Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)

Size: px
Start display at page:

Download "Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)"

Transcription

1 Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared by: Christina Saad Wadid Maria Emad Yousef Marina Wahba Elias Research project submitted in fulfillment of the requirements of B.Sc. in statistics Under the supervision of: Dr. Ahmed Gad 2011

2 Acknowledgment First, we would like to thank Dr. Ahmed Gad, our supervisor and professor in Faculty of Economics and Political Science, statistics department, for his support, guidance and useful comments throughout this research paper. We would like to give special thanks and appreciation to Dr. Fatma Elzanaty, professor in the statistics department in the faculty, for helping us with her advice.

3 List of contents Acknowledgment Chapter one: Introduction and objectives...1 Introduction Research objectives Research questions The Conceptual framework Literature review Chapter two: Methodology of the study Population of the study Sample of the study Sample design Pretest Questionnaire design Data collection.9 Chapter three: Data description Introduction univariate description Bivariate description Graphical description Tabular description Chapter four: Inferential analysis Introduction comparisons of students' opinion Logistic regression Binary logistic regression Multinomial logistic regression Chapter five: Conclusion and recommendations Conclusion Recommendations References Appendix I: SPSS output Appendix II: Questionnaire

4 Chapter 1 Introduction and objectives Introduction Education is the basis of culture and civilization. Future of any nation is safe in the hands of educated individuals. Education gives us the knowledge of the world around us. It arms us with an insight to look at our life and learn from every experience. Getting educated and finally having a professional degree prepares us to be part of the society. Education is not about lessons and poems in textbooks; it is about the lessons of life. There are many reasons why education is important specially university education. Many people consider a university degree as a main factor that will substantially increase their potential earnings. But actually the main importance of the university is that it increases confidence among students. There is nothing quite like believing in you, many people do not realize that but faculty builds confidence in personal and professional levels (Hata, 2011) Egypt has the biggest educational system in the Middle East. We can see that in the university system. There were a lot of changes to improve the way of teaching and learning. (http://www.modernegypt.info/one-hundred-facts-about-egypt) But no one can deny the problems that exist in our university system, such as the lack of resources in many faculties and rigidity of the university administrations. Due to the importance of the university life and its effect on the students, we decided to focus on the opinion of students regarding university and the problems that faced them during their years in the university. 1.1 Research Objectives Research Questions a. Background of the students at Faculty of Economics and Political Science before joining it Regarding this objective, we will answer some questions such as; Why did you join the Faculty of Economics and Political Science? How did you expect the level of studying? Did you know the departments of faculty before you join it? 1

5 b. Effects of the faculty on the students' life We aim to explore the difficulties that face students to have friends inside and outside the faculty, gaining opinion skills and building a relationship with instructors. c. Students' opinion on the faculty after joining it Through this objective, we will answer many questions such as; Did you find the attendance important? Do you expect to find a good job after graduation? Did you find that failing or passing depends on the opinions of instructors? d. knowing which variables can affect the grade of the students Through this objective, we will answer questions such as; Does participation in social activities affect the students' grade? Does the teaching method affect the students' grade? e. knowing which variables can affect reasons for changing the faculty Through this last objective, we will answer questions such as; Does the gender affect reasons for changing the faculty? Does the grade affect reasons for changing the faculty? Through these five major objectives, we can answer the main question of this project; Did the opinions of students about Faculty of Economics and Political Science change after joining it? As through the first objective, we collected some information about the faculty and how the students see it. Then we focused on the impact of Faculty of Economics and Political Science on the social life of students and how it affected them positively or negatively. And the third objective helps to see if the students changed their opinion and why. By the final two objectives, we reached a conclusion about the questions "which variables can affect the students' grade? " and " which variables can affect reasons for changing the faculty?". Also we discovered some problems that faced the students and tried to put some solutions that could make the students satisfied with their faculty. 2

6 1.1.2 The Conceptual framework Conceptual framework of the factors that affect the opinion of students about the faculty: Social life in the faculty Instructors and Assistants Discovering the students' Opinion about the faculty Participation in activities Job market Way of study 3

7 1.2 Literature review There are few studies concern with the university students and their opinion towards some issues. Abul Dahab, Abdelazem, Tohamy and Abdullah (2010) studied the problems that encountered students of third and fourth year, in the Faculty of Economics and Political Science, during their study in the faculty. They were concern with how to measure the opinions of students in the educational process within the Faculty of Economics and Political Science. Also, they were interested to measure the opinions of students in some regulatory and administrative aspects. One of their study objectives is to figure out the comparability problems faced by students of the faculty with the problems facing other Egyptian university students. The factors that affect students satisfaction of the university are also studied. Abul Dahab, Abdelazem, Tohamy and Abdullah (2010) suggested using the proportional stratified random sample as a sampling method. The sample size was 203 from the fourth year and 197 from the third year. They selected the sample from students of the Faculty of Economics and Political Science of the final two years. This is because students of the two final years got familiar with the faculty services and activities. They reached the following results: 1. Students opinion about the regulatory process and administrative: 82% of the sample was satisfied with the performance of instructors in the major specialization and 82.6% in minor specialization. 75.8% of the sample was satisfied with the exams in the major specialization and 74.3% in the minor specialization. 2. Problems faced by the students during their study in the Faculty: 74.9% faced problems related to the size of the course and 76.3% faced problems related to availability of books for some courses. 71.8% of the students faced problems related to the instructors who don t allow them to review the results of their exams. 4

8 Depending on these results Abul Dahab, Abdelazem, Tohamy and Abdullah (2010) gave the following recommendations. 1. Reduce the number of the assignments so that the students find time to participate in activities. 2. Reduce the contents of the courses and work on introducing some scientific and applied research fields in the teaching methods. 3. Updating the library and provide it with books in all fields. 4. Increase the number of computer laboratories. 5. Reduce the prices of the text books. Another study have been cited by Abul Dahab, Abdelazem, Tohamy and Abdullah (2010) concerned with the effectiveness of the library. This study titled "Efficient use of the library in Faculty of Economics and Political Science". This study aimed to explore the relationship between the students' grades and the library usage. Also, they were interested to study the relationship between the students' economic level and using the library. The authors in this study used the proportional stratified random sample, with sample size of 150 students. The frame was the students' seat numbers. The sample was divided to nine categories based on the major specialization. This study ended with the following results. 1. Economics department: When a student uses different library (not the faculty library), that will raise his grades level. The time spent by the students in the library does not affect their average score. 2. Statistics department: Going to libraries does not affect the students' grades as they depend on the lectures. 3.Political Science department: When a student uses different library (not the faculty library), that will raise his grades level. The time spent by the students in the library affect their average score. 5

9 A third study also cited by Abul Dahab, Abdelazem, Tohamy and Abdullah (2010) was concerned with the use of information technology under the title Use of information technology in the Faculty of Economics and Political Science. This study objectives were; 1.Assess the level of the possible available technology equipments. 2.Limitation for the required and not available technology equipments. The authors in this study used two questionnaires, one for the students and one for the faculty members. The sample size was 263 students and 25 from the faculty members. All the students of Statistics department were included in the survey because of their limited number. The authors reached the following results: 1.About 2 % of the sample studied materials includes the use of computers. 2.About 72 % of the sample needed extra hours in the laboratory. 3.About 83 % of the sample believed that there are benefits of using computers. 4. About 63 % of the sample believed that the use of computers help them access to the benefits of many materials. 6

10 2.1 Population of the study Chapter 2 Methodology of the Study The population of the study is the students of Faculty of Economics and Political Science. We chose our faculty because we want to know the opinion of students about it, in order to know the problems that face them. The target is to survey the third and the fourth year students because they built their opinions about the faculty and they faced more problems than freshers. 2.2 Sample of the study Sample design A stratified random sample of students was selected from the faculty where the two Strata s are the students of third and fourth year (Levy,P, 2008). To calculate the sample size we used the formula: n Z 2 α/2 p (1-p) / d 2, where, n : sample size. Z α/2 : it is the (1-α/2) th quantile of the standard normal distribution. Usually the significance level α is used as 0.05 so that Z α/2 = d : sampling error which is the ratio between the sampling error when using the stratified random sample and the sampling error when using simple random sample (it is assumed to be 5% ). P : population proportion (where it can be replaced with 0.5 to ensure having the biggest size of a sample at the specified error and confidence levels). We used this formula to calculate the sample size and we got a sample of size 384 students. Then adding the non response amount which is assumed to be 4%, i.e. 16 extra students. Finally we got a sample size of 400 students. Some of the respondents didn t complete the questionnaires so, we canceled their questionnaires. We ended up with a sample of 396 students, distributed on both years (192 from third year students and 204 from fourth year students). In the faculty the frame was obtained for the number of students in third and fourth year (Stratum s) and students were taken randomly equal from each year. The sample subjects in each year were chosen proportionally (according to their population weights) for the departments and sections. 7

11 The distribution of the population is: Table 1: population distribution from the third year English Arabic French section department Economics Political science Statistics Total Table 2: population distribution from the fourth year English Arabic French section department Economics Political science Statistics Total The total population is 1717 students (841 from third year and 876 from forth year) The weight of third year= 841/1717=.4898 The weight of fourth year=876/1717= Sample size of third year=196 Sample size of fourth year=204 The 196 students (from third year) were distributed as follows: Table 3: sample distribution from the third year English Arabic French section department Economics Political science Statistics 7 10 Total

12 The 204 students (from fourth year) were distributes as follows: Table 4:samle distribution from the fourth year English Arabic French section department Economics Political science Statistics Total Pretest In order to make sure that the questionnaire is completely clear to everyone, we conduct a pretest trial. The trial was conduct on a sample of 10 students. We changed some questions that were not understandable by the students Questionnaire design After the arrangements that we made on the questionnaire, we reached the final form of the questionnaire that is consisted of 48 questions to reach our research objects. 1. Personal information (from question 1 to question 6). 2. Educational information (from question 7 to question 21). 3. Social life information (from question 22 to question 41). 4. Information about the work (from question 42 to question 46). A copy of the questionnaire is attached in Appendix II Data collection The data were collected in one week. During the collection we faced some problems. First, the low percentage of males in the faculty leads to low percentage of males in the sample. Second, it was found that the students didn't complete their questionnaires and they left directly after the lectures. Third, some students refused to fill the questionnaires from the beginning. 9

13 Chapter 3 Data description Introduction One of our main objectives is to know about the background of the students of Faculty of Economics and Political Science before and after joining it. So, in this chapter we will describe the main characteristics of the students of the sample such as gender, place of residence, monthly pocket money, and the grade of the previous year. Also, we study some relationships between many variables such as the major & real study level. 3.1 Univariate description Figure 1: Pie chart of the gender From the pie chart, we can see that in the sample, most of the students are females and the reason for this is that males are minority in the faculty. 10

14 Figure 2: Bar chart of the place of residence From the figure, we can see that majority of the students (72%) are from Cairo while only (28%) came from out of Cairo areas, distributed as (16.7%) from urban areas and (11.3%) from rural areas. Figure 3: Bar chart of the monthly pocket money From the figure, we can see that that around 42 % of the students take 300 LE and more while only around 4% of them take less than 100 LE which indicates an acceptable standard of living. 11

15 Figure 4: Bar chart of the grade of previous year Only 4% of the students passed but failed in one or two subjects while around 45% got good for their grade. Around 37% got very good for their grade which is considered a great indicator of the sample. Figure 5: Bar chart of reasons of joining We can see that, the majority of the sample (around 63% ) joined the faculty because of their own choice while only around 3% joined the faculty because of job opportunities. 12

16 Figure 6: Bar chart of the reason for willingness to change faculty From this graph, we can see that there are equal percentages (29.7%) for students who want to change the faculty because it s difficult, and who think that there is no job opportunities. Also, 15.3% of the students think that the faculty isn t suitable for them. 13

17 Figure 7: Bar chart of willingness to change the faculty From this graph, we can see that only around 28% of the sample would change the faculty if they had the chance while around 72% would not change the faculty. Figure 8: Bar chart about if the faculty is considered an obstacle for students to make friends outside the faculty Around 77% don't think that the faculty is considered an obstacle for students to make friends and only around 23% do think that the faculty is an obstacle to make friends. 14

18 3.2 Bivariate Description Graphical description Figure 9: Bar chart for the major and the actual study level This graph shows that the majority of statistics students said that the study level is very difficult. While the majority of economics and political science students said that the study level is difficult. Also, economics students are the majority among who consider their department is the easiest. 15

19 Figure 10: Bar chart for the major specialization & why choosing it This graph shows that there are equal percentages (41%) for those who choose Economics department because of job opportunities and those who choose it because they want to study it. Also, the majority of Political Science and Statistics departments choose their department because they want to study it. There is no one in Political science and statistics department chose his\her department to be with friends. There is no one in Political Science choose it because of job opportunities. 16

20 Figure11: bar chart between opinion about job opportunities before and after joining the faculty From this figure we see that, 46.8 % of the students who thought job opportunities exist, didn t change their minds. More than half of the students who thought job opportunities doesn't exist, changed their minds to "don't know". 40.8% of the students who didn t know about job opportunities before the faculty, found that job opportunities exist. 17

21 1.2.2 Tabular description One of our main objectives is to find out if the faculty affects the students' educational and social life. So, we used cross tabulation to fulfill our objective. Table 5: Comparison between the expected and the actual study level study expectations before joining Total easy moderat e actual study level after joining Difficu lt very difficu lt Easy 5.0% 4.9% 1.6% 2.5% 2.8% moderate 5.0% 31.1% 27.2% 23.5% 26.3% difficult 55.0% 41.7% 45.0% 45.7% 44.8% very difficult 35.0% 22.3% 26.2% 28.4% 26.1% Total 100.0% 100.0% 100.0% 100.0% 100.0% This table shows that 5% of those who expected having easy study found it easy, while 55% found it difficult. Around 31% of those who expected having moderate study found it moderate. 45% of those who expected difficult study found it difficult. And around 28% of those who expected having very difficult study found it very difficult while 2.5% found it easy. Table 6: Comparison between the expected and the actual study method actual study method text books study method expectations references lectures Summaries Total text books 34.8% 57.8% 53.0% 50.0% 42.6% References 7.8% 8.9% 6.0% 2.9% 7.1% Lectures 44.8% 31.1% 32.5% 41.2% 40.3% Summaries 12.6% 2.2% 8.4% 5.9% 9.9% Total 100.0% 100.0% 100.0% 100.0% 100.0% From this table, we can see that around 45% of students who were expecting that studying will be from text books found that it depends on lectures and around 3% of those who were expecting studying from summarized papers found that it depends mainly on references. 18

22 Table 7: Comparison between the expected and the actual relationship between instructors & students actual relationship between you and instructor expecting relationship between you and instructor before joining bad normal Good Total bad 13.3% 9.0% 11.5% 10.2% normal 50.0% 56.4% 43.8% 51.8% good 36.7% 34.6% 44.6% 38.1% Total 100.0% 100.0% 100.0% 100.0% From this table, we can see that the majority of those who were expecting bad relationship and those who were expecting normal relationship found it normal. The percentage of students who were expecting good relationship and found it good(around 45%) approximately equal the percentage of those who found it normal (around 44%). The percentage of bad relationship after joining the faculty is the lowest percentage. Table 8: Comparison between participating in social activities & the actual study level Actual study level after joining social activities yes no Total Easy 27.3% 72.7% 100.0% moderate 31.7% 68.3% 100.0% difficult 15.8% 84.2% 100.0% very difficult 22.5% 77.5% 100.0% Total 22.1% 77.9% 100.0% From this table, we can see that 72.7% of those who said that studying level is easy don't participate.also,77.5% of those who said that studying level is difficult don't participate. 19

23 Table 9: Comparison between the major and the actual relationship between instructors & students actual relationship between you Total and instructor bad normal good major Statistics 14.3% 31.0% 54.8% 100.0% Economics 9.4% 57.3% 33.3% 100.0% political 10.1% 49.6% 40.3% 100.0% science Total 10.2% 51.8% 38.1% 100.0% From this table, we can see that the majority of students of Statistics department find the relationship good while the majority of students of Political Science and Economics find it normal. Table 10: Comparison between the major & asking when facing hard questions if you faced hard question, who will Total you ask? doctors colleagues no one major Statistics 45.2% 52.4% 2.4% 100.0% Economics 39.0% 54.9% 6.1% 100.0% political 40.7% 54.3% 5.0% 100.0% science Total 40.3% 54.4% 5.3% 100.0% From this table we can see that, around 3% of statistics department students will not ask any one if they found hard questions while around 55% of political science department students will ask their colleagues if they found hard questions. The majority of the three departments depend on their colleagues. 20

24 Table11: Comparison between the major & asking for personal advice from instructors Asking for personal advice Total from instructors yes no major Statistics 31.0% 69.0% 100.0% Economics 22.5% 77.5% 100.0% political 27.9% 72.1% 100.0% science Total 25.3% 74.7% 100.0% We can see that around 70% of Statistics department don't take personal advice from instructors. While in Economics department this percentage increased to be around 78%. Around 23% of Economics department take personal advice from instructors while in Statistics department this percentage increased to be 31%. Table 12: Comparison between the major and job opportunities after joining the faculty do you expect job opportunities after Total joining Yes no Do not know major statistics 42.9% 11.9% 45.2% 100.0% economics 38.5% 12.7% 48.8% 100.0% political 35.5% 20.3% 44.2% 100.0% science Total 37.9% 15.3% 46.8% 100.0% From this table, we can see that the majority of the students of the three departments don't know if they will find job. Around 43% of statistics department, which is the largest percentage compared with the other two departments, expect finding job. The percentages of students of the three departments who don't think that they will find job are the lowest percentages. Using Chisquare test, we found that p-value= 0.35.So, there is no significant relationship between the major and job opportunities after joining the faculty 21

25 Table 13: Comparison between the grade and changing faculty changing the Total faculty Yes no grade Passed but failed 9.3% 1.8% 3.9% in one or two subjects Passed 9.3% 5.0% 6.2% Good 45.8% 46.2% 46.1% very good 30.8% 38.4% 36.3% Excellent 4.7% 8.6% 7.5% Total 100.0% 100.0% 100.0% Around 46% of students who want to change faculty, their grade is Good. While only about 2% of students who don t want to change faculty passed but failed in one or two subjects. So we conclude that there is no relation between the grade and the willingness to change the faculty. Using linear by linear association test, we found that p-value= 0.647so, there is an insignificant relationship between the grade and changing the faculty. Table 14: Comparison between the major & the social activities social activities Total yes no major Statistics 23.8% 76.2% 100.0% Economics 22.5% 77.5% 100.0% political 20.7% 79.3% 100.0% science Total 22.0% 78.0% 100.0% Around 76% from statistics department don't participate in social activities. And this percentage increased in economics department to be 78%. While in political science around only 20% participate in social activities. We can say that the department doesn't affect the participation in social activities. Using Chi-square test, we found that p-value= so, there is no significant relationship between the major & the social activities participation. 22

26 Table15: Comparison between the gender and participating in social activities social activities Total yes No gender male 38.9% 61.1% 100.0% female 19.4% 80.6% 100.0% Total 22.0% 78.0% 100.0% From the table, around 40% of males participate in social activities and the percentage decreased to be around 19% in females, indicating that females participate less than males. Using odds ratio measure, we found that there is a significant relationship between gender and participating in social activities. The odds of participating in social activities for males are times the odds of participating in social activities for females. Table 16: Comparison between the gender and the reasons of joining the faculty reasons of joining Total grade job opportunities family opinion your own choice or the faculty name Gender male 17.6% 3.9% 7.8% 70.6% 100.0% female 24.2% 2.9% 10.9% 61.9% 100.0% Total 23.3% 3.1% 10.5% 63.1% 100.0% Around 18% of males joined the faculty because of the grade and this percentage increased to be around 24% in females. Around 3% of females joined the faculty because of the job opportunities compared to the males (4%) which is considered very low percentages. Using Chisquare test, we found that p-value= so, there is no significant relationship between the gender & reasons of joining the faculty. 23

27 Chapter 4 Inferential analysis Introduction In this chapter we will use more advanced techniques to achieve our objectives, also to achieve a significant answer about some relationships between the variables. To do so, we explore the relationships between different variables and try to compare student' opinion before and after joining the faculty. Also, we model the relationship of students' grade and some variables and the relationship of reasons of changing the faculty and some variables. 4.1 Comparisons of Students' Opinion Here, we want to see if students opinion changed after joining the faculty or not. This will help us to answer one of our main questions; whether the faculty affects students' opinion or not. To compare between students' opinion after and before, we will use non-parametric methods because the data is categorical data. Also, the nonparametric methods can be used to infer about any postulated proposition not only a specific parameter. But the parametric methods are used to conduct statistical inference on parameters depending on strong assumptions regarding the probability of the population. We will use the sign paired test because we want to test the difference between students' opinion about the same variable before and after joining the faculty (Conover, 2008). The test assumptions are: The sample (pairs) is random sample. The measurement scale is at least ordinal. 24

28 To conduct the sign test, we use the deviation as ( after-before ) for all factors. Table 23: The sign test results Z Asymp. Sig. (2-tailed) attendance importance assignments staying for late time more than 3 lectures passing and failing depends on the instructor real study level real teaching method real relationship between you and instructors number of holidays per week job opportunities Testing if there is a significant difference between the opinions of students about the importance of attendance after and before joining the faculty H 0 : M d = 0 H 1: M d 0 P-value=0 < α Reject H 0. We are 95% confident that there is a significant difference between the opinions of students about the importance of attendance after and before joining the faculty Testing if there is a significant difference between opinions of the students about the existence of assignments after and before joining the faculty H 0 : M d = 0 H 1: M d 0 P-value=0 < α Reject H 0. We are 95% confident that there is a significant difference between opinions of the students about the existence of assignments after and before joining. 25

29 4.1.3 Testing if there is a significant difference between opinions of the students about staying at faculty till late after and before joining the faculty H 0 : M d = 0 H 1: M d 0 P-value=0 < α Reject H 0. We are 95% confident that there is a significant difference between opinions of the students about staying at faculty till late after and before joining the faculty Testing if there is a significant difference between opinions of the students about having more than 3 lectures after and before joining the faculty H 0 : M d = 0 H 1: M d 0 P-value=0 < α Reject H 0. We are 95% confident that there is a significant difference between opinions of the students about having more than 3 lectures after and before joining the faculty Testing if there is a significant difference between opinions of the students about if failing and passing depends on the instructors' opinion after and before joining the faculty H 0 : M d = 0 H 1: M d 0 P-value =0 < α Reject H 0. We are 95% confident that there is a significant difference between opinions of the students about if failing and passing depends on the instructors' opinion after and before joining the faculty Testing if there is a significant difference between studying level after and before joining the faculty H 0: M d =0 H 0 : M d 0 P-value=.314>α Don't reject H 0, therefore we are 95% confident that there is no significant difference between opinions of the students about studying level before joining the faculty and after joining it. 26

30 4.1.7 Testing if there is a significant difference between the way of teaching after and before joining the faculty H 0: M d =0 H 0 : M d 0 P-value=0 <α Reject H 0, therefore we are 95% confident that there is a significant difference between opinions of the students about the way of teaching before and after joining the faculty Testing if there is a significant difference between students' opinion about the relationship between students and instructors in the faculty after & before joining. H 0: M d =0 H 0 : M d 0 P-value=.358 >α Don't reject H 0, therefore we are 95% confident that there is no significant difference between opinions of the students about the relationship between students and instructors after and before joining faculty Testing if there is a significant difference between the number of holidays after and before joining the faculty H 0: M d =0 H 0 : M d 0 P-value=0 <α Reject H0, therefore we are 95% confident that there is a significant difference between the opinions of the students about the number of holidays after and before joining the faculty Testing if there is a significant difference in opinions about job opportunities after and before joining the faculty H 0: M d =0 H 0 : M d 0 P-value=0 <α Reject H0, therefore we are 95% confident that there is a significant difference between the opinions of the students about job opportunities after and before joining the faculty. 27

31 4.2 Logistic regression Binary logistic regression of the students' grade and some variables To study the effect of some variables on the students' grade we used binary logistic regression model because our independent variables are categorical variables and our dependent variable is categorical and binary. The model is; (Π/1- Π) = Exp( β 1 X 1 + β 2 X 2 +.) Using forward stepwise method, we performed the model. Our dependent variable is the grade which takes the values 0 if good or less 1 if more than good The independent variables are; the reason for choosing the department, reasons for joining the faculty, real teaching method, real study level, relationship between students and instructors, students' opinions about if passing and failing depends on the instructors,difficulty of making friends, participating in social activities, students' opinion about having more than 3 lectures per day, students' opinions about staying for late time to attend lectures, students' opinion about assignments' existence, students' opinion about attendance importance and gender. The reference category for variable (the reason for choosing the department): don't prefer the other departments. The reference category for variable (reasons of joining the faculty): your own choice or the faculty name. The reference category for variable (real teaching method): summaries The reference category for variable (real study level): very difficult The reference category for variable ( relation between students and instructors): good The reference category for variable (passing and failing depends on the instructors): No The reference category for variable (difficulty of making friends): No The reference category for variable (participating in social activities): no The reference category for variable (students' opinion about having more than 3 lectures per day): no 28

32 The reference category for variable (students' opinion about staying for late time to attend lectures): no The reference category for variable (students' opinion about assignments' existence): no The reference category for variable (students' opinion about attendance' importance): no The reference category for variable (gender): female. From table 3 in appendix I: The model in step (4) is the best model as it has the smallest value of G 2 (-2log likelihood) = and the highest R 2 = From table4 in appendix I: 79% from those who their grades are good or less are correctly classified while 46.7% from those who their grades are more than good are correctly classified. 64.8% is the overall percentage of correct classification. From table 5 in appendix I: Step = G 2 (M3) G 2 (M4) = The difference in G 2 is significant compared with model step (3). Model = G 2 (M0) G 2 (M4) = The difference in G 2 is significant. It is worth to loss 11 degrees of freedom compared with the reduction in G 2 = From table 6 in appendix I: The model, (Π/1- Π) = (choosing department because of job opportunities) (passing and failing depends on the instructors). Exp (B) = The odds of getting the grade more than good for those who chose their department because of job opportunities are times the odds of getting the grade more than good for those who chose their department because they don't prefer the other two departments. Exp (B) = The odds of getting the grade more than good for those who think that passing the exam depends on the instructor are times the odds of getting the grade more than good for those who think that passing the exam doesn't depend on the instructors. 29

33 4.2.2 Multinomial logistic regression of reasons for changing the faculty and some variables: To study the effect of some variables on the reasons for changing the faculty, we used multinomial logistic regression model because independent variables are categorical variables and our dependent variable is categorical and nominal. The model is; (Π j /1- Π j ) = Exp( β 1j X 1 + β 2j X 2 +.). Using main effect method, we performed the model. Our dependent variable is the reasons for changing the faculty which takes the categories: Difficult Not suitable No job opportunities (reference category) The independent variables are; gender, grade, real study level, place of residence, the difficulty of making friends because of the faculty. From table 8 in appendix I: The initial log likelihood value ( ) is a measure of a model with no independent variables, i.e. only a constant or intercept. The final log likelihood value ( ) is the measure computed after all of the independent variables have been entered into the logistic regression. The difference between these two measures is the model Chi-square value (35.341) that is tested for statistical significance. This test is analogous to the F-test for R² or change in R² value in multiple regression which tests whether or not the improvement in the model associated with the additional variables is statistically significant. Here the model Chisquare value has a significance so we conclude that there is a significant relationship between the dependent variable and the set of independent variables.. From table 9 in appendix I: The Cox and Snell R² measure operates like R², with higher values indicating greater model fit. However, this measure is limited in that it cannot reach the maximum value of 1, so Nagelkerke proposed a modification that had the range from 0 to 1. We will rely upon Nagelkerke's measure as indicating the strength of the relationship. we would conclude that the relationship is weak. From table 10 in appendix I: we can conclude that the model is accurate as the overall correct classification percentage was 50.5% (great percentage). 30

34 From the table 11 in appendix I: Where, j=1 if the reason is difficult, 2 if the reason is that it is not suitable If the reason is difficult (j=1) (Π j /1- Π j ) = Exp( β 1j X 1 + β 2j X 2 +.). (Π 1 /1- Π 1 )= (real study level is moderate) (real study level is difficult) Exp (β) = The odds of changing the faculty because it is difficult for those who said that the study level is moderate are the odds of changing the faculty because it is difficult for those who said that the study level is very difficult. Exp (β) = 0.16 The odds of changing the faculty because it is difficult for those who said that the study level is difficult are 0.16 the odds of changing the faculty because it is difficult for those who said that the study level is very difficult. If the reason is it is not suitable (j=2) (Π 2 /1- Π 2 )= 0.137(place of residence is out of Cairo but in urban areas) Exp (β) = The odds of changing the faculty because it is not suitable for those who live out of Cairo but in urban areas are the odds of changing the faculty because it is not suitable for those who live in Cairo. 31

35 Chapter 5 Conclusion and Recommendations Conclusion Our main objective is to know if the opinion of students about Faculty of Economics and Political Science changed after joining it or not. Also we need to explore if the faculty affected them in many aspects of their life or not. To accomplish this objective, we answered some main questions such as knowing the background of the students at Faculty of Economics and Political Science before joining it; the effect of the faculty on the students' life; Student s opinion on the faculty after joining it. Finally we answered two main questions; which factors can affect the student's grade? And which factors can affect the reasons of changing the faculty? We found that the majority of the sample joined the faculty because of their own choice while only around 3% joined the faculty because of job opportunities. When we studied the satisfaction degree with the faculty, we found that 28% of the sample would change the faculty if they had the chance while the majority don t want to change the faculty, which indicates that the majority of students is satisfied with the faculty. From the students who aren't satisfied with the faculty, there are equal percentages (29.7%) for students who want to change the faculty because it s difficult, and who think that there is no job opportunities. Also we found that 15.32% of the students think that the faculty isn t suitable for them. Also we found that the majority of Political Science and Statistics departments choose their department because they want to study it. No one in Political science and Economics department choose his/her department to be with friends and no one in Political Science choose it because of job opportunities. When we asked the students about their knowledge on the existence of job opportunities, we found that 46.8 % of the students, who think that job opportunities exist, didn t change their minds. More than half of the students, who thought that job opportunities don t exist, changed their minds to "don't know. 40.8% of the students, who didn t know about job opportunities before the faculty, found that job opportunities exist. All these results indicates that after the students joined the faculty and after contacting with graduates, instructors and teacher s assistants and knowing enough information about job fields, they changed their minds about the idea of not existing job opportunities for graduates. 32

36 When we studied the relationship between the major and asking when facing hard questions, we found that the majority of the three departments depend on their colleagues when they face a hard question. The majority of students of Statistics department found the relationship between the student and the instructor is good while the majority of students of Political Science and Economics found it normal. Around 46% of students who want to change faculty, their grade is Good. While only about 2% of students who don t will to change faculty are passed but failed in one or two subjects. So we conclude that there is no relation between the grade and the willingness to change the faculty. Around 76% of statistics department don't participate in social activities. This percentage increased in economic department to be 78%. While in political science only 20% participate in social activities. This means that departments don t affect participation in the social activities. When we tested the relationship between the grade and reasons of changing the faculty, we found that there is a significant weak relationship between the grade and reasons of changing the faculty. There is no significant relationship between the major & the social activities participation. Generally speaking, we found that the faculty does affect the students' opinions. We found a significant difference between students' opinions about the importance of attending lectures before and after joining. Also there is a significant difference between students' opinions about having more than 3 lectures before and after joining. But there is no significant difference between the medians of studying level before joining the faculty and after joining it and there is no significant difference between the medians of studying level before joining the faculty and after joining it. The odds of getting the grade more than good for those who chose their department because of job opportunities is times the odds of getting the grade more than good for those who chose their department because they don't prefer the other two departments. The odds of getting the grade more than good for those who think that passing the exam depends on the instructor is times the odds of getting the grade more than good for those who think that passing the exam doesn't depend on the instructors. The odds of changing the faculty because it is difficult for those who said that the study level is moderate is the odds of changing the faculty because it is difficult for those who said that the study level is very difficult. 33

37 The odds of changing the faculty because it is not suitable for those who live out of Cairo but in urban areas is the odds of changing the faculty because it is not suitable for those who live in Cairo. 5.2 Recommendations Depending on the conclusions above, we can suggest the following recommendations. It is recommended that the faculty encourage students to participate in social activities, for example, by giving bonus for students who participate in social activities, including applied activities in some courses, or make the courses more easier to give students a chance not to study only but also to have time to participate in social activities. It is recommended that instructors determine explicitly in the first lectures the references and text books of the course. In addition the instructors should use these references and text books during the course so that the student can benefit from buying the books. It is recommended that the faculty should inform students about job fields that are appropriate to their field so that students can be familiar with the job opportunities that they might find after graduation. 34

38 References Abu el dahab, E., Abdel azem, A., Tohamy, M. and Abdullah, M. (2010) Opinions of Faculty of Economics and Political Science students of third and fourth year about the problems that encountered during their study in the faculty, Unpublished Graduation Project, Faculty of Economics and Political Science, Cairo University, Egypt. Agresti, A. (2007) An Introduction To Categorical Data Analysis, John Wiley & Sons Inc., New York, US. Conover, W. J. (1980) Practical Nonparametric Statistics, John Wiley & Sons Inc., New York, US. Education is free through university level in Egypt, accessed on 10\3\2011 Hata, P. (2008), The importance of a university education, and-university-articles/british-academics-the-importance-of-a-university-education html. Accessed on 15/3/2011. Levy, P. (2008) Sampling of Populations, John Wiley &Sons, Inc., New York, US. 35

39 Appendix I SPSS Output 36

40 All tables of the Binary Logistic Regression: Table1: dependent variable (grade) coding. Original Value Internal Value Good or less 0 More than good 1 Table2: Categorical variables coding Frequenc y Parameter coding (1) (2) (3) (4) why did you choose this department the easiest want to study this branch job opportunities to be with my friends don't prefer the other departments The reasons of joining the college grade job opportunities family opinion your own choice or the faculty name real study method text book

41 references lectures summaries real study level easy moderate difficult very difficult real relationship between you and instructor bad normal good did you find passing and failing depends on the instructor yes no was it difficult to make friends yes no social activities yes no did you have more than 3 lectures per day yes no did you stay for late time to attend lectures after joining college yes no assignments after college yes no

42 Attendance importance yes no gender male female Block 1: Method = Forward Stepwise (Likelihood Ratio) Table3: model summary Cox & Snell R Nagelkerke R Step -2 Log likelihood Square Square a a b b a. Estimation terminated at iteration number 4 because parameter estimates changed by less than.001. b. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. Table4: classification table Predicted New grade Observed 0 1 Percentage Correct Step 4 New grade Good or less More than good Overall Percentage

43 Predicted New grade Observed 0 1 Percentage Correct Step 4 New grade Good or less More than good Overall Percentage 64.8 a. The cut value is.500 Table5: tests of model coefficients Chi-square df Sig. Step 4 Step Block Model Table6: variables in the equation B S.E. Wald df Sig. Exp(B) Step 4 d Real teaching method Real teaching method(1) Real teaching method(2) Real teaching method(3) Why did you choose this department

44 Why did you choose this department (1) Why did you choose this department (2) Why did you choose this department (3) Why did you choose this department (4) Relation extent between DRs & students Relation extent between DRs & students (1) E E9 Relation extent between DRs & students (2) E E9 Relation extent between DRs & students (3) E E9 Passing exam depend on DRs mode after(1) Constant E E d. Variable(s) entered on step 4: real teaching method. All tables of the multivariate Logistic Regression: Table 7: Table Case Processing Summary N Marginal Percentage why3 difficult % not suitable % 41

Wednesday PM. Multiple regression. Multiple regression in SPSS. Presentation of AM results Multiple linear regression. Logistic regression

Wednesday PM. Multiple regression. Multiple regression in SPSS. Presentation of AM results Multiple linear regression. Logistic regression Wednesday PM Presentation of AM results Multiple linear regression Simultaneous Stepwise Hierarchical Logistic regression Multiple regression Multiple regression extends simple linear regression to consider

More information

CRJ Doctoral Comprehensive Exam Statistics Friday August 23, :00pm 5:30pm

CRJ Doctoral Comprehensive Exam Statistics Friday August 23, :00pm 5:30pm CRJ Doctoral Comprehensive Exam Statistics Friday August 23, 23 2:pm 5:3pm Instructions: (Answer all questions below) Question I: Data Collection and Bivariate Hypothesis Testing. Answer the following

More information

Binary Logistic Regression

Binary Logistic Regression Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including

More information

Inferential Statistics

Inferential Statistics Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

More information

Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini

Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building

More information

11. Analysis of Case-control Studies Logistic Regression

11. Analysis of Case-control Studies Logistic Regression Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

More information

STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance

STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis

More information

Semester 1 Statistics Short courses

Semester 1 Statistics Short courses Semester 1 Statistics Short courses Course: STAA0001 Basic Statistics Blackboard Site: STAA0001 Dates: Sat. March 12 th and Sat. April 30 th (9 am 5 pm) Assumed Knowledge: None Course Description Statistical

More information

Profiles and Data Analysis. 5.1 Introduction

Profiles and Data Analysis. 5.1 Introduction Profiles and Data Analysis PROFILES AND DATA ANALYSIS 5.1 Introduction The survey of consumers numbering 617, spread across the three geographical areas, of the state of Kerala, who have given information

More information

SOME NOTES ON STATISTICAL INTERPRETATION. Below I provide some basic notes on statistical interpretation for some selected procedures.

SOME NOTES ON STATISTICAL INTERPRETATION. Below I provide some basic notes on statistical interpretation for some selected procedures. 1 SOME NOTES ON STATISTICAL INTERPRETATION Below I provide some basic notes on statistical interpretation for some selected procedures. The information provided here is not exhaustive. There is more to

More information

Credit Risk Analysis Using Logistic Regression Modeling

Credit Risk Analysis Using Logistic Regression Modeling Credit Risk Analysis Using Logistic Regression Modeling Introduction A loan officer at a bank wants to be able to identify characteristics that are indicative of people who are likely to default on loans,

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Final Exam Review MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) A researcher for an airline interviews all of the passengers on five randomly

More information

The Chi-Square Test. STAT E-50 Introduction to Statistics

The Chi-Square Test. STAT E-50 Introduction to Statistics STAT -50 Introduction to Statistics The Chi-Square Test The Chi-square test is a nonparametric test that is used to compare experimental results with theoretical models. That is, we will be comparing observed

More information

Calculating the Probability of Returning a Loan with Binary Probability Models

Calculating the Probability of Returning a Loan with Binary Probability Models Calculating the Probability of Returning a Loan with Binary Probability Models Associate Professor PhD Julian VASILEV (e-mail: vasilev@ue-varna.bg) Varna University of Economics, Bulgaria ABSTRACT The

More information

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

More information

Variables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test.

Variables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test. The Analysis of Research Data The design of any project will determine what sort of statistical tests you should perform on your data and how successful the data analysis will be. For example if you decide

More information

What is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data

What is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data Introduction What is Statistics? Statistics is about Collecting data Organizing data Analyzing data Presenting data What is Statistics? Statistics is divided into two areas: descriptive statistics and

More information

II. DISTRIBUTIONS distribution normal distribution. standard scores

II. DISTRIBUTIONS distribution normal distribution. standard scores Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

More information

(Logistic Regression) 2 *! %$%!&2.!0(%%##%#$" 2 $ (dichotomy) %%!0(%-"! #$""$1 $%,#*"./'(0(6 *-16*7" 22% "1 / 4"2'(0(-17")* "(4(*:#5* $" $2

(Logistic Regression) 2 *! %$%!&2.!0(%%##%#$ 2 $ (dichotomy) %%!0(%-! #$$1 $%,#*./'(0(6 *-16*7 22% 1 / 42'(0(-17)* (4(*:#5* $ $2 (Logistic Regression)!"#$ %!&'()* *%!+,-*("./.$ Multiple Regression +%!& %%!*"0(%-"! %$..(/" + 2 *! %$%!&2.!0(%%##%#$" 2 $ (dichotomy) %%!0(%-"! #$""$ '()*$%#$"#0( 2 $(4( 54( $#%)#*" $"-$(*( $%,#*"./'(0(6

More information

LOGISTIC REGRESSION ANALYSIS

LOGISTIC REGRESSION ANALYSIS LOGISTIC REGRESSION ANALYSIS C. Mitchell Dayton Department of Measurement, Statistics & Evaluation Room 1230D Benjamin Building University of Maryland September 1992 1. Introduction and Model Logistic

More information

SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES

SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR

More information

A STUDY ON IMPACT OF JOB ENRICHMENT PRACTICES TOWARDS EMPLOYEE SATISFACTION AT HDFC STANDARD LIFE INSURANCE

A STUDY ON IMPACT OF JOB ENRICHMENT PRACTICES TOWARDS EMPLOYEE SATISFACTION AT HDFC STANDARD LIFE INSURANCE A STUDY ON IMPACT OF JOB ENRICHMENT PRACTICES TOWARDS EMPLOYEE Shilpa R* A. Asif Ali* N. Sathyanarayana* Roopa Rani * SATISFACTION AT HDFC STANDARD LIFE INSURANCE Abstract: In today s dynamic world organizations

More information

Fairfield Public Schools

Fairfield Public Schools Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity

More information

User Behaviour on Google Search Engine

User Behaviour on Google Search Engine 104 International Journal of Learning, Teaching and Educational Research Vol. 10, No. 2, pp. 104 113, February 2014 User Behaviour on Google Search Engine Bartomeu Riutord Fe Stamford International University

More information

Northumberland Knowledge

Northumberland Knowledge Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about

More information

Name of the module: Multivariate biostatistics and SPSS Number of module: 471-8-4081

Name of the module: Multivariate biostatistics and SPSS Number of module: 471-8-4081 Name of the module: Multivariate biostatistics and SPSS Number of module: 471-8-4081 BGU Credits: 1.5 ECTS credits: Academic year: 4 th Semester: 15 days during fall semester Hours of instruction: 8:00-17:00

More information

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012 Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

More information

STATISTICA Formula Guide: Logistic Regression. Table of Contents

STATISTICA Formula Guide: Logistic Regression. Table of Contents : Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

More information

Module 9: Nonparametric Tests. The Applied Research Center

Module 9: Nonparametric Tests. The Applied Research Center Module 9: Nonparametric Tests The Applied Research Center Module 9 Overview } Nonparametric Tests } Parametric vs. Nonparametric Tests } Restrictions of Nonparametric Tests } One-Sample Chi-Square Test

More information

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different

More information

Introduction to Statistics and Quantitative Research Methods

Introduction to Statistics and Quantitative Research Methods Introduction to Statistics and Quantitative Research Methods Purpose of Presentation To aid in the understanding of basic statistics, including terminology, common terms, and common statistical methods.

More information

Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality

Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality Quantitative Data Analysis: Choosing a statistical test Prepared by the Office of Planning, Assessment, Research and Quality 1 To help choose which type of quantitative data analysis to use either before

More information

Survival Analysis Using SPSS. By Hui Bian Office for Faculty Excellence

Survival Analysis Using SPSS. By Hui Bian Office for Faculty Excellence Survival Analysis Using SPSS By Hui Bian Office for Faculty Excellence Survival analysis What is survival analysis Event history analysis Time series analysis When use survival analysis Research interest

More information

Organizing Your Approach to a Data Analysis

Organizing Your Approach to a Data Analysis Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize

More information

Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing. C. Olivia Rud, VP, Fleet Bank

Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing. C. Olivia Rud, VP, Fleet Bank Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Rud, VP, Fleet Bank ABSTRACT Data Mining is a new term for the common practice of searching through

More information

Simple Predictive Analytics Curtis Seare

Simple Predictive Analytics Curtis Seare Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use

More information

Logistic Regression. Introduction. The Purpose Of Logistic Regression

Logistic Regression. Introduction. The Purpose Of Logistic Regression Logistic Regression...1 Introduction...1 The Purpose Of Logistic Regression...1 Assumptions Of Logistic Regression...2 The Logistic Regression Equation...3 Interpreting Log Odds And The Odds Ratio...4

More information

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics. Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing

More information

Testing Hypotheses using SPSS

Testing Hypotheses using SPSS Is the mean hourly rate of male workers $2.00? T-Test One-Sample Statistics Std. Error N Mean Std. Deviation Mean 2997 2.0522 6.6282.2 One-Sample Test Test Value = 2 95% Confidence Interval Mean of the

More information

Statistics Review PSY379

Statistics Review PSY379 Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

More information

Modeling Lifetime Value in the Insurance Industry

Modeling Lifetime Value in the Insurance Industry Modeling Lifetime Value in the Insurance Industry C. Olivia Parr Rud, Executive Vice President, Data Square, LLC ABSTRACT Acquisition modeling for direct mail insurance has the unique challenge of targeting

More information

COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.

COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. 277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies

More information

SUGI 29 Statistics and Data Analysis

SUGI 29 Statistics and Data Analysis Paper 194-29 Head of the CLASS: Impress your colleagues with a superior understanding of the CLASS statement in PROC LOGISTIC Michelle L. Pritchard and David J. Pasta Ovation Research Group, San Francisco,

More information

" Y. Notation and Equations for Regression Lecture 11/4. Notation:

 Y. Notation and Equations for Regression Lecture 11/4. Notation: Notation: Notation and Equations for Regression Lecture 11/4 m: The number of predictor variables in a regression Xi: One of multiple predictor variables. The subscript i represents any number from 1 through

More information

Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.)

Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.) Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.) Logistic regression generalizes methods for 2-way tables Adds capability studying several predictors, but Limited to

More information

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk

Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:

More information

Lecture 2: Descriptive Statistics and Exploratory Data Analysis

Lecture 2: Descriptive Statistics and Exploratory Data Analysis Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals

More information

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-

More information

JOB SATISFACTION AMONG FEMALE TEACHERS IN PUBLIC AND PRIVATE SECTORS (A CROSS-SECTIONAL STUDY)

JOB SATISFACTION AMONG FEMALE TEACHERS IN PUBLIC AND PRIVATE SECTORS (A CROSS-SECTIONAL STUDY) Volume 2, 2013, Page 88-97 JOB SATISFACTION AMONG FEMALE TEACHERS IN PUBLIC AND PRIVATE SECTORS (A CROSS-SECTIONAL STUDY) Naila Amjad, Shazia Qasim Department of Statistics, Lahore College for Women University,

More information

IBM SPSS Statistics 20 Part 4: Chi-Square and ANOVA

IBM SPSS Statistics 20 Part 4: Chi-Square and ANOVA CALIFORNIA STATE UNIVERSITY, LOS ANGELES INFORMATION TECHNOLOGY SERVICES IBM SPSS Statistics 20 Part 4: Chi-Square and ANOVA Summer 2013, Version 2.0 Table of Contents Introduction...2 Downloading the

More information

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

More information

Having a coin come up heads or tails is a variable on a nominal scale. Heads is a different category from tails.

Having a coin come up heads or tails is a variable on a nominal scale. Heads is a different category from tails. Chi-square Goodness of Fit Test The chi-square test is designed to test differences whether one frequency is different from another frequency. The chi-square test is designed for use with data on a nominal

More information

Statistics and research

Statistics and research Statistics and research Usaneya Perngparn Chitlada Areesantichai Drug Dependence Research Center (WHOCC for Research and Training in Drug Dependence) College of Public Health Sciences Chulolongkorn University,

More information

Levels of measurement in psychological research:

Levels of measurement in psychological research: Research Skills: Levels of Measurement. Graham Hole, February 2011 Page 1 Levels of measurement in psychological research: Psychology is a science. As such it generally involves objective measurement of

More information

Ordinal Regression. Chapter

Ordinal Regression. Chapter Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe

More information

Research Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement

Research Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.

More information

χ 2 = (O i E i ) 2 E i

χ 2 = (O i E i ) 2 E i Chapter 24 Two-Way Tables and the Chi-Square Test We look at two-way tables to determine association of paired qualitative data. We look at marginal distributions, conditional distributions and bar graphs.

More information

F. Farrokhyar, MPhil, PhD, PDoc

F. Farrokhyar, MPhil, PhD, PDoc Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

More information

Chapter VIII Customers Perception Regarding Health Insurance

Chapter VIII Customers Perception Regarding Health Insurance Chapter VIII Customers Perception Regarding Health Insurance This chapter deals with the analysis of customers perception regarding health insurance and involves its examination at series of stages i.e.

More information

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math

More information

Choosing the correct statistical test made easy

Choosing the correct statistical test made easy Classroom Choosing the correct statistical test made easy N Gunawardana Senior Lecturer in Community Medicine, Faculty of Medicine, University of Colombo Gone are the days where researchers had to perform

More information

An introduction to IBM SPSS Statistics

An introduction to IBM SPSS Statistics An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive

More information

Foundation of Quantitative Data Analysis

Foundation of Quantitative Data Analysis Foundation of Quantitative Data Analysis Part 1: Data manipulation and descriptive statistics with SPSS/Excel HSRS #10 - October 17, 2013 Reference : A. Aczel, Complete Business Statistics. Chapters 1

More information

Chapter 13 Introduction to Linear Regression and Correlation Analysis

Chapter 13 Introduction to Linear Regression and Correlation Analysis Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing

More information

DATA INTERPRETATION AND STATISTICS

DATA INTERPRETATION AND STATISTICS PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE

More information

Main Effects and Interactions

Main Effects and Interactions Main Effects & Interactions page 1 Main Effects and Interactions So far, we ve talked about studies in which there is just one independent variable, such as violence of television program. You might randomly

More information

It is important to bear in mind that one of the first three subscripts is redundant since k = i -j +3.

It is important to bear in mind that one of the first three subscripts is redundant since k = i -j +3. IDENTIFICATION AND ESTIMATION OF AGE, PERIOD AND COHORT EFFECTS IN THE ANALYSIS OF DISCRETE ARCHIVAL DATA Stephen E. Fienberg, University of Minnesota William M. Mason, University of Michigan 1. INTRODUCTION

More information

THE EFFECT OF CO-CURRICULAR ACTIVITIES ON THE ACADEMIC PERFORMANCES OF THE STUDENTS: A CASE STUDY OF THE ISLAMIA UNIVERSITY OF BAHAWALPUR, PAKISTAN

THE EFFECT OF CO-CURRICULAR ACTIVITIES ON THE ACADEMIC PERFORMANCES OF THE STUDENTS: A CASE STUDY OF THE ISLAMIA UNIVERSITY OF BAHAWALPUR, PAKISTAN Bulgarian Journal of Science and Education Policy (BJSEP), Volume 6, Number 2, 2012 THE EFFECT OF CO-CURRICULAR ACTIVITIES ON THE ACADEMIC PERFORMANCES OF THE STUDENTS: A CASE STUDY OF THE ISLAMIA UNIVERSITY

More information

Mathematics within the Psychology Curriculum

Mathematics within the Psychology Curriculum Mathematics within the Psychology Curriculum Statistical Theory and Data Handling Statistical theory and data handling as studied on the GCSE Mathematics syllabus You may have learnt about statistics and

More information

Sun Li Centre for Academic Computing lsun@smu.edu.sg

Sun Li Centre for Academic Computing lsun@smu.edu.sg Sun Li Centre for Academic Computing lsun@smu.edu.sg Elementary Data Analysis Group Comparison & One-way ANOVA Non-parametric Tests Correlations General Linear Regression Logistic Models Binary Logistic

More information

SPSS: Descriptive and Inferential Statistics. For Windows

SPSS: Descriptive and Inferential Statistics. For Windows For Windows August 2012 Table of Contents Section 1: Summarizing Data...3 1.1 Descriptive Statistics...3 Section 2: Inferential Statistics... 10 2.1 Chi-Square Test... 10 2.2 T tests... 11 2.3 Correlation...

More information

Some Essential Statistics The Lure of Statistics

Some Essential Statistics The Lure of Statistics Some Essential Statistics The Lure of Statistics Data Mining Techniques, by M.J.A. Berry and G.S Linoff, 2004 Statistics vs. Data Mining..lie, damn lie, and statistics mining data to support preconceived

More information

Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@

Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@ Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@ Yanchun Xu, Andrius Kubilius Joint Commission on Accreditation of Healthcare Organizations,

More information

Simple Linear Regression One Binary Categorical Independent Variable

Simple Linear Regression One Binary Categorical Independent Variable Simple Linear Regression Does sex influence mean GCSE score? In order to answer the question posed above, we want to run a linear regression of sgcseptsnew against sgender, which is a binary categorical

More information

The Dummy s Guide to Data Analysis Using SPSS

The Dummy s Guide to Data Analysis Using SPSS The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests

More information

Lecture - 32 Regression Modelling Using SPSS

Lecture - 32 Regression Modelling Using SPSS Applied Multivariate Statistical Modelling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 32 Regression Modelling Using SPSS (Refer

More information

Module 4 - Multiple Logistic Regression

Module 4 - Multiple Logistic Regression Module 4 - Multiple Logistic Regression Objectives Understand the principles and theory underlying logistic regression Understand proportions, probabilities, odds, odds ratios, logits and exponents Be

More information

How to Get More Value from Your Survey Data

How to Get More Value from Your Survey Data Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2

More information

International Statistical Institute, 56th Session, 2007: Phil Everson

International Statistical Institute, 56th Session, 2007: Phil Everson Teaching Regression using American Football Scores Everson, Phil Swarthmore College Department of Mathematics and Statistics 5 College Avenue Swarthmore, PA198, USA E-mail: peverso1@swarthmore.edu 1. Introduction

More information

Descriptive Analysis

Descriptive Analysis Research Methods William G. Zikmund Basic Data Analysis: Descriptive Statistics Descriptive Analysis The transformation of raw data into a form that will make them easy to understand and interpret; rearranging,

More information

How to set the main menu of STATA to default factory settings standards

How to set the main menu of STATA to default factory settings standards University of Pretoria Data analysis for evaluation studies Examples in STATA version 11 List of data sets b1.dta (To be created by students in class) fp1.xls (To be provided to students) fp1.txt (To be

More information

Multinomial and Ordinal Logistic Regression

Multinomial and Ordinal Logistic Regression Multinomial and Ordinal Logistic Regression ME104: Linear Regression Analysis Kenneth Benoit August 22, 2012 Regression with categorical dependent variables When the dependent variable is categorical,

More information

Hints for Success on the AP Statistics Exam. (Compiled by Zack Bigner)

Hints for Success on the AP Statistics Exam. (Compiled by Zack Bigner) Hints for Success on the AP Statistics Exam. (Compiled by Zack Bigner) The Exam The AP Stat exam has 2 sections that take 90 minutes each. The first section is 40 multiple choice questions, and the second

More information

Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101

Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101 Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam Software Profiling Seminar, 2013 Statistics 101 Descriptive Statistics Population Object Object Object Sample numerical description Object

More information

MAT 12O ELEMENTARY STATISTICS I

MAT 12O ELEMENTARY STATISTICS I LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 12O ELEMENTARY STATISTICS I 3 Lecture Hours, 1 Lab Hour, 3 Credits Pre-Requisite:

More information

Categorical Data Analysis

Categorical Data Analysis Richard L. Scheaffer University of Florida The reference material and many examples for this section are based on Chapter 8, Analyzing Association Between Categorical Variables, from Statistical Methods

More information

MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS. Biostatistics

MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS. Biostatistics MEU. INSTITUTE OF HEALTH SCIENCES COURSE SYLLABUS title- course code: Program name: Contingency Tables and Log Linear Models Level Biostatistics Hours/week Ther. Recite. Lab. Others Total Master of Sci.

More information

SPSS Notes (SPSS version 15.0)

SPSS Notes (SPSS version 15.0) SPSS Notes (SPSS version 15.0) Annie Herbert Salford Royal Hospitals NHS Trust July 2008 Contents Page Getting Started 1 1 Opening SPSS 1 2 Layout of SPSS 2 2.1 Windows 2 2.2 Saving Files 3 3 Creating

More information

Data quality in Accounting Information Systems

Data quality in Accounting Information Systems Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania

More information

When to Use a Particular Statistical Test

When to Use a Particular Statistical Test When to Use a Particular Statistical Test Central Tendency Univariate Descriptive Mode the most commonly occurring value 6 people with ages 21, 22, 21, 23, 19, 21 - mode = 21 Median the center value the

More information

Statistics in Retail Finance. Chapter 6: Behavioural models

Statistics in Retail Finance. Chapter 6: Behavioural models Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics:- Behavioural

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not. Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

More information

Bivariate Analysis. Correlation. Correlation. Pearson's Correlation Coefficient. Variable 1. Variable 2

Bivariate Analysis. Correlation. Correlation. Pearson's Correlation Coefficient. Variable 1. Variable 2 Bivariate Analysis Variable 2 LEVELS >2 LEVELS COTIUOUS Correlation Used when you measure two continuous variables. Variable 2 2 LEVELS X 2 >2 LEVELS X 2 COTIUOUS t-test X 2 X 2 AOVA (F-test) t-test AOVA

More information

SUMAN DUVVURU STAT 567 PROJECT REPORT

SUMAN DUVVURU STAT 567 PROJECT REPORT SUMAN DUVVURU STAT 567 PROJECT REPORT SURVIVAL ANALYSIS OF HEROIN ADDICTS Background and introduction: Current illicit drug use among teens is continuing to increase in many countries around the world.

More information

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

More information

Linda K. Muthén Bengt Muthén. Copyright 2008 Muthén & Muthén www.statmodel.com. Table Of Contents

Linda K. Muthén Bengt Muthén. Copyright 2008 Muthén & Muthén www.statmodel.com. Table Of Contents Mplus Short Courses Topic 2 Regression Analysis, Eploratory Factor Analysis, Confirmatory Factor Analysis, And Structural Equation Modeling For Categorical, Censored, And Count Outcomes Linda K. Muthén

More information

LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016

LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016 UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe

More information

Course Syllabus MATH 110 Introduction to Statistics 3 credits

Course Syllabus MATH 110 Introduction to Statistics 3 credits Course Syllabus MATH 110 Introduction to Statistics 3 credits Prerequisites: Algebra proficiency is required, as demonstrated by successful completion of high school algebra, by completion of a college

More information

4.1 Exploratory Analysis: Once the data is collected and entered, the first question is: "What do the data look like?"

4.1 Exploratory Analysis: Once the data is collected and entered, the first question is: What do the data look like? Data Analysis Plan The appropriate methods of data analysis are determined by your data types and variables of interest, the actual distribution of the variables, and the number of cases. Different analyses

More information