace (a10)' a3c 0.842 Age (q10)' q3d 0.397 Age (q10) • 03e 0.196 Aae (a10)' a4 0.182 AQe (a10) • a7 0.857 Gender (a9)' a1d 0.112 Gender (q9) • a2a 0.271 Gender (09) • a2b 0.455 Gender (a9) • a2c 0.736 Gender (a9) • a2f 0.133 Gender (a9) • 020 0.901 Gender (a9) • a3a 0.592 Gender (a9) • a3b 0.377 Gender (09) • 03c 0.394 Gender (a9) • a3d 0.409 Gender (q9) • q4 0.309 Question Chisquared value Gender (a9) • a5 0.515 Department (q8) • a1a 0.690 Department (a8) • a1c 0.515 Department (a8)' a1e 0.311 Department (a8) • q2a 0.666 Department (a8)' 02b 0.196 Department (a8) • a2f 0.320 Department (a8) • 020 0.324 Department (a8) • a3a 0.569 Department (a8) • 03b 1.000 Department (a8) • a3c 0.433 Department (08) • a3d 0.199 Department (08) • a4 0.710 Department (q8) • a5 0.490 Department (a8) • a7 0.208 a1a' a5 0.860 a5 • a1b 0.552 q5 • a1c 0.721 q5' a1d 0.498 as' a1e 0.931 as' a2a 0.384 as' a2c 0.778 as' a2d 0.445 q5' 02e 0.579 05' a2f 0.257 as' a2Q 0.120 a5' a2h 0.648 a5' a3a 0.229 as' a3b 0.804 as' a3d 0.600 q5' q3e 0.903 a4' a1a 0.163 a4' a1b 0.768 a4' a1c 0.615 04' q1d 0.345 a4' a1e 0.506 q4' q2b 0.522 a4' a2c 0.813 136 Question Chlsquared value 04· a2d 0.441 a4· a2e 0.891 a4· q2f 0.977 q4· a2Q 0.725 a4· q2h 0.269 q4· a2i 0.270 q4· a3b 0.585 a4· a3d 0.929 04· a3e 0.876 a3e * a2c 0.254 03e * a2a 0.928 a3e * a3d 0.261 a3d * Q1a 0.645 a3d * a1b 0.314 a3d * a1c 0.558 a3d * a1d 0.228 a3d * Q1e 0.440 a3d * a2b 0.754 a3d * Q2c 0.258 a3d * a2d 0.112 a3d * a2a 0.111 a3d * Q21 ~ . 0.118 a3d * a3b 0.420 a3c * Q1a 0.429 a3c*a1b 0.174 a3c * a1d 0.246 a3c * a1e . 0.966 a3c * a2a 0.183 a3c * a3b 0.481 q3b· a1a 0.957 q3b· Q1b 0.282 a3b· Q1c 0.586 a3b· a1d 0.564 q3b • a1e 0.803 q3b· a2g 0.286 a3a * Q1a 0.991 a3a • a1b 0.545 a3a· Q1c 0.298 Question Chi· squared value a3a· q1d 0.412 q3a· Q1e 0.679 a3a· a2e 0.139 q2i * q1a 0.231 q2i· q1b 0.607 q2i • q1c 0.744 q2i· q1e 0.661 a2a * a1a 0.340 a2q * a1b 0.976 a2a * a1c 0.607 a2q * a1d 0.706 Q2q· Q1e 0.450 a2a * a2d 0.174 a2f*a1a 0.444 Q2f*Q1b 0.833 a2f * a1c 0.308 Q2e· Q1a 0.952 a2e*a1c 0.131 Q2d * Qlb 0.162 a2d * ald 0.188 Q2c * a1a 0.316 Q2c * Q1b 0.257 a2c * a1d 0.326 Q2c· a1e 0.304 a2b * ala 0.127 a2b * alb 0.216 Q2b· a1c 0.775 Q2b * 01d 0.331 a2a*a1b 0.663 02a * 01c 0.911 Q2a * old 0.802 q7· q1a 0.431 Q7·01b 0.401 a7 * a1c 0.735 07 * a1d 0.141 07 * a1e 0.201 07 * 02b 0.543 07 * 02c 0.762 137 Question Chisquared value 07· a2d 0.219 07· a2f 0.168 07· q2q 0.363 07·02i 0.235 07· q3a 0.116 07· a3b 0.119 07· a3c 0.205 07· a3d 0.321 07' a3e 0.701 q7' 04 0.697 07· q5 0.893 138 Environmental Soil Science Survey  Chi· Question squared value Year in School (018) • ala 0.690 Year in School (018)· alb 0.171 Year in School (018) * old 0.125 Year in School (018) * ale \ 0.695 Year in School (018) * 02 0.108 Year in School (018) * 07 0.264 Year in School (018)' a9a 0.255 Year in School (018)· 09c 0.167 Year in School (018) * a9d 0.277 Year in School (018) * 0ge 0.695 Year in School (018) * 09t 0.301 Year in School (q18) * q9g 0.844 Year in School (018) * 09h 0.352 Year in School (018)' 09i 0.697 Year in School (q18) • q9j 0.353 Year in School (018)' 09k 0.617 Year in School (q18) * 091 0.645 Year in School (q18) * q9m 0.411 Year in School (018) * al0a 0.371 Year in School (q18) * 010b 0.793 Year In School (018) * 012a 0.797 Year in School (018) * a12b 0.287 Year in School (018) * a12c 0.215 Year in School (018) * 012d 0.287 Year in School (018) * 013 0.228 Year in School (q18) * q14 0.353 Year in School (q18) * Gender (016) 0.264 Year in School (018)' Aqe (017) 0.238 Aqe (017) * ala 0.774 Aae (017) * alc 0.156 Aoe (q17) * ale 0.753 AQe (017) • 02 /" 0.405 Aoe (017) • 07 0.753 Ace (017) * 09a 0.412 Aoe (017) * q9b 0.283 Aoe (q17) * 09d 0.209 Aqe (017) * 09t 0.773 Age (017) * q9g 0.879 Age (017) * 09h 0.558 Chi· Question squared value Ace (017) * 09i 0.419 Age (017) * 091 0.350 Aoe (017) * aSk 0.122 Aqe (017) * 091 0.710 Age (017) * q9m 0.213 Age (q17) * ql0a 0.182 Age (q17)' a10b 0.777 Aae (017) ·012a 0.744 Age (017) * 012b 0.910 Aae (017) * 012c 0.689 Aoe (017\* 012d 0.910 Age (017) * 013 0.357 Aae (017\ * 014 0.475 Age (017) * Gender (016\ 0.362 Gender (016\ • 01a 0.292 Gender (016\ • alb 0.370 Gender (016) • 01c 0.368 Gender (016) • old 0.469 Gender (016) • ale 0.282 Gender (016\ • 02 1.000 Gender (016) • 09a 0.333 Gender (016) • 09b 0.490 Gender (016) * 09c 0.406 Gender (016) • Q9d 0.506 Gender (016\* 0ge 0.766 Gender (016) • 09f 0.343 Gender (016) • 09a 0.487 Gender (016) * a9i 0.504 Gender (016) * q9j 0.343 Gender (016) * Q9k 0.572 Gender (016) * a9I 1.000 Gender (016) * a9m 0.572 Gender (016) • olDa 0.549 Gender (016\* olOb 0.308 Gender (016) * 012a 0.513 Gender (016) * 012b 0.333 Gender (016) * 012c 0.572 Gender (016) * 012d 0.333 Gender (016) * 013 0.615 139 Chi Question squared valued Gender la16) * a14 0.753 a14*ala 0.774 a14*alb 0.892 014"01e 0.887 014"qld 0.766 q14"019 0.233 014 * 02 0.405 014" q7 0.753 a14*09a 0.385 q14*a9b 0.103 a14 * 0ge 0.215 q14 * q9d 0.182 q14 * age 0.179 a14 * q9f 0.437 q14 * a90 0.154 014 * q9h 0.572 a14 * q9i 0.357 014 * a9i 0.228 a14 * q91 0.370 014 * 010a 0.902 014 * al0b 0.777 014 * 012a 0.887 q14  012b 0.412 014 * 012e 0.509 014  012d 0.412 014 * q13 0.394 a13 * ala 0.628 a13*01b 0.163 a13*alc 0.211 01301d 0.154 013*01e 0.895 013 * a2 0.349 q13 * 07 0.323 a13*a9a 0.425 q13 * 09b 0.349 a13 * age 0.260 q13*09d 0.555 q13*age 0.495 q13 * 09f 0.273 Chi Question squared valued q13 * q90 0.857 a13 * a9h 0.648 013 * q9i 0.389 013 * q9j 0.385 a13 * a9k 0.345 q13 * q91 0.167 q13 * 09m 0.736 q13 * al0a 0.133 013 * 010b 0.599 q13 * a12a 0.352 q13 * q12b 0.371 013 * a12c 0.543 q13*q12d 0.371 a12d * ala 0.574 q12dqlb 0.715 q12d  qlc 0.263 q12d * old 0.596 a12d * ale 0.231 q12d * q2 0.392 q12d * q9a 0.541 a12d * a9b 0.270 q12d*q9c 0.619 a12d * a9d 0.115 q12d*qge 0.612 q12d * 091 0.288 a12d * a9a 0.487 q12d * a9h 0.749 q12d*q9i 0.425 a12d * 09j 0.370 q12d * 09k 0.632 q12d*a91 0.263 012d * q9m 0.298 a12d * 010a 0.783 012d * ql0b 0.238 012c * ola 0.644 012c * olb 0.834 012c * ole 0.396 012e * old 0.740 a12c*ale 0.378 140 Chl Question squared I valued a12c * a2 0.290 a12c * a7 0.261 o12c*a9a 0.298 a12c * 09b ~ 0.414 o12c * age 0.724 o12e * age 0.321 o12c * a9f 0.350 a12c * 09g 0.663 o12c * 09h 0.689 a12c * a9i 0.375 a12c * q9j 0.233 a12e * 09k 0.350 a12c * 091 i, 0.333 a12c * q9m I 0.501 a12c * 010a 0.407 a12c * 010b 0.150 a12b * a1a 0.435 a12b * a1b 0.619 a12b * 01e I 0.263 a12b * a1d L 0.731 a12b*a1e 0.231 a12b * a9a 0.541 a12b * 09b 0.270 a12b * age ~ 0.846 a12b * age 0.213 a12b * a9! 0.160 a12b * a9a n 0.487 a12b * 09h I, 0.749 a12b * a9i 0.893 a12b * 09i 0.370 a12b * q9k 0.632 a12b * 091 " 0.579 q12b * 09m 0.257 a12b * 010a 0.794 Q12b * q10b , 0.238 a12b * 012a 0.225 Q12a * a1a 0.690 q128 * 01b 0.702 012a * a1c i 0.504 Chi· Question squared valued Q12a * Q1d 0.399 a12a * a1a 0.827 012a*02 0.435 Q12a * a7 0.368 a12a*09a 0.675 a12a· a9b 0.788 Q12a·09c 0.442 a12a· a9d , 0.301 a12a * age 0.406 a12a*a9g 0.844 a12a· a9h 0.604 a12a· a9l • 0.265 a12a*a9j 0.580 012a*a9k 0.475 a12a*a91 0.212 a128 * a9m 0.898 a12a· a1Da 0.132 a10b· a1b 0.234 a1Ob· a1c 0.417 a1Ob· a1d 0.535 a10b * a1e 0.617 a1(lb * Q2 0.287 a10b * a9b 0.369 a10b * a9c 0.543 a10b * a9d 0.446 Q10b * qge 0.408 Q10b * Q9f 0.487 a10b * a9a 0.213 a10b * a9h " 0.375 a10b * a9! 0.465 a10b * a91 0.106 a10b * a9k 0.394 a10b * a91 0.248 a1Ob* a9m 0.191 Q10b * a1Da 0.412 Q1Da· Q1a 0.893 a1Da * a1b 0.124 Q1Da * Q1c 0.185 Q1Da * Qle 0.419 141 Chi· Question squared valued a10a*a2 0.323 a10a * a7 0.549 a10a * a9a 0.485 a10a * q9b 0.543 a10a * age 0.158 a10a * a9d 0.673 a10a*age 0.419 a10a * a91 0.497 a10a * a9g 0.668 a10a * a9h 0.538 a10a * a91 0.158 a10a * a9i 0.545 a10a * a9k 0.451 a1Ga * a91 0.132 a10a * a9m 0.793 a9m * a1a I 0.292 a9m*a1b 0.880 a9m· a1e 0.846 a9m * a1d 0.835 a9m * a1e 0.319 a9m * a2 0.462 a9m * a7 0.569 a9m * aSC 0.272 a9m * a9f 0.350 a9m * a9h 0.398 a9m * a91 0.729 a9m * a91 0.140 a9m * a9k 0.278 a91 * a1a 0.588 a91*a1b 0.509 a91 • a1e i 0.272 a91 • a1d 0.465 a91 * a1e 0.233 a91* q2 0.588 a91* a7 0.268 a91* a9c ' . 0.434 a91* a9d 0.302 a91* age .' ( 0.106 a91* a9g i 0.107 Chi· Question squared valued a91* a9h 0.490 a91* a91 0.211 a91* a9i 0.326 q91* a9k 0.382 a9k • a1a 0.402 a9k • a1b 0.422 a9k * a1e 0.200 a9k * a1d 0.369 a9k * a1e 0.251 a9k * a2 0.821 a9k * a7 0.402 a9k * a9a 0.422 a9k * a9b 0.189 a9k * age 0.297 a9k *age 0.188 a9k * a9f 0.490 a9k * a9a 0.427 a9k *a9h 0.145 a9k * a9i 0.428 a9i * a1a • 0.402 a91*a1b 0.650 a9i * a1e 0.131 a9i * a1d 0.276 a9i * a2 0.662 a9j * a7 0.233 a9i * a9b 0.299 a9i * aSC 0.484 a9j * a9d 0.173 a91*age 0.459 a9j * a9f 0.542 a9i * a90 0.350 a91* a9h 0.307 a9j * a9i 0.609 a91 * a1a 0.292 a91*a1b , 0.200 a9i * a1e 0.517 a9i * a1d 0.211 a91 *a1e 0.247 a91 * a2 0.811 ChIQueallon aquared velued 09;" 07 0.462 091" a9a 0.553 091" a9b 0.751 091 "Q9d 0.560 a9i" age 0.488 a9i" Q9t 0.629 a9;" Q9Q 0.191 a9;" a9h 0.378 a9h" Qlb 0.169 a9h" ale 0.381 a9h" Qld 0.452 a9h" ale 0.122 a9h" Q2 0.462 a9h" Q7  0.462 a9h" a9a ~ 0.282 a9h" Q9b 0.366 a9h" Qge 0.241 a9h" a9d 0.696 a9h" Qge 0.550 a9h" a9t 0.227 a9h' Q90 ~ 0.116 a90" alb 0.198 ago' Qle o.en a9a" old 0.350 a90 "Q2 0.528 a9a" 07 0.469 Q90 "Q9a 0.178 a9a" Qge 0.596 Q90" 09d 0.226 a90" Q9t 0.273 Q9t" Qla 0.750 Q9t"alb 0.679 a9t" Qle 0.374 Q9t" old 0.609 091" Qle 0.488 a9t "07 0.688 a9l' Q9a 0.258 091" a9b 0.153 091' a9c 0.586 ChIQuestion aqU8Rd valued 091" Q9d 0.252 Q9I" Qge 0.198 0ge" ala 0.402 Qge "Qlb 0.789 age "Qle 0.590 age" Old 0.660 Qge "Qle 0.148 age" 02 0.693 Qge" Q7 0.693 age" a9c 0.467 Q9d"Qla 0.750 a9d" alb 0.843 Q9d "Qle 0.558 a9d" old I 0.765 Q9d "Qle 0.319 Q9d' Cl2 0.514 09d' 07 0.514 Q9d'age 0.120 Q9d"a9c 0.414 a9c "Qla 0.292 a9c'Qle 0.105 a9c" old 0.135 a9c' Qle 0.488 a9c" Cl2 0.688 a9c" a7 0.688 a9c"age 0.452 a9c'a9b 0.797 a9b'ala 0.730 a9b" alb 0.686 a9b" ale 0.338 a9b" aId 0.651 a9b' ale 0.299 a9b" Cl2 0.280 a9b" a7 0.497 a9a "ala 0.724 age' alb 0.878 a9a" ale 0.333 age 'aId 0.718 age "al. 0.408 143 Question Chisquared valued 0.371 0.547 0.251 0.329 0.402 0.176 0.122 0.338 0.402 0.273 0.520 0.676 0.231 0.481 0.821 0.632 144 Appendix C Transcript from Focus Group Transcript from Focus Group Transcript for the focus group held February 15, 2006 in the Jesse Knight Building on BYU campus. The group was moderated by Bryce Youngquist. Participation by: Jenny Cox, Blaine Bateman, Russell Memory, and Baxter Oliphant. Those speaking will be referred to by their initials accordingly. BY: Welcome to the focus group everyone. First we'll start with Blaine. Say your name, how old you are, where are you from, what's your major, hobbies. BB: My name's Blaine Bateman. I'm 22 years old, I'm a sophomore here at BYU, I come from Calleville, WA. And my hobbies are golf, fishing, boating, baseball, that sort of stuff. My major is landscape management. JC: My name is Jenny Cox, I'm from Orem, UT. I'm a senior in Environmental Soil Science. And I like to read and do outdoorsy type stuff, ski and hike. BO: My name is Baxter Oliphant, I am a senior majoring in political science. I'm 24 years old. I'm from Arlington, VA, outside of DC, therefore I am into politics, I'm a news junkie. I like to read, write, and to study, I'm one of those losers. RM: My name is Russell Memory, I'm an exercise science major. I'm a senior. Like Baxter I'm also from VA. My hobbies include running and working out and I like to play the guitar. BY: Let me introduce myself, I'm Bryce Youngquist, I'm a business management marketing emphasis major. I'm from Minneapolis, MN. Welcome to the focus group. We want open ideas. We want to hear the positives and the negatives. We want to understand what make you tick. Let's get a discussion going and see what you have to say and go from there. Let us know your feelings. Tell us what's on your mind. Don't worry about hurting our feelings. Russell, how did you select your major? RM: I started out up at Ricks College my freshman year, I was a sports medicine major. I liked to work out. I didn't know quite what I wanted to do with that major. So I went on my mission, I decided to be a business major. I took some business classes, after a while I decided I couldn't stand the business classes. Then I bounced around from major to major and didn't know what I wanted to do as a career. I'm going to study what I like to do. And will see what I do for a profession later. Study what I'm interested in. We'll see how it works out in the end. But I'll study what I'm interested in. BY: Anyone can answer. Anything else? Is it all interests? How did you pick your major? BB: I think a lot of people pick their major by how much they're going to make later. I know deciding for me was affected by what the pay is later. What's the payoff for what I'm studying later. BO: I think everybody considers that, but I don't know if everyone makes a decision based on that. A lot of people do. I'm doing political science, which is useless, but I really like it. I know I'll have to go to graduate school. There are times I wished I was doing something that's a little more practical, something that I wouldn't have to go to graduate school. But I'm still I'm doing what I love. 146 JC: It was pretty hard for me. There are a lot of things I like to do, but cience intere t m th m t. And I took bunch of classes and that helped out. RM: I also was going to add. One of the reasons why I was reluctant to elect a major, b au e 1didn't want to limit myself in my profession, whatever I wanted to do in the future. But like Baxter aid, a bachelor's degree doesn't have to limit what you do later. Exercise science is not a big money maker by itself, you have to go to graduate school. Like what Baxter said, it's something I enjoy doing. BY: Now we kind of touched on this, what specific elements do you look for in a major, what' in tore later, job potential? BO: If I can do it, I didn't think of physics, I can't do physics. I picked the major I good do well at. Sometime I could get A's and do well and enjoy it at the same time. BY: Do you or others do you think choose a major even though the c1as e aren't that iting, but ba. d more on what the end result is? BO: I think some people are better than I at seeing the end result. They know they hav to tak hard, basic courses. I know some guys really into physics that do that, they don't njoy th ut they're willing to put up with the tough classes for the end re ult . They can ee p t that. BY: Jenny, taking your classes in your major, did that affect you? JC: It has so far. I've enjoyed the classes so far, they're not alway th mo t \tlO ju llik with any other major. I kind of like having the major small, then you ha e the maH clas ire. Th re'. lik I r 5 in my classes. BY: And political science classes ... ? BO: Once you get to the upper division classes they get down to 30 or o. BY: Now, how many times have you switched your major 0 far? BB: Oh, I don't know, maybe 5 or 6 times. JC: I was actually a music major for three years. I only witched m maj other majors. BO: I've switched my major officially three times. But in my plan pr babl a ul7 r tim . I started with international studies, then econ, some political cience, th n did ome ngli h tuf, lh n 1 went back to political science. And that's the degree that I'm ending up with. RM: I started out with sports medicine, then went business, and no I m e different majors, but in my mind I would take different clas es in a cordan them out. So I probably switched mentally 5 or 6 times. rei ith h k BY: Now Jenny, did you take different classes while you were a mu i m j r1 JC: Yeah, I took a lot of chemistry, some soil science, some biolo BY: Anything about professors, did that change anyone' major? 147 BO: Well, it never changed my major, but it reinforced my decision in a major. I got to know some professors, then got into some interesting classes, it got me to settle down. BY: Any professors ruined it for you? BO: I took an econ class and I didn't like the professor very much. I just didn't like how it was setup. Not the students, but just the environment, the attitude. It wasn't terrible, I didn't hate it. I realized it wasn't for me it wasn't where I wanted to be. RM: I think it wasn't the professor, it was just the atmosphere and the class. I considered accounting for a while. I enjoy accounting. We'd sit there in class, even the accounting majors would come in and would bag on their major. "Yeah, we have the most boring major out there ..." There wasn't a very enthusiastic feeling in the room. The teacher was great, I still like accounting. Maybe it was just the atmosphere that turned me off. BY: So, in general, would just being around the majors or being in the building, not that it would decide your major, but would that help inform you and help you decide? BB: I think being around the people who are taking those classes or those that are in their last semester and just being around them, seeing how they feel, see what they say. How do they feel now that they're almost done. What their experience was. BY: Do you like the Widstoe building? IC: It's kind of old, kind of junky, it's got a homely feel. BY: Ok, now let's go through your future career plans? BB: My future career plans are to graduate from BYU and then go get a masters in landscape architecture somewhere. Then work for myself or for another firm designing parks or golf courses, neighborhoods. stuff like that. BY: So your future plans really influenced your decision on a major? BB: Yeah, I thought I wanted to do landscape architecture. I talked to some people. And I talked to some professors. And I liked landscape architecture and I knew landscape management was a good way to prepare for a masters degree in landscape architecture. I like being outside, I grew up on a farm. You can run your own business or work for someone. IC: Future plans, it's kind of up in the air. No one really knows. I'd like to be a professor or work for the bureau of land management. BO: My plan is to go to law school. And possibly get a joint ID/PhD. But that's a lot of school. But I want to get involved with politics, public policy and legal issues, social debate. All those things somehow. RM: I would like to go into something health related. Podiatry, physician's assistant, physical therapist, something like that. I still haven't ruled out business, even though I still don't enjoy the classes. My roommate's dad was a zoology major and was in dental school, realized he didn't like, backed out and got 148 his MBA and now he works for circuit city. So he's actually a real big motivation to me 10 gel a "deadend" bachelor degree. Because there really are no "deadend" bachelor degrees. BY: Alright, this kind of leads to my next question, would you all agree you ju t need to get a bachelor degree? BO: It's important to do something you like. But also, you just can't pick anything. There are some majors that really are pointless, or are very hard to do something with. Like a music dance thealer major for example, I don't how much writing and reading they learn how to do. I've found it important; it's not necessarily the subjects I learn, but the skills I have learned. I've learned how to read and write well and to think for myself. And so that had to be in the major, so if I would have picked interpretive dance, I wouldn't have learned that. RM: On the other hand though, sometimes these graduate schools do like the "deadend" majors. They like variety and diversity. Because in law school, if everyone's political science, history, or Engli h majors. Then there's a stigma attached to the lawyers. They're looking for people who are accounting majors or business majors. I have some friends who are Chinese majors going to medical school. That's why they make you take the prerequisites to go to these graduate programs, to prove that you can learn what they are going to teach you. Within reason though. There are majors that maybe they truly are deadend, but basically what people are looking for is that you're able to learn and complete a task, regardless of what that task is. BO: But I've talked to some people that have taken the "deadend" majors and they fell like it' been a real disadvantage to them. Because then they get into the graduate program and find them elves behind the curve. I know someone that went to law school, after being an econ major, but he really !ruggled to keep up with the writing. That's why it's important to keep in mind the field you want 10 go into. You can't say, "I want learn calculus inside and out," then go to law school. It will help you of course, it' not a dead end, but you also need to keep in mind the skills you want 10 learn. RM: I think that's where a minor comes in hand. BY: Now, I want you guys to think of words or characteristics that make majors attractive? RM: Interesting. BB: I really think the paycheck plays a big role. JC: The doability. If you really stink at something, you probably shouldn't get into it. BY: So we have interesting, doability, paycheck... BO: I would say one of the things is " 



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