ࡱ> M Dbjbj== %WWw@lvvvvvvvzTzTzT8T\Ul>WW(WWWWWW$  vWWWWW]vvWW]]]WvWvW]W]]cvv)WzW OzTZq)0>$Ĥ6\rĤ)]vvvvStatistical Tools for Research ---SPSS (2) Topic: Quantitative Data Analysis (Intermediate) Date: April 14 & 15, 2003 Time: 6:00 - 8:30pm Venue: B0415 & B0416 Facilitators: Dr. Zhang Wei-yuan (CRIDAL, OUHK) Ms. Elaine Kwok (CRIDAL, OUHK) This is the second session of workshops on quantitative data analysis using Statistical Package for the Social Science (SPSS) for Windows. This workshop will describe some basic statistical concepts and introduce techniques in One-Way ANOVA (Analysis of Variance), Reliability Analysis, Non-Parametric Techniques, and Multiple Response and Multiple Dichotomy analysis. Recommended reading: Norusis, M. J. (2000). SPSS10.0: Guide to Data Analysis, New Jersey: Prentice Hall. Ferguson, G. A. & Takane, Y. (1989). Statistical Analysis in Psychology and Education, 6th ed,. New York: McGraw-Hill Publishing Company. Mertens, D. M. (1998). Research Methods in Education & Psychology: Integrating Diversity with Quantitative & Qualitative Approaches, California: Sage Publications. Wiersma, W. (2000). Research Methods in Education: An Introduction Research, 7th edn, MA, USA: Allyn & Bacon. Lesson 5: To Run One-Way ANOVA (Analysis of Variance) Comparing more than two population means Example: If you use four different methods for teaching English, you want to compare average test scores for all four groups. Independent variable and dependent variable Independent variable: a variable that affects (or is assumed to affect) the dependent variable under study and is included in the research design so that its effect can be determined. Dependent variable: a variable being affected or assumed to be affected by the independent variable. Example 1: The effect of four teaching methods on reading scores on students. Independent variable: teaching methods Dependent variable: reading scores Example 2: Peoples average number of working hours are affected by their educational levels Independent variable: educational levels (less than high school; high school; junior college; bachelor; and graduate). Dependent variable: the average number of hours worked in a week To obtain a one-way analysis of variance (ANOVA): You must indicate the variable whose mean you want to compare, and move it into Dependent List Select the variable whose values define the groups and move it into Factor box Click OK Exercise: >Open file gssft >Click Analyze - Compare Means - One-way ANOVA >Select the variable hrs1 and move it into Dependent List >Select the variable degree and move it into Factor box. >Click OK Bonferroni Multiple Comparison Test Many multiple comparison procedures are available. One of the simplest is the Bonferroni procedure. Select the variable hrs1 and move it into Dependent List >Select the variable degree and move it into Factor box >click Post Hoc and tick Bonferroni >Set significance level at 0.05 or 0.01 >click Continue and then OK The difference in hours worked between the two groups is shown in the column labeled Mean Difference. Pairs of means that are significantly different form each other marked with an asterisk. Results: People with graduate degree work significantly longer than people with less than a high school education; People with graduate degree work significantly longer than people with just a high school education. Exercise Repeating the sample above. Use the Gss.savdata file: Is there a relationship between highest degree earned and number of hours of television viewed a day (variable degree & tvhours)? Dependent variable: the average number of hours of TV viewed a day Independent variable: educational levels (less than high school; high school; junior college; bachelor; & graduate). Further Reading: Norusis, M. J (2000) SPSS10.0: Guide to Data Analysis, New Jersey: Prentice Hall. 259 277. Lesson 6: Reliability Analysis Reliability means consistency. It is the degree to which an instrument will give similar results for the same individuals at different times. Reliability can take on values of 0 to 1.0, inclusive. Methods for checking Reliability: Test-retest reliability The calculation of test-retest reliability is straightforward. The same test is administrated on two occasions to the same individuals under the same conditions. This yields two scores for each person and the correlation between these two sets of scores is the test-retest reliability coefficient. If the test is reliable, there will be a high positive association between the scores. Exercise: The scores of 20 students in language proficiency test and retest Inputting the following data StudentTestRetest194962928738891487865878968686785898959198584108386118284128177137881147671157276166872176666186572196359205855 To conduct a reliability analysis Analyze-Correlate Bivariate click Pearson and Flag Move Test and Retest to Variables-click OK Pearson Correlation Result: r = 0.947 Split half Only need one administration. The test items are divided into two halves, with the items of the two halves matched on content and difficulty. Exercise: Interest Inventory (RIASEC) Five-point scale: very much like me 5 somewhat like me 4 neither like nor unlike me 3 somewhat unlike me 2 very much unlike me 1 Social type Easy to talk with all kinds of people Good at explaining things to others Enjoying working as a neighbourhood organiser Teach children easily Teach adults easily Help people who are upset or troubled Good understanding of social relationships Good at teaching others Making people fell at ease Better at working with people than things or ideas Inputting the following data It1It2It3It4It5It6It7It8It9It10125314521242222222222232221212122412111421125252223212261112223111723213231238141511211193224223122101111121113To conduct a reliability analysis Analyze Scale Reliability analysis >Move the variables (i.e. It1 to It10) into the Items box >In Model box select Split-half >Click Statistics-under the Descriptive for-click Scale& Scale if item deleted; and then under the Inter-Item-click Correlations >Click Continue and then OK Report: Guttman Split-half = 0.6164 If deleting item it4, the Alpha will raise to 0.7860. Cronbach alpha Prof. Lee J. Cronbach, Stanford University. Attitude scales Five point Likert scale format Strongly agree Agree Undecided Disagree Strongly disagree (5) (4) (3) (2) (1) e.g. Please circle the choice after each statement that indicates your opinion. Students can learn to become a scientist without losing their cultural values. Science alienates people from their traditional culture. Exercise: Scale for Measuring Attitudes Towards Mathematics or Science. Strongly agree Agree Undecided Disagree Strongly disagree I want to develop my mathematical (science) skills and study this subject more.54321Mathematics (science) is not a very interesting subject.54321Mathematics (science) is a very worthwhile and necessary subject.54321Mathematics (Science) makes me feel nervous and uncomfortable.54321I have usually enjoyed studying mathematics (science) in school.54321I dont want to take any more mathematics (science) than I absolutely have to.54321Other subjects are more important to people than mathematics (science).54321I am very calm and unafraid when studying mathematics (Science).54321I have seldom liked studying mathematics (Science).54321I am interested in acquiring further knowledge of mathematics (science).54321 Inputting the following data S1S2S3S4S5S6S7S8S9S10Q14333333333Q22124351322Q34443533353Q41234333323Q55243442133Q61344133433Q75433424233Q84243243133Q92324353223Q105343133333 Positively worded items from the questionnaire: 1, 3, 5, 8, 10 Negatively worded items from the questionnaire: 2, 4, 6, 7, 9 Recoding value Recoding negatively worded items: 2, 4, 6, 7, 9 Transform Recode into Same Variables move variable(s) to be recoded into Numeric variables, i.e. 2, 4, 6, 7, 9 >Click Old and new variable- from old value-click Value and put 1; from new value-click Value and put 5 >click Add >Repeat the same for value (old value 2=new value 4; old value 3=new value 3; old value 4=new value 2; old value 5=new value 1) >Click Continue >Click OK Or Transform Recode into Different Variables move variable(s) to be recoded into Numeric variables- >Click Output Variable >Under Name-type in a new name for this variable >Under Label-type in a new label for this variable >Click Change >Click Old and new variable- from old value-click Value and put 1; from new value-click Value and put 5 >click Add >Repeat the same for value (old value 2=new value 4; old value 3=new value 3; old value 4=new value 2; old value 5=new value 1) >Click Continue >Click OK To conduct a reliability analysis >Analyze Scale Reliability Analysis Move the variables (i.e. Q1 to Q10) into the Items box >In Model box select Alpha >Click Statistics-under the Descriptive for-click Scale& Scale if item deleted; and then under the Inter-Item-click Correlations >Click Continue and then OK Report: Cronbach Alpha Results: Alpha = 0.6572 If deleting item 7, the Alpha will raise to 0.7904. Lesson 7: Non-Parametric Techniques For the most non-parametric analyses, assumptions about the shape of the population are not required. For that reason, they are often used when small sample sizes are involved. Chi-square test for goodness of fit (One-sample Chi-square test: only one variable) Exercise: A researcher is interested in the factors that are involved in course selection. A sample of 50 students is asked, Which of the following factors is most important to you when selecting a course? Students must choose one and only one of the following alternatives. Interest in course topic Ease of passing the course Instructor for the course Time of day course is offered The frequency distribution of responses for this sample is as follows: Interest in topic26Ease of passing12Course instructor7Time of day5 Inputting the following data Factors (factor)Frequency (freq)11 (Interest in topic)2622 (Ease of passing)1233 (Course instructor)744 (Time of day)5 >Go to tool bar Data - Weight Cases - move Freq into Frequency Variable box-click OK >Analyze Nonparametric Tests Chi-square move Factor into the Test Variable List box-click OK Results: P = 0.000 < 0.05 Conclusion: some of the factors are more important than others in course selection. Chi-square test for relatedness or independence (2 x 2 table) Exercise: Purpose: To determine whether or not the gender is an important factor in students course selection Sample: 74 male students and 72 female students Preference for course selecting MaleFemaleScience4626Social science2845 Inputting the following data Labels Gender: 1=male 2=female Preference of courses (pref): 1=science 2=social science GenderPreference of courses (pref)Number of frequency (freq)11146212283212642245 >Go to tool bar Data - Weight Cases - move Frequency into Frequency Variable box-click OK >Analyze Descriptives statistics Crosstabs move pref into Row(s) move gender into column(s) 1 Analyses P =. 002 < 0.01 The test is valid. (.0%) < 20% and 32.26 > 1 Conclusion: Gender is factor which influences on students course selection. Lesson 8: Multiple Responses and Multiple Dichotomy Analysis Multiple responses and multiple dichotomy analysis are commonly used in the analysis of questionnaire or survey data. Open-ended questions More than one choices Multiple Responses Open-ended questions: What important factors do you consider when you choose jobs? Or: What factors do you consider when you choose jobs? Use of ability ( Working conditions ( Secure and stable employment ( Chance to advance ( Status/prestage ( Job opportunity ( Interest ( Benefit to society ( Personal qualifications ( Salary ( Challenging ( Independent ( Location ( Working time ( Survey results: The maximum number of responses obtained from an individual was six. The fourteens factors were identified. Data: Six variables: crit1; crit2; crit3; crit4; crit5; crit6. 99: No answer Participant 1: 01 06 99 99 99 99 Participant 2: 02 04 06 11 14 99 Participant 3: 01 03 08 09 10 13 Participant 4: 01 04 06 12 13 99 Participant 5: 02 05 07 09 10 12 Exercise: Inputting the following data Crit1Crit2Crit3Crit4Crit5Crit6Case 11699999999Case 2246111499Case 313891013Case 4146121399Case 525791012 To run a multiple response to the above data: > Go to Analyze- Multiple Response- Define Sets Define Multiple Response Sets > Move the variables from Set Definition (i.e. crit1 to crit 6) into Variables in Set box. > In the Variables Are Coded as tick Categories Under the Range: . through .. box - put 1 as the lowest code and 14 as the highest code Under the Name-type a suitable variable name ($crits) Under the Label-type a description of this variable (e.g. Factor considered in choosing jobs) >Click Add >Click Close > Go to Analyze- Multiple Response Frequencies >Move Selection criteria ($crits) from Mult Response Sets into Table(s) for >Click OK Result: Percentage of responses refers to the proportion of a given response in relation to the count: count/total responses. Percentage of cases refers to the proportion of a given response in relation to the number of valid cases: count/total valid cases. Multiple Dichotomy Analysis Multiple dichotomy analysis is very similar to the multiple response analysis. Exercise: Question: please tick the important reasons why you study at the Open University. ___ Job change ___ Professional development ___ Earning university degree ___ Personal interest __ Career advancement Data Each item would be given a variable (labels) If the item is ticked, give 1 If the item is not ticked, give 0 No items were ticked from one case, tick 9 (no answer) The data from the first participants may look as follows: Participant 1 0 1 1 0 0 (This participant ticked 2 & 3) Participant 2 1 1 1 1 1 (This participant ticked all five) Participant 3 9 9 9 9 9 (This participant didnt tick any items) Participant 4 1 1 0 0 0 (This participant ticked the first two) Inputting the following data JobProfeDegreeInterestcareerPart101100Part211111Part399999Part411000 To run a multiple dichotomy analysis: > Go to Analyze- Multiple Response- Define Sets Define Multiple Response Sets > Move the variables from Set Definition (i.e. job, profe, degree, interest, career) into Variables in Set box. In the Variables Are Coded as tick Dichotomies Counted value and type the value that you assigned to those items which were ticked by respondents (i.e. 1) Under the Name-type a suitable variable name (Reasons) Under the Label-type a description of this variable (The reasons why choosing OU) >Click Add >Click Close > Go to Analyze- Multiple Response Frequencies >Move $reasons from Mult Response Sets into Table(s) for >Click OK Result: Percentage of responses refers to the proportion of a given response in relation to the count: count/total responses. Percentage of cases refers to the proportion of a given response in relation to the number of valid cases: count/total valid cases. 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