USING ORDINAL REGRESSION MODELING TOEVALUATE THE SATISFACTION OF JOMO KENYATTAUNIVERSITY OF AGRICULTURE AND TECHNOLOGY

FACULTY OF SCIENCE STUDENTS

OMBUI G. MONARI

A RESEARCH PROJECT REPORT SUBMITTED TO THESTATISTICS AND ACTUARIAL SCIENCES

DEPARTMENT IN PARTIAL FULFILLMENT OF THEDEGREE OF POSTGRADUATE DIPLOMA IN APPLIED

STATISTICS

APRIL, 2010

DeclarationI hereby declare that this is my original work and has not been presentedanywhere in any other university or institution either in whole or in part foraward of any degree, fellowship or any other similar title whatsoever.

Ombui M. Geofrey

Signature.................. Date ...............

This project report has been submitted for examination with my approvalas the supervisor.

Dr.Gichuhi A. Waititu

Signature.................. Date ...............

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ACKNOWLEDGMENTS

Thanks to Dr. Gichuhi A. Waititu who has been my supervisor and mentorthroughout my lifetime in the JKUAT Actuarial and Statistics department.He has encouraged me, and had faith in me; and has been available at anytime that I needed his advice and knowledge. I could not have hoped fora more supportive supervisor. I would also like to extend my thanks to allthe members of the JKUAT Actuarial and Statistics department who haveall helped and encouraged me during my time at JKUAT University in thedepartment.

I would also like to thank Dr. S. M. Mwalili of JKUAT Actuarial andStatistics department.He has provided me with amazing support, encourage-ment and numerous discussions about the issues discussed in this project.

Finally, a very special thank you to my parents, brothers and sisters whohave supported me throughout my academic studies from day one at school.I cannot thank them enough for the support, love and encouragement andfinancial assistance for this project.

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Abstract

General students’ satisfaction of Jomo Kenyatta University of Agricultureand Technology (J.K.U.A.T) faculty of science students is associated witha combination of predictor variables that are a mixture of qualitative andquantitative categories. Ordinal regression statistical technique was used tomodel the relationship between the academic programs, facilities and ser-vices and the outcome variable to determine the explanatory variables thatinfluence students’ satisfaction factors that will assist us advice the facultyadministrators on future service delivery improvements. Data descriptionwas done using the frequency tables and interactive graphs while data analy-sis was done using fitting statistics; information fitting that checks the pres-ence of a relationship between the dependent variable and combination ofindependent variables, goodness of fit that gives the information about howmany predicted cell frequencies differ from the observed frequencies, param-eter of estimates that determines the factors that influence satisfaction andthe test of parallel lines assumption that makes judgment concerning themodel adequacy. The factors that we found to influence the satisfaction ofthe J.K.U.A.T faculty of science were four, namely, Service delivery at thedepartment office, the library services, accommodation facilities inside theuniversity hostels and the accommodation facilities outside the university.

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Contents

1 INTRODUCTION AND LITERATURE REVIEW 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Statement of the problem . . . . . . . . . . . . . . . . . . . . 41.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.5 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.6 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 MATERIALS AND METHOD 62.1 The model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 62.1.2 Definition of terms and concepts . . . . . . . . . . . . . 62.1.3 Ordinal Regression Model . . . . . . . . . . . . . . . . 7

2.2 Link function . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.1 The Complementary Log-log link function. . . . . . . . 9

2.3 The assumptions . . . . . . . . . . . . . . . . . . . . . . . . . 102.4 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5 Data questionnaire . . . . . . . . . . . . . . . . . . . . . . . . 112.6 Data sample and data analysis software package . . . . . . . . 11

3 RESULTS 123.1 Data description . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4 CONCLUSION AND RECOMMENDATIONS 514.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 53

A Appendix I 56

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List of Figures

3.1 Year of study . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Program of study . . . . . . . . . . . . . . . . . . . . . . . . . 153.3 market demand . . . . . . . . . . . . . . . . . . . . . . . . . . 173.4 service delivery at the faculty office . . . . . . . . . . . . . . . 193.5 relationship between students and faculty sub-ordinate staff . 213.6 course promotion . . . . . . . . . . . . . . . . . . . . . . . . . 233.7 computer skills . . . . . . . . . . . . . . . . . . . . . . . . . . 253.8 admission and registration process . . . . . . . . . . . . . . . . 273.9 library services . . . . . . . . . . . . . . . . . . . . . . . . . . 293.10 career counseling services . . . . . . . . . . . . . . . . . . . . . 313.11 JKUAT hospital facilities . . . . . . . . . . . . . . . . . . . . . 333.12 accommodation facilities inside JKUAT . . . . . . . . . . . . . 353.13 internet facilities in JKUAT . . . . . . . . . . . . . . . . . . . 373.14 inter-departmental sports events . . . . . . . . . . . . . . . . . 39

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List of Tables

2.1 The five link functions . . . . . . . . . . . . . . . . . . . . . . 9

3.1 Departments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.3 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.4 The lecturers’ service delivery . . . . . . . . . . . . . . . . . . 183.5 The Service delivery at the department office . . . . . . . . . . 203.6 The accessibility of the faculty office . . . . . . . . . . . . . . 223.7 The communication skills gained from your course . . . . . . . 243.8 The research skills gained from your course . . . . . . . . . . 263.9 The financial ability to pay school fees and meet personal needs 283.10 The tutorial services offered in your course . . . . . . . . . . . 303.11 The classroom facilities . . . . . . . . . . . . . . . . . . . . . . 323.12 The laboratory facilities . . . . . . . . . . . . . . . . . . . . . 343.13 The accommodation facilities outside JKUAT . . . . . . . . . 363.14 The student center faculties in JKUAT . . . . . . . . . . . . . 383.15 The general faculty of science service delivery . . . . . . . . . 403.16 Observed distribution of general question, participation by de-

partment, gender and program of study . . . . . . . . . . . . . 413.17 Model Fitting Information . . . . . . . . . . . . . . . . . . . . 423.18 goodness-of-Fit . . . . . . . . . . . . . . . . . . . . . . . . . . 423.19 Pseudo R-Square . . . . . . . . . . . . . . . . . . . . . . . . . 433.20 Parameter estimates for Clog-log logistic regression . . . . . . 483.21 Test of parallel lines . . . . . . . . . . . . . . . . . . . . . . . 50

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Chapter 1

INTRODUCTION ANDLITERATURE REVIEW

1.1 Introduction

Students have always been investigated every end of semester using a twentyseven questions questionnaire with gender, year of study, unit code, lec-turer’s main name and programs of study measured using a nominal scalewhile course objectives given at the beginning of the course, description ofcourse outline, appropriateness of course objectives, relevance of given refer-ence materials,course coverage/completion, attendance of all schedule classes,lecturer punctuality, lecturer duration, lecturer delivery as per the content,use of examples and illustrations, use of teaching aids, communication skills,notes/materials/handouts given, presentation sequence, students participa-tion, lecturer’s motivation of students, availability of lecturer for consulta-tion, lecturer’s mastery of content, administration of CAT’s and assignmentsas scheduled, feedback on CATs and assignments(revision of cats)and lastlyrelevance of CATs and assignments/labwork in relation to course outline ismeasured using a five point ordinal scale to rate lecturers’ per unit as thestudents have needs and rights to participate in quality and satisfactory ser-vices. The survey reflects key issues as perceived by the Faculty of Scienceadministrators to timely plan for quality services majorly from lecturers tostudents.

This research study was used to analyzed the students satisfaction factorsusing ordinal regression statistical technique. Frequency tables and interac-tive graphs were applied to detect the satisfaction factors regarding academicprograms, facilities and services. Ordinal regression method was useful in an-

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alyzing the relationship between multiple explanatory variables and outcomevariable.

In the study we investigated the following factors in relation to studentssatisfaction : department, year of study, gender and Program of study weremeasured using a nominal scale while age was measured using scale and theindependent variables course satisfaction in reference to the market demand,lecturers’ service delivery, Service delivery at the faculty office, Service deliv-ery at the department office, Relationship between students and the facultysub-ordinate, accessibility of the faculty office, course promotion by the fac-ulty, communication skills gained from your course, computer skills gainedfrom your course, research skills gained from your course, Admission andregistration process in reference to time, Financial ability to pay school feesand meet your needs, library services, tutorial services in your course, careercounseling services in the faculty, lecture room facilities, JKUAT hospitalfacilities, course laboratory facilities, accommodation facilities in JKUAT,accommodation facilities outside JKUAT, JKUAT internet facilities, stu-dent center facilities, faculty inter-departmental sports events and lastly thedependent variable generally on faculty of science service delivery that weremeasured using a five scale ordinal scale in Statistical Package for the SocialSciences (SPSS). The statistical regression technique was used to model thesatisfaction of sampled students from the Faculty of Science using ordinalregression to identify explanatory variables related to academic programs,facilities and services that contribute to the overall Students satisfaction.

1.2 Literature Review

Students end of semester questionnaires evaluate the students’ satisfactionon faculty of science programs only from the lecturers’ delivery point of view.To evaluate general students’ satisfaction factors and determine the studentssatisfaction ratings on factors that influence their satisfaction in campus us-ing ordinal regression model.

To obtain various satisfaction ratings, different statistical methods suchas descriptive statistics and ordinal regression techniques were used to ana-lyze student satisfaction questionnaires. Descriptive statistics has extensivelybeen used to detect the satisfactory items that students have experiencedfrom their programs, facilities and services.

For instance, the mean responses of student satisfaction survey conducted

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by Noel-Levitz Company revealed community college student satisfaction.The survey respondents rated highest satisfaction on responsiveness to di-verse populations, registration effectiveness, and academic services, whilerating the lowest satisfaction on admissions and financial aid, academic ad-vising, and campus support services, [3].

Using percentages, means, modes, and qualitative written reports, stu-dent satisfaction with the quality curriculum content, faculty involvement,support service, facilities and recreation was rated using a five point ordinalscale of ’very satisfactory’, ’satisfactory’, ’average ’, ’ unsatisfactory’ , ’veryunsatisfactory’. The rating depicted factors that influenced satisfaction, [4], [10].

Chi-square and linear regression techniques has been utilized to deter-mine the association between the explanatory variables and the performance.Cross tabulation and chi-square techniques were used,[2] to predict collegestudent retention based on satisfaction. A strong relationship between stu-dent satisfaction and retention found on 40 of the 68 questions was 59%.

Using linear regression and decision tree analysis with the chi-squaredautomatic interaction detector (CHAID) software program, a study by [8]compared student satisfaction responses between academically and non aca-demically oriented student groups. The research results demonstrated thatfaculty preparedness, social integration, and pre-enrollment opinions emergedas the most important variables contributing to student satisfaction for bothgroups.

Linear regression methods were used to investigate the relationship be-tween student satisfaction and medical school learning environment, [7]. Thestudy results provided evidence that curriculum structures, (e.g., timely feed-back and promotion of critical thinking) were prominent explanatory vari-ables.

Using a multilevel modeling technique to analyze survey data, a studyby [9] examined the impact that different departments have on student sat-isfaction in a large research university. The research finding revealed thatcharacteristics of departments such as size, faculty contact with students, re-search emphasis, and proportion of female students had a significant impacton education satisfaction within major.

Utilizing an ordinal regression model, a newly implemented study,[5] was

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used to estimating the probabilities of the four ordinal categories (”worse”,”can’t tell”, ”better”, and ”much better”) of client improvement in a counsel-ing center. The research findings showed that the five explanatory variablessignificantly associated with the probability of an outcome category. Thesevariables included previous experience as a client; readiness to change; levelof symptomatic and interpersonal distress; pre-counseling clinical status; andthe number of counseling sessions in which a client might be involved.

Based on the literature review, one might conclude that descriptive statis-tics (e.g., means, percentages, and frequency counts), chi-square (e.g., cross-tabulation, Pearson’s chi-square test, decision tree with CHAIDS softwareprogram), linear regression, and multilevel modeling approaches were increas-ingly utilized to study student satisfaction in relation to various explanatoryvariables. However, compared to these study , the ordinal regression methodseems to be the most suitable and practical techniques to analyze the effectsof multiple explanatory variables on the ordinal outcome that cannot be as-sumed as continuous measure and normal distribution.

Researchers do not need to alter an ordinal outcome as binary or dichoto-mous measure for logistic regression analysis, which may lead to the loss ofinherent information. The ordinal regression analysis is currently underusedin the field of education, several articles were found in the medical field,which illustrated the foundation of the mathematical model.

1.3 Statement of the problem

The challenge facing the administrators is managing students’ satisfaction.

1.4 Objectives

General objectiveTo investigate which explanatory factors influence students’ satisfaction.Specific objectives

1. To model the satisfaction of sampled students from the Faculty of Sci-ence using ordinal regression.

2. To provide timely advice to administrators hence ensuring faculty con-tinuity and timely development and modification of student facilities,programs and receive quality services from the staff.

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1.5 Hypothesis

Student satisfaction is influenced by the explanatory variables: department,year of study, gender, Program of study, age, course satisfaction in refer-ence to the market demand, lecturers’ service delivery, and Service deliveryat the faculty office, Service delivery at the department office, Relationshipbetween students and the faculty sub-ordinate, accessibility of the facultyoffice, course promotion by the faculty, communication skills gained fromyour course, computer skills gained from your course, research skills gainedfrom your course, Admission and registration process in reference to time,Financial ability to pay school fees and meet your needs, library services,tutorial services in your course, career counseling services in the faculty,lecture room facilities, JKUAT hospital facilities, course laboratory facili-ties, accommodation , facilities in JKUAT, accommodation facilities outsideJKUAT, JKUAT internet facilities, student center facilities and faculty inter-departmental sports events.

1.6 Justification

End of semester questionnaires have always measured students solely on thestudents’ satisfaction from the lecturer point of view. Thus the need to modelthe general students’ satisfaction using ordinal regression.

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Chapter 2

MATERIALS AND METHOD

2.1 The model

2.1.1 Introduction

Ordinal regression technique was used to model the behavior of dependentvariable with a set of independent variables. In ordinal regression, the de-pendent variable is the order response category variable and the independentvariable was a mixture of qualitative and quantitative variables.

2.1.2 Definition of terms and concepts

Dependent variable: The dependent variable is ordinal. The first categorywas considered as the lowest category and the last category was consideredas the highest category.Covariate: Covariates are continuous independent variables e.g. age.Factor: Factor is a categorically independent variable that must be codedas numeric in SPSS e.g. department.Complimentary Log-log (Clog-log)link function: clog-log link functionwas used to predict the dependent variable category. Clog-log link functionwas used in SPSS statistical package for ordinal regression modeling becausethe data that was gathered for analysis had dependent ordinal variable withequal category. The clog-log link function is of the form

f (X) = log (−log (1−X)) (2.1)

Since the ordinal clog-log model is non linear, transformation was done onthe dependent variable, to make the model linear. Clog-log link function isnatural log of the odds and is good at linearizing the model. The technique

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uses maximum likelihood, thus more cases than a similar Ordinary LeastSquares model (OLS) was required. The odds ratio was generated by theclog-log, then probabilities were predicted from the model.

2.1.3 Ordinal Regression Model

Generalized linear model is a very powerful class of model, which can beused to provide solutions to a wide range of statistical questions. The basicform of a generalized linear model is shown in the following equation.

link (γij) = θj − [β1Xi1 + β2Xi2 + .........+ βKXiK ] (2.2)

where, link() -is the link functionγij -is the cumulative probability for the jth category for the ith caseθj - is the threshold for the jth categoryk - is the number of regression coefficientsβ1....βk - are the regression coefficientsXi1....XiK are values of the predictors for the ith caseThe important things to note on the generalized linear model are:

• The model is based on the notion that there is some latent continuousoutcome variable, and that the ordinal outcome variable arises fromdiscretizing the underlying continuum into ordered groups. The cutoffvalues that define the categories are estimated by the thresholds. Insome cases, there is good theoretical justification for assuming such anunderlying distribution. However, even in cases in which there is notheoretical concept that links to the latent variable, the model can stillperform quite well and give valid results.

• The thresholds or constants in the model (corresponding to the in-tercept in linear regression models) depend only on which category’sprobability is being predicted. Values of the predictor (independent)variables do not affect this part of the model.

• The prediction part of the model depends only on the predictors andis independent of the outcome category. These first two propertiesimply that the results were a set of parallel lines or planes-one for eachcategory of the outcome variable.

• Rather than predicting the actual cumulative probabilities, the modelpredicts a function of those values. This function is called the linkfunction, and you choose the form of the link function when you buildthe model. This allows you to choose a link function based on theproblem under consideration to optimize your results.

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Three major components in ordinal regression model are:Location component. The portion of the equation 2.2 includes the coeffi-cients and predictor variables, is called the location component of the model.The location is the ”meat” of the model. It uses the predictor variablesto calculate predicted probabilities of membership in the categories for eachcase.Scale component.The scale component is an optional modification to thebasic model to account for differences in variability for different values of thepredictor variables. For example, if certain groups have more variability thanothers in their ratings, using a scale component to account for this improvedthe model. The model with a scale component follows the form shown in 2.3.

link (γj) =θj − [β1X1 + β2X2 + .........+ βKXK ]

exp (τ1z1 + τ2z2 + ..........+ τmzm)(2.3)

where,τ1...τm are coefficients for the scale componentz1....zm are m predictor variables for the scale component.

2.2 Link function

The link function is a transformation of the cumulative probabilities thatallows estimation of the model.It defines what goes to the left side of theequation. It’s the link between the random component on the left side of theequation and the systematic component on the right.

Five link functions are available in the Ordinal Regression procedure. Thefollowing are the link functions , form and typical application.

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Function Form Typical application

Logit log(

γ1−γ

)evenly distributed categories

CLog-log log (−log (1− γ)) higher categories more probableNegative log-log −log (−log (γ)) lower categories more probableprobit φ−1 (γ) latent variable is normally distributedCauchit (inverse cauchy) tan (π (γ − 0.5)) latent variable has many extreme values

Table 2.1: The five link functions

In ordinal regression analysis, we used the Clog-log link function to buildour model that is generally suitable for analyzing the ordered categoricaldata with higher categories more probable among all categories.

2.2.1 The Complementary Log-log link function.

The Clog-log link function in ordinal regression modeling was used in ana-lyzing the data is written in the form.

f (X) = log (−log (1−X)) (2.4)

It is not a typo that there is a minus sign before the coefficients for thepredictor variables, instead of the customary plus sign.

Each Clog-log has its own θj term but the same coefficient β. That meansthat the effect of the independent variable is the same for different Clog-logfunctions. That’s an assumption that one has to check. That’s also thereason the model is also called the proportional odds model. The θj terms,called the threshold values, often are not of much interest. Their values donot depend on the values of the independent variable for a particular case.They are like the intercept in a linear regression, except that each Clog-loghas its own. They are used in the calculations of predicted values.

The coefficients in the Ordinal regression model depicts how much theClog-log changes based on the values of the predictor variables. Parameterestimates from the output of SPSS computation where a table called ’param-eter estimates’ appears were analyzed.

parameter estimates table: a variable named location variable gavethe coefficient for the independent variable for the specified link function inordinal regression.

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Factor Summary: Factor summary depicts that the general questionordinal scale distribution in percentage on respondents.

Model Fitting Information: checks the presence of a relationship be-tween the dependent variable and combination of independent variables wasbased on the statistical significance of the final model.

Goodness of fit information: Pearson chi-square test that gives theinformation about how many predicted cell frequencies differ from observedfrequencies.

R-square estimate: One cannot use simple r-square in ordinal regres-sion when modeling categorical data.

Test of Parallel Lines: Test of parallel lines was designed to makejudgment concerning the model adequacy.

2.3 The assumptions

1. parallel lines. One of the assumptions underlying ordinal regression isthat the relationship between each pair of outcome groups is the same.In other words, ordinal regression assumes that the coefficients thatdescribe the relationship between, say, the lowest versus all higher cat-egories of the response variable are the same as those that describe therelationship between the next lowest category and all higher categories,etc. This is called the proportional odds assumption or the parallelregression assumption. Because the relationship between all pairs ofgroups is the same, there is only one set of coefficients. Thus, in orderto asses the appropriateness of our model, we evaluated whether theproportional odds assumption is tenable. Statistical tests are availablein Statistical Package for the Social Sciences (SPSS) software version12.0. However, these tests have been criticized for having a tendencyto reject the null hypothesis (that the sets of coefficients are the same),and hence, indicate that there the parallel slopes assumption does nothold, in cases where the assumption does hold, [6].

2. Adequate cell count: As per the rule of thumb, 80 % of cells musthave more than 5 counts. No cell should have Zero count as it isconsidered as a missing value and excluded from the study. the largepercentage of cells with missing data could lead to a decrease of actual

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sample size from the model construction or an inaccurate Chi-squaretest for the model fitting, since the model goodness-of-fit is usually de-pendent of chi-square test result. The chi-square test normally dependson the sample size. Hence, if the number of cells with a zero value islarge, the chi-square goodness of fit statistics may not be appropriate[1].

2.4 Data collection

The random sample size was one hundred and twenty five students from thefaculty of science from each of the eight departments in the faculty of scienceat equal capacity. The sample of the students was not Gender bias andfrom any year of study however, more fourth years were sampled from eitherprogram of study i.e Government Sponsored (GOK) or Alternative DegreeProgram (ADP). Gender, department, year of study and program of studywere measure using a nominal scale as follows Department 1 = ’Physics’,2 = ’ Medical Microbiology ’, 3 = ’Zoology’, 4 = ’Statistics and ActuarialSciences’, 5 = ’Pure and Applied Mathematics’, 6 = ’Biochemistry’, 7 =’Chemistry’ and 8 = ’Botany’, Year of study 1 = ’First year’, 2 = ’Secondyear’, 3 = ’Third year’ and 4 = ’Fourth year’, Gender 0 = ’Female’ and 1= ’Male’, Program of study 0 = ’GOK’ and 1 = ’ADP’ and Age using scalewhile a five point ordinal scale was used in rating the students satisfactionlevels with 5 = ’Very satisfactory’, 4 = ’satisfactory’, 3 = ’Average’, 2 =’unsatisfactory’ and 1 = ’Very satisfactory’ for the rest of the explanatoryvariables in SPSS

2.5 Data questionnaire

The questionnaire that was used to collect the data is attached in the ap-pendix.

2.6 Data sample and data analysis software

package

The data sample was one hundred and twenty five students from the facultyof science and Statistical Package for the Social Sciences (SPSS) softwareversion 12.0 was used to analyze the data.

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Chapter 3

RESULTS

3.1 Data description

DepartmentThe data was collected from the eight faculty of science departments and therespondents per department were as follows; fifteen from the department ofPhysics, fourteen from the department of Medical Microbiology, fifteen fromthe department of Zoology, seventeen from the department of Statistics andActuarial sciences, twenty one from the department of Pure and AppliedMathematics, sixteen from the department of Biochemistry, thirteen fromthe department of Chemistry and finally fourteen from the department ofBotany. The questionnaires were administered at random thus the differencein proportions per department.

Table 3.1: Departments

Department Count Percentage(%)Physics 15 12.0%Medical Microbiology 14 11.2%Zoology 15 12.0%Statistics and Actuarial Sciences 17 13.6%Pure and Applied Mathematics 21 16.8%Biochemistry 16 12.8%Chemistry 13 10.4%Botany 14 11.2%Total 125 100.0%

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Figure 3.1: Year of study

The respondents’ counts from different years of study were as follows;53.6% were fourth years, 39.2% was distributed between the second andthird years while first years were 7.2%. Majority of the respondents werefourth year students as they were thought to have more experience on theuniversity operations, compared to the other years’ students like first yearswho may have not even known where their departments offices.

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GenderThe frequency of the of the interviewees with respect to their gender wereforty nine and seventy six female and male respectively i.e. 39.2% and 60.8%female and male respectively. There are more male to female respondentsthat reflect the sex ratio of female to male in the faculty.

Table 3.2: Gender

Gender Count Percentage(%)Female 49 39.2%Male 76 60.8%Total 125 100.0%

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Figure 3.2: Program of study

Program of studyThe interviewees were from the Self Sponsored students (ADP) and theGovernment of Kenya sponsored students (GOK) programs. There were41.6% interviewees from GOK and 58.4% from the ADP program of study.The ADP respondents are more than the GOK respondents because somedepartments like Statistics and Actuarial Sciences many ADP students.

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AgeThe respondents were spread in age from seventeen to thirty three years ofage as follows; twenty one to twenty four years of age had 78.4% respon-dents, seventeen to twenty years of age and 11.2% repondents and 10%of therepondents were between twenty five years of age thirty three years of age.

Table 3.3: Age

Respondents’ Age Count Percentage(%)17 1 0.8%19 3 2.4%20 10 8.0%21 20 16.0%22 24 19.2%23 34 27.2%24 20 16.0%25 6 4.8%26 1 0.8%27 2 1.6%28 1 0.8%29 1 0 .8%30 1 0.8%33 1 0.8%Total 125 100.0%

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Figure 3.3: market demand

The course satisfaction in reference to the market demand was as follows;80.8% rated average and satisfactory, 12% were either very unsatisfactory orunsatisfactory and 7.2% rated very satisfactory.

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Lecturers’ serviceThe lecturer’s service delivery had its frequency as follows; average had high-est count at sixty one, followed by satisfactory at a frequency of fifty three,then unsatisfactory at a count of seven and finally very satisfactory and veryunsatisfactory at a count of two respectively.

Table 3.4: The lecturers’ service delivery

Lecturers’ service Count Percentage(%)delivery categoriesvery unsatisfactory 2 1.6%Unsatisfactory 7 5.6%Average 61 48.8%Satisfactory 53 42.4%very satisfactory 2 1.6%Total 125 100.0%

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Figure 3.4: service delivery at the faculty office

The count of service delivery at the faculty office was as follows; averagehad highest count at fifty one, followed by satisfactory at a frequency of thirtythree, then unsatisfactory at a count of twenty three, very unsatisfactory ata count of thirteen and finally very satisfactory at a count of five respondentsrespectively.

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Service delivery at the departmentThe service delivery at the department office count was as follows; satisfac-tory had highest count at forty eight, followed by average at a frequencyof thirty eight, then unsatisfactory at a count of nineteen, very satisfactoryat a frequency of fifteen and finally very unsatisfactory at a count of fiverespondents.

Table 3.5: The Service delivery at the department office

Service delivery at the Count Percentage(%)department categoriesvery unsatisfactory 5 4.0%Unsatisfactory 19 15.2%Average 38 30.4%Satisfactory 48 38.4%very satisfactory 15 12.0%Total 125 100.0%

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Figure 3.5: relationship between students and faculty sub-ordinate staff

The count of the relationship between students and faculty sub-ordinatestaff was recorded as follows; average had highest frequency at fifty six, fol-lowed by satisfactory at a frequency of thirty four, then unsatisfactory at afrequency of twenty one, very unsatisfactory at a count of nine and finallyvery satisfactory at a count of five respondents respectively.

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Faculty accessibilityThe accessibility of the faculty office frequency was as follows; average hadhighest count at fifty six, followed by satisfactory at a frequency of thirtyseven, then unsatisfactory at a count of fifteen, very satisfactory at a fre-quency of nine and finally very unsatisfactory at a count of eight respondentsrespectively.

Table 3.6: The accessibility of the faculty office

Faculty accessibility Count Percentage(%)categoriesvery unsatisfactory 8 6.4%Unsatisfactory 15 12.0%Average 56 44.8%Satisfactory 37 29.6%very satisfactory 9 7.2%Total 125 100.0%

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Figure 3.6: course promotion

The course promotion by the faculty was rated by the interviewees withhighest frequency at average of fifty eight, unsatisfactory at a frequency oftwenty seven, satisfactory at a count of twenty, then very satisfactory ateleven and lastly very unsatisfactory at nine.

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Communication skillsThe communication skills gained from the respective respondents’ course wasrated with highest frequency at satisfactory of forty eight, average at thefrequency of forty five respondents, unsatisfactory at a frequency of twentythree, very unsatisfactory at a count of six and finally very satisfactory atthree.

Table 3.7: The communication skills gained from your course

Communication skills Count Percentage(%)gained categoriesvery unsatisfactory 6 4.8%Unsatisfactory 23 18.4%Average 45 36.0%Satisfactory 48 38.4%very satisfactory 3 2.4%Total 125 100.0%

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Figure 3.7: computer skills

The counts on the computer skills gained from the respective courses ofthe interviewees was rated with highest count of respondents rating averageat forty four, very unsatisfactory was second with thirty respondents, unsat-isfactory third with twenty six respondents, satisfactory fourth with twentythree respondents and finally very unsatisfactory with only two respondents.

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Research skillsThe research skills gained from the respondents’ course was rated as followswith satisfactory being rated highest with a frequency of forty interviewees,thirty eight average, twenty one very unsatisfactory, nineteen unsatisfactoryand lastly seven very satisfactory.

Table 3.8: The research skills gained from your course

Research skills Count Percentage(%)gained categoriesvery unsatisfactory 21 16.8%Unsatisfactory 19 15.2%Average 38 30.4%Satisfactory 40 32.0%very satisfactory 7 5.6%Total 125 100.0%

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Figure 3.8: admission and registration process

The counts on the admission and registration process in reference to timewas rated with highest frequency of respondents rating average at fifty three,unsatisfactory was second with thirty six respondents, then satisfactory wasthird with twenty one respondents, very unsatisfactory fourth with elevenrespondents and lastly very satisfactory with only four respondents.

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Financial ability to pay school fees and meet personal needsThe financial ability to pay school fees and meet personal needs by the re-spective respondents was rated with highest frequency of respondents ratingaverage at forty three, unsatisfactory was second at thirty, then satisfactorywas third with twenty six respondents, very unsatisfactory fourth with twentythree respondents and finally very satisfactory with just three respondents.

Table 3.9: The financial ability to pay school fees and meet personal needs

Financial ability to pay Count Percentage(%)school fees and meetpersonal needs categoriesvery unsatisfactory 23 18.4%Unsatisfactory 30 24.0%Average 43 34.4%Satisfactory 26 20.8%very satisfactory 3 2.4%Total 125 100.0%

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Figure 3.9: library services

The respondents frequencies on the rates of the library services was asfollows; highest frequency of respondents rated average at fifty, unsatisfactorywas second at thirty, then followed closely by satisfactory at twenty eightrespondents, very unsatisfactory fourth with fifteen respondents and finallyvery satisfactory with only two respondents.

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Tutorial servicesThe respondents’ frequencies on the rates of the tutorial services with respectto their respective courses average had highest frequency of respondents atforty seven, unsatisfactory was second at thirty nine, then followed by veryunsatisfactory at twenty two interviewees, satisfactory was rated fourth withthirteen respondents and lastly very satisfactory with four respondents.

Table 3.10: The tutorial services offered in your course

Tutorial services Count Percentage(%)categoriesvery unsatisfactory 22 17.6%unsatisfactory 39 31.2%average 47 37.6%satisfactory 13 10.4%very satisfactory 4 3.2%Total 125 100.0%

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Figure 3.10: career counseling services

The respondents rating frequencies on the career counseling services inthe faculty with respect to their courses had highest frequency of respondentsat average with thirty nine, unsatisfactory recorded a frequency of thirtyfour, then followed closely by very unsatisfactory at thirty one respondents,satisfactory fourteen respondents and finally very satisfactory with sevenrespondents.

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Classroom facilitiesThe respondents rates on classroom facilities had frequencies as follows; high-est frequency of respondents rated average at forty eight , unsatisfactorysecond with a frequency of thirty seven, then followed by satisfactory attwenty four respondents, very unsatisfactory ten respondents and lastly verysatisfactory with six respondents.

Table 3.11: The classroom facilities

Classroom facilities Count Percentage(%)categoriesvery unsatisfactory 10 8.0%Unsatisfactory 37 29.6%Average 48 38.4%Satisfactory 24 19.2%very satisfactory 6 4.8%Total 125 100.0%

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Figure 3.11: JKUAT hospital facilities

The JKUAT hospital facilities rates by respondents were as follows; high-est count of respondents were average at sixty one, unsatisfactory at twentythree, satisfactory eighteen respondents, then followed closely by very unsat-isfactory at sixteen, and finally very satisfactory with seven respondents.

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Laboratory facilitiesThe JKUAT laboratory facilities with respect to the respondent courses rateswere as follows; highest count of respondents were unsatisfactory at fortyfour, followed by average at thirty eight, satisfactory twenty two respondents,then very unsatisfactory at sixteen, and finally very satisfactory with fiverespondents.

Table 3.12: The laboratory facilities

Laboratory facilities Count Percentage(%)categoriesvery unsatisfactory 16 12.8%Unsatisfactory 44 35.2%Average 38 30.4%Satisfactory 22 17.6%very satisfactory 5 4.0%Total 125 100.0%

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Figure 3.12: accommodation facilities inside JKUAT

The accommodation facilities inside JKUAT rates by GOK students wereas follows; highest frequency of respondents was twenty six at average, fol-lowed by satisfactory at twelve, unsatisfactory eight interviewees, then verysatisfactory at five, very unsatisfactory with one respondent and finally notapplicable as rated by the ADP students was at seventy three.

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Accommodation outside JKUATThe accommodation facilities outside JKUAT rates as rated by ADP studentswere as follows; highest frequency of respondents were average at thirty six,twelve unsatisfactory, followed closely by ten interviewees satisfactory andnine interviewees very unsatisfactory, then very satisfactory with six respon-dents and finally fifty two respondents not applicable that as rated by theGOK students.

Table 3.13: The accommodation facilities outside JKUAT

Accommodation outside Count Percentage(%)JKUAT categoriesvery unsatisfactory 9 7.2%Unsatisfactory 12 9.6%Average 36 28.8%Satisfactory 10 8.0%very satisfactory 6 4.8%not applicable 52 41.6%Total 125 100.0%

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Figure 3.13: internet facilities in JKUAT

The internet facilities in JKUAT were rated by the interviewees as followswith highest frequency of respondents unsatisfactory with counts at forty five,followed by thirty seven very unsatisfactory respondents, thirty two respon-dents’ average, eight interviewees satisfactory and lastly three respondentsvery satisfactory.

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Student centerThe student center facilities were rated by the respondents as follows withhighest frequency of respondents unsatisfactory with frequency of fifty three,followed by forty two respondents very unsatisfactory, twenty one intervie-wees average, seven respondents satisfactory and finally two respondents verysatisfactory.

Table 3.14: The student center faculties in JKUAT

Student center Count Percentage(%)categoriesvery unsatisfactory 42 33.6%Unsatisfactory 53 42.4%Average 21 16.8%Satisfactory 7 5.6%very satisfactory 2 1.6%Total 125 100.0%

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Figure 3.14: inter-departmental sports events

The frequencies for the faculty inter-departmental sports events wererated average at fifty, followed by thirty seven interviewees unsatisfactory,twenty respondents very unsatisfactory, then fifteen respondents rated satis-factory and finally three interviewees rated it very satisfactory.

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General faculty of science service deliveryThe counts generally on the faculty of science service delivery were ratedby the interviewees were sixty two respondents average, twenty seven re-spondents satisfactory, twenty four interviewees unsatisfactory, followed byseven respondents very unsatisfactory and eventually five respondents verysatisfactory.

Table 3.15: The general faculty of science service delivery

General faculty of science Count Percentage(%)service delivery categoriesvery unsatisfactory 7 5.6%Unsatisfactory 24 19.2%Average 62 49.6%Satisfactory 27 21.6%very satisfactory 5 4.0%Total 125 100.0%

3.2 Data analysis

Factors’ SummaryThis implies that over 75% of the students are above averagely satisfied ofthese 25% are either satisfactory or very satisfactory, but 25% are below av-erage satisfaction.Overall all departments participated in equal capacity.

The ratio of female to male respondents was approximately 39% to 61%.The big difference depicts the general female to male ratio in the university.

The factor program of study had Government of Kenya (GOK) spon-sored students at 41.6% and Alternative Degree Program (ADP) students at58.4% the difference in respondents in percentages was because some of thedepartments like Statistics and Actuarial Sciences have more ADP students.

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Table 3.16: Observed distribution of general question, participation by de-partment, gender and program of study

Variables n PercentagesGeneral question Very unsatisfactory 7 5.6%

unsatisfactory 24 19.2%average 62 49.6%satisfactory 27 21.6%Very satisfactory 5 4.0%

Department Physics 15 12.0 %Medical Micro-Biology 14 11.2%Zoology 15 12.0 %Statistics and Actuarial Sciences 17 13.6%Pure and Applied Mathematics 21 16.8%Biochemistry 16 12.8%Chemistry 13 10.4%Botany 14 11.2%

Gender female 49 39.2%Male 76 60.8%

Program of study GOK 52 41.6%ADP 73 58.4%

Valid 125 100.0%Missing 0Total 125

Missing data variables were truncated hence the model has no missingvariables.

Model Fitting InformationThe results from model fitting in the section provide results of ordinal logisticregression versus reduced model(intercept) with complimentary log-log linkfunction. The presence of a relationship between the dependent variable andcombination of independent variables is based on the statistical significanceof the final model. From Table 1.17, the -2LL of the model with only inter-cept is 321.455 while the -2LL of the model with intercept and independentvariables is 0.000. That is the difference (Chi-square statistics) is 321.455-0.000 = 321.455 which is significant at 0.05 since P=0.000<0.05. We canconclude that there is the association between the dependent and indepen-dent variable(s)in complimentary Log-log link function

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Table 3.17: Model Fitting Information

Model -2Log Likelihood Chi-Square df Sig.Reduced 321.455Final 0.000 321.455 34 0.000

Goodness-of-FitThe table tests for consistency between the observed data and the fittedmodel. The null hypothesis states that the observed data are consistent withthe fitted model. The null hypothesis is accepted and one concludes that theobserved data were consistent with the estimated values in the fitted modelsince the P-value was insignificance p=1.00>0.05. Using complementary Log-log Link function.

Table 3.18: goodness-of-Fit

Measure Chi-Square df Sig.Pearson 299.192 462 1.00Deviance 238.120 462 1.00

Pseudo R-SquareIn ordinal regression models, these measures were based on likelihood ratiosrather than raw residuals. There are several measures intended to mimic theR-squared analysis, but none of them are an R-squared. The interpretation isnot the same, but they can be interpreted as an approximate variance in theoutcome. The three different methods were used to estimate the coefficientof determination.McFadden’s r-squared (McFadden, 1974) is based onthe log-likelihood kernels for the intercept-only model and the full estimatedmodel.Cox and Snell’s r-squared (Cox and Snell, 1989)is a generalizationof the usual measure designed to apply when maximum likelihood estimationis used, as with ordinal regression. However, with categorical outcomes, ithas a theoretical maximum value of less than 1.0. For this reason, Nagelk-erke (Nagelkerke, 1991) proposed a modification that allows the index totake values in the full zero-to-one range. From ”Model Fitting Information”table McFadden R2 (aka pseudo R2) is Pseudo R2 = Model L2/DEV0 =321.455/321.455 = 1.000. using CLog-log link function.

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Table 3.19: Pseudo R-Square

Cox and Snell 0.924Nagelkerke 1.000McFadden 1.000

Parameter EstimatesEstimateWhile direct interpretation of the coefficients in this model is difficult due tothe nature of the link function, the signs of the coefficients can give impor-tant insights into the effects of the predictors in the model.

Sig.These are the p-values of the coefficients that, within a given model, the nullhypothesis that a particular predictor’s regression coefficient zero given thatthe rest of the predictors variables are in the model.

ThresholdThe response category 1=very unsatisfactory for the general question hadp-value=0.800>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for response category 1 for the general question waszero in the estimation.

The response category 2= unsatisfactory for the general question had p-value=0.270>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for response category 2 for the general question waszero in the estimation.

The response category 3= average for the outcome variable had p-value=0.032<0.05,we reject the null hypothesis and concluded that the regression coefficient forresponse category 3 for the general question was found to be statistically dif-ferent from zero in the estimation.

The response category 4= satisfactory for the general question had p-value=0.006<0.05, reject the null hypothesis and concluded that the regres-sion coefficient for response category 4 for the general question was found tobe statistically different from zero in the estimation.

Location parametersThe predictor age in years had p-value=0.098>0.05, we fail to reject the null

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hypothesis and concluded that the regression coefficient for age in years ofthe respondent was zero in estimating the general question given that theother predictor variables were in the model.

The independent variable reference to market demand had P-value=0.286>0.05, we fail to reject the null hypothesis and concluded that the regressioncoefficient for reference to the market demand of the respondent course waszero in estimating the general question given that the other predictor vari-ables were in the model.

Lecturer service delivery had a P-value=0.249>0.05, we fail to reject thenull hypothesis and concluded that the regression coefficient for lecturer ser-vice delivery to the interviewee was zero in estimating the outcome variablegiven that the other predictor variables are in the model.

The predictor variable service delivery at faculty office to the studentsP-Value=0.033<0.05, we reject the null hypothesis and concluded that theregression coefficient for service delivery at faculty office was found to bestatistically different from zero in estimating the general question given theother independent variable are in the model.

The predictor variable service delivery at department office P-value=0.984> 0.05, we fail to reject the null hypothesis and concluded that the regres-sion coefficient for service delivery at the department office to the studentswas zero in estimating the response variable given that the other predictorvariables are in the model.

Relationship between the students and the faculty subordinate staff hadP-value=0.712>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for relationship between the students and the fac-ulty subordinate staff was zero in estimating the outcome variable given thatthe other predictor variables are in the model.

The predictor variable faculty office’s accessibility P-value=0.880>0.05,we fail to reject the null hypothesis and concluded that the regression co-efficient for faculty office’s accessibility was zero in estimating the generalquestion given that the other predictor variables are in the model.

The predictor variable course promotion p-value=0.112>0.05, we fail toreject the null hypothesis and concluded that the regression coefficient forcourse promotion was zero in estimating the response variable given that the

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other predictor variables are in the model.

Communication skills gained from course of study of the respondent inde-pendent variable had P-value=0.622>0.05, we fail to reject the null hypoth-esis and concluded that the regression coefficient for communication skillsgained from course of study of the respondent was zero in estimating theoutcome variable given that the other predictor variables are in the model.

The predictor variable computer skills gained from course of study of therespondent P-value=0.364>0.05, we fail to reject the null hypothesis and con-cluded that the regression coefficient for computer skills gained from courseof study of the respondent was zero in estimating the response variable giventhat the other predictor variables are in the model.

The predictor variable research skills gained from course of study of therespondent P-value=0.542>0.05, we fail to reject the null hypothesis and con-cluded that the regression coefficient for research skills gained from courseof study of the respondent was zero in estimating the general question giventhat the other predictor variables are in the model.

The predictor variable admission and registration process in reference totime had P-value=0.114>0.05, we fail to reject the null hypothesis and con-cluded that the regression coefficient for admission and registration processin reference to time was zero in estimating the response variable given thatthe other predictor variables are in the model.

Financial ability to pay tuition fees and meet personal needs had P-value=0.130>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for financial ability to pay tuition fees and meetpersonal needs was zero in estimating the outcome variable given that theother predictor variables were in the model.

The predictor library service’s significance P-value=0.030<0.05, we re-ject the null hypothesis and concluded that the regression coefficient for thelibrary service was found to be statistically different from zero in estimatingthe general question given the other independent variables are in the model.

The predictor variable tutorial Service in the respondents’ course of studyp-value=0.652>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for tutorial Service in the respondents’ course ofstudy was zero in estimating the response variable given that the other pre-

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dictor variables are in the model.

The predictor variable career counseling service in the faculty of scienceP-value=0.325>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for career counseling service in the faculty of sci-ence zero in estimating the general question given that the other predictorvariables are in the model.

Classroom facilities had P-value=0.404>0.05, we fail to reject the null hy-pothesis and concluded that the regression coefficient for classroom facilitieswas zero in estimating the outcome variable given that the other predictorvariables are in the model.

The predictor JKUAT hospital facilities P-value=0.668>0.05, we fail toreject the null hypothesis and concluded that the regression coefficient forJKUAT hospital facilities was zero in estimating the response variable giventhat the other predictor variables are in the model.

Course laboratory facilities predictor variable had p-value=0.142>0.05,we fail to reject the null hypothesis and concluded that the regression coef-ficient for respondent course laboratory facilities was zero in estimating theoutcome variable given that the other predictor variables are in the model.

The predictor variable accommodation facilities inside JKUAT hostelsP-value=0.009<0.05,we reject the null hypothesis and concluded that theregression coefficient for accommodation facilities inside JKUAT hostels wasfound to be statistically different from zero in estimating the general questiongiven the other independent variable are in the model.

The predictor variable accommodation facilities outside JKUAT had P-value=0.028<0.05, we reject the null hypothesis and concluded that the re-gression coefficient for accommodation facilities outside JKUAT was foundto be statistically different from zero in estimating the general question giventhe other independent variable are in the model.

The predictor variable internet facilities in JKUAT P-value=0.862>0.05,we fail to reject the null hypothesis and concluded that the regression coef-ficient for internet facilities in JKUAT was zero in estimating the responsevariable given that the other predictor variables are in the model.

Student center facilities had P-value=0.905>0.05, we fail to reject the null

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hypothesis and concluded that the regression coefficient for student centerfacilities was zero in estimating the outcome variable given that the otherpredictor variables are in the model.

Inter-departmental sport events had P-value=0.942>0.05, we fail to rejectthe null hypothesis and concluded that the regression coefficient for inter-departmental sport events was zero in estimating the outcome variable giventhat the other predictor variables were in the model.

The predictor variable year of study of the respondent P-value=0.551>0.05,we fail to reject the null hypothesis and concluded that the regression coef-ficient for year of study independent variable was zero in estimating theresponse variable given that the other predictor variables are in the model.

The predictor variables of the eight departments P-values are 0.897, 0.296,0.610, 0.203, 0.169, 0.886 and 0.261 for Physics, Medical Micro-Biology, Zool-ogy, Statistics and Actuarial Sciences, Pure and Applied Mathematics, Bio-chemistry, Chemistry and Botany respectively on setting alpha level to 0.05,we fail to reject the null hypothesis and concluded that the regression coeffi-cient for the eight departments location variable was zero in estimating thegeneral question in controlling the other predictor variables are in the model.

The interpretation for a dichotomous variable such as gender parallelsthat of a continuous variable: the observed difference on 0=’females’ P-value=0.200>0.05, we fail to reject the null hypothesis and concluded thatthe regression coefficient for the gender independent variable was zero in es-timating the general question in controlling the other predictor variables arein the model.

Program of study predictor variable 0=’GOK’ P-value=0.282>0.05, wefail to reject the null hypothesis and concluded that the regression coeffi-cient for the program of study predictor variable was zero in estimating theoutcome variable in controlling the other independent variables are in themodel.CLog-log link function was used in the estimation of the parameters

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Table 3.20: Parameter estimates for Clog-log logistic regressionParameters Esti- Std. Wald df Sig. 95% Cofide-

mate Error nce IntervalLower UpperBound Bound

Threshold Very unsatisfactory 0.545 2.152 0.064 1 0.800 -3.672 4.763unsatisfactory 2.345 2.125 1.218 1 0.27 -1.820 6.511Average 4.588 2.145 4.575 1 0.032 0.384 8.792satisfactory 6.066 2.200 7.605 1 0.006 1.755 10.377

Location Age in years -0.136 0.082 2.733 1 0.098 -0.0297 0.025Reference to the -0.173 0.162 1.140 1 0.286 -0.491 0.145market demandLecturer service 0.248 0.215 1.329 1 0.249 -0.174 0.670deliveryService delivery 0.391 0.183 4.561 1 0.033 0.032 0.749faculty officeService delivery 0.003 0.156 0.000 1 0.984 -0.303 0.309department officeRelationship 0.081 0.219 0.136 1 0.712 -0.348 0.510between studentsfaculty subor-dinate staffFaculty office -0.029 0.193 0.023 1 0.880 -0.407 0.349accessibilityCourse 0.256 0.162 2.520 1 0.112 -0.060 0.573promotionCommunication -0.090 0.183 0.244 1 0.622 -0.449 0.268skills gainedComputer Skills 0.150 0.165 0.825 1 0.364 -0.173 0.473gainedResearch skills -0.094 0.155 0.373 1 0.542 -0.397 0.209gainedAdmission regi- 0.242 0.153 2.498 1 0.114 -0.058 0.542stration processreference timeFinancial ability -0.205 0.135 2.291 1 0.130 -0.470 0.060to pay tuitionfee and meet needsLibrary service -0.358 0.164 4.730 1 0.030 -0.680 -0.035Tutorial Service 0.066 0.145 0.204 1 0.652 -0.219 0.350in course

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Parameters Esti- Std. Wald df Sig. 95% Cofide-mate Error nce Interval

Lower UpperBound Bound

Location Career counseling 0.143 0.145 0.970 1 0.325 -0.142 0.428servicesClassroom facilities 0.122 0.146 0.697 1 0.404 -0.164 0.409JKUAT hospital -0.065 0.152 0.184 1 0.668 -0.363 0.233facilitiesCourse laboratory 0.220 0.150 2.157 1 0.142 -0.074 0.513facilitiesAccommodation 0.592 0.22 6.773 1 0.009 0.146 1.038facilitiesin JKUATAccommodation 0.334 0.152 4.818 1 0.028 0.036 0.631facilities outsideJKUATInternet faci- -0.032 0.184 0.030 1 0.862 -0.394 0.329lities in JKUATStudent center -0.019 0.161 0.014 1 0.905 -0.334 0.296facilitiesInter-department 0.010 0.141 0.005 1 0.942 -0.267 0.288sportsYear of study 0.114 0.191 0.355 1 0.551 -.261 0.489[Department=1] -0.071 0.549 0.017 1 0.897 -1.146 1.005[Department=2] 0.640 0.612 1.093 1 0.296 -0.560 1.840[Department=3] -0.252 0.494 0.260 1 0.610 -1.220 0.716[Department=4] -0.717 0.563 1.621 1 0.203 -1.821 0.387[Department=5] -0.692 0.503 1.893 1 0.169 -1.678 0.294[Department=6] -0.078 0.541 0.021 1 0.886 -1.137 0.982[Department=7] -0.624 0.554 1.265 1 0.261 -1.710 0.463[Department=9] 0(a) 0. . 0 0. . .[Gender=0] -0.385 0.301 1.640 1 0.200 -0.974 0.204[Gender=1] 0(a) . . 0 . . .[Program of study=0] 0.939 0.873 1.157 1 9.282 -0.772 2.650[Program of study=1] 0(a) . . 0 . . .

a. This parameter is set to zero because it is redundant

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Test of Parallel LinesTest of parallel lines was designed to make judgment concerning the modeladequacy. SPSS tests the proportional odds assumption that is commonlyreferred to as the test of parallel lines. The model null hypothesis statesthat the slope coefficients in the model are the same across the responsecategories. Since the significance P-Value=1.000>0.05 indicated that therewas no significant difference for the corresponding slope coefficients acrossthe response categories, suggesting that the model assumption of parallellines was not violated in the model with the Complementary Log-log link.

Table 3.21: Test of parallel lines

Model -2Log Likelihood Chi-Square df Sig.Null Hypothesis 0.000General 0.000(a) 0.000 102 1.000

1. The log-likelihood value is practically zero. There may be a completeseparation in the data. The maximum likelihood estimates do not exist.

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Chapter 4

CONCLUSION ANDRECOMMENDATIONS

4.1 Conclusion

This project demonstrates the use of ordinal regression statistical techniqueto model students’ satisfaction ratings data.This is a statistical tool that usedwhen the outcome is categorical with a natural ordering. Ordinal regressionallows for predicted probabilities of success to be calculated for each level ofthe response. The data for this project was collected from J.K.U.A.T fac-ulty of science’s one hundred and twenty five students of first to fourth yearpicked at random from the eight departments. The data contains most ofthe student satisfaction evaluations factors.

Clog-log link became the best model based on the screening criteria thecredibility of model assumption, the fitting statistics i.e. fitting Informa-tion, goodness of fit information, and the stability of parameter estimation.Therefore, needless to say, major research findings and implications shouldbe drawn from the best model.

The explanatory variables related to the satisfaction of faculty involve-ment is Service delivery at the department office it was identified in thebest model. Student satisfaction with faculty involvement significantly con-tributes to the probability of students expressing satisfaction with the gen-erally on faculty of science service delivery. It is evident that a the faculty ofscience is one of the largest faculties within the university, thus higher stu-dent satisfaction rating regarding faculty involvement provides compellingevidence that faculty members have played a significant role in creating a

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pleasant environment influenced on student satisfaction for the generally onfaculty of science service delivery.

Furthermore, the library services ware significantly associated with thesatisfaction of generally on faculty of science service delivery. It may provideevidence that improved service delivery at the library has addressed the needsof Faculty of science students and contributed to the fulfillment of universitygoal, e.g., booking of and reserving of library books online a success.

The study suggested that the accommodation facilities inside and outsidethe university that can be improved further by providing services like inter-net in the hostels and provision of free transport to and from the universityfor the students residing outside the university and providing power backupsin the rooms of residence

Overall, this study should be viewed as an important first step for the fac-ulty of science to explore the relationship between the generally on facultyof science service delivery satisfaction and multiple explanatory variablesconcerning academic programs, facilities and services in the faculty. Theknowledge gained from this study would be beneficial to the faculty of sci-ence and its students.

The goal was to obtain information from students to establish the ex-planatory variables that influence satisfaction that could be helpful to deci-sion makers in faculty of science for improving academic programs, facilitiesand services in the faculty. For example the administrators could ensurethat the faculty students could ensure themselves participate in the qualityof academic programs supported by the faculty capacity and facilities andservices. Model assumption of parallel lines was checked to ensure modeladequacy and it was fulfilled by the model, assuring the model goodness offit, fitting Information and parameter estimation stability.

Clearly, the ordinal regression modeling is a unique statistical techniquein that the ordinal outcome variable is frequently encountered in the fieldof educational research and the model assumption of parallel lines is easilyassumed and verified.

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4.2 Recommendations

Further research to be done on the signs of the regression coefficients.

In this study the data collected was from the Jomo Kenyatta Universityof Agriculture and Technology faculty of science only. The questionnaireshould be rolled out to all the JKUAT faculties, schools and institutes.

The same questionnaire or otherwise could be used in other public andchartered universities to have a larger sample size for analysis.

As stated earlier random sampling method for collected data. We rec-ommend that stratified random sampling be used since it is a method ofsampling, which involves the division of a population into smaller groups,known as strata. In stratified random sampling, the strata are formed basedon their members sharing a specific attribute or characteristic which could bea sample from each province in Kenya. A random sample from each stratumis taken, in a number proportional to the stratum’s size when compared tothe population. These subsets of the strata are then pooled to form a ran-dom sample. The main advantage with stratified sampling will be how it willcapture key population characteristics in the sample. Similar to a weightedaverage, this method of sampling produces characteristics in the sample thatwill be proportional to the overall population. Stratified sampling works wellfor populations with a variety of attributes.

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Bibliography

[1] A. Agresti. Categorical Data Analysis. 2nd rev-ed., New York: JohnWiley & Sons, 2002.

[2] L. Bailey, Brenda, Curtis Bauman, and A. Lata, Kimberly. StudentRetention and Satisfaction: The Evolution of a Predictive Model., 1998.

[3] Frank Cooney. A Review of the Results and Methodology in the 1999noel Levitz Student Satisfaction Survey at Salt Lake Community Col-lege. Salt Lake City,. Utah: Salt Lake Community College., (Eric No:ED443482), 2000.

[4] K. Damminger, Joanne. Student Satisfaction with Quality of AcademicAdvising Offered by Integrated Department of Academic Advising andCareer Life Planning,. Glassboro, (Eric No: ED453769), 2001.

[5] J. Hummel, T. and W. Lichtenberg, J. Predicting Categories of Im-provement Among Counseling Center Clients. 2001.

[6] A. O’Connell, A. Methods for modeling ordinal outcome variables. Mea-surement and Evaluation in Counseling and Development, 33(3):170–193, 2000.

[7] S. Robins, Lynne, D. Gruppen, Larry, L. Alexander, Gwen, C. Fantone,Joseph, and Wayne Davis. A Prediction Model of Student Satisfactionwith the Medical School Learning Environment. Academic Medicine,,72(2), 1997.

[8] H. Thomas, Emily and Nora Galambos. What Satisfies Students? Min-ing Student-Opinion Data with Regression and Decision-Tree Analysis.Stony Brook, 2002.

[9] D. Umbach, Paul and R. Porter, Stephen. How Do Academic Depart-ments Impact Student Satisfaction? Understanding the Contextual Ef-fects of Departments., 2001.

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[10] Nancy Wild. Rogue Community College Student Satisfaction Survey,Management. (Eric No: ED448831), 2000.

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Appendix A

Appendix I

Student Satisfaction QuestionnaireThe questionnaire will be used to determine the students’ satisfaction inrelation to their performance.Please take a few minutes of your time to choose the response which bestdescribes your opinion in the following statements. Please consider all thecourses you have already taken and currently taking as you formulate yourresponse. Your response will be considered confidential.

PART I a) Personal Details

1. Department

2. Year of study First Second Third Fourth

3. Gender Female Male

4. Age

5. Program of studyGOK Alternative degree program (ADP)

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b) faculty involvementIn a scale of 1 - 5 where

1 = Very unsatisfactory2 = unsatisfactory3 = Average4 = satisfactory5 = Very satisfactoryplease rate by ticking in the appropriate box.

1. Course satisfaction in reference to the market demand1 2 3 4 5

2. Lecturers’ service delivery1 2 3 4 5

3. Service delivery at the faculty office1 2 3 4 5

4. Service delivery at the department office1 2 3 4 5

5. Relationship between students and the faculty sub-ordinate1 2 3 4 5

6. Accessibility of the faculty office1 2 3 4 5

PART IIcurriculum contents and incorporated psychological factors

1. Course promotion by the faculty1 2 3 4 5

2. The communication skills gained from your course1 2 3 4 5

3. The computer skills gained from your course1 2 3 4 5

4. The research skills gained from your course1 2 3 4 5

PART III Support services

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1. Admission and registration process in reference to time1 2 3 4 5

2. Financial ability to pay school fees and meet your needs1 2 3 4 5

3. The library services1 2 3 4 5

4. The tutorial services in your course1 2 3 4 5

5. Career counseling services in the faculty1 2 3 4 5

PART IV Facilities

1. The lecture room facilities1 2 3 4 5

2. The JKUAT hospital facilities1 2 3 4 5

3. Course laboratory facilities1 2 3 4 5

4. The accommodation facilities in JKUAT1 2 3 4 5

5. The accommodation facilities outside JKUAT1 2 3 4 5

6. The JKUAT internet facilities1 2 3 4 5

7. The student center facilities1 2 3 4 5

PART V Recreation activities

1. The faculty inter-departmental sports events1 2 3 4 5

PART VI General question

1. Generally on faculty of science service delivery1 2 3 4 5

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