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Module 5: t-Test and SEM Intro

Module 5: t-Test and SEM Intro. Rosseni Din Muhammad Faisal Kamarul Zaman Nurainshah Abdul Mutalib. Types. Independent-samples Compare mean scores of 2 different groups Paired-samples Compare mean of the same group on 2 different occasions Only comparing 2 groups or 2 conditions

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Module 5: t-Test and SEM Intro

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  1. Module 5:t-Test and SEM Intro Rosseni Din Muhammad Faisal Kamarul Zaman Nurainshah Abdul Mutalib

  2. Types • Independent-samples • Compare mean scores of 2 different groups • Paired-samples • Compare mean of the same group on 2 different occasions • Only comparing 2 groups or 2 conditions • More than that use variance

  3. Independant • It needs • One categorical variable / independent variable • One continuous variable / dependant variable • What the test will do • It will tell you whether there is a statistically significant difference in the mean scores for the 2 groups. • Assumptions needed

  4. Paired • One group but 2 different occasion / conditions • E.g. pre/post test • Requirements: the same as independent • One categorical independent • One continuous, dependent variable • It will tell you whether there is a statistically significant in the mean scores

  5. Data Analysis Using SPSS t-test

  6. t-test • Used to test whether there is significant difference between the means of two groups, e.g.: • Male v female • Full-time v part-time

  7. t-test • Typical hypotheses for t-test: • There is no difference in affective commitment (affcomm) between male and female employees • There is no difference in continuance commitment (concomm) between male and female employees • There is no difference in normative commitment (norcomm) between male and female employees

  8. Performing T-test Analyze → Compare Means → Independent-Samples T-test

  9. Analyze Compare Means

  10. Independent-Samples T Test

  11. Performing T-test • Select the variables to test (Test Variables), in this case: • affcomm • concomm • norcomm • And bring the variables to the “Test Variables” box

  12. Test variables are selected and carried to the box on the right by pressing thearrow

  13. The test variables: affcomm, concomm, and norcomm

  14. Performing T-test • Select the grouping variable, i.e. gender; bring it to the “grouping variable” box • Click “Define Groups”

  15. Gender is the grouping variable

  16. Performing T-test • Choose “Use specified values” • Key in the codes for the variable “gender” as used in the “Value Labels”. In this case: 1 - Male 2 - Female • Click “Continue”, then “OK”

  17. Specified values for gender are: 1 (Male) and 2 (Female)

  18. T-Test: SPSS Output Mean scores for “Male” on the three test variables The mean scores for “Female”

  19. T-test: SPSS Output 1 2 3 (1) Sig. is 0.306 (> 0.05) so there is no significant difference in the variances of the two groups (2) so the row “Equal variances assumed” will be used to read the sig. of t-test (3) Sig. level for t-test is 0.035 (<0.05) Therefore there is a significant difference in the levels of affective commitment (affcomm) between male and female employees.

  20. From the SPSS output, we are able to see that the means of the respective variables for the two groups are: Affective commitment (affcomm) Male 3.49720 Female 3.38016 Continuance commitment (concomm) Male 3.18838 Female 3.15159 Normative commitment (norcomm) Male 3.24090 Female 3.27540

  21. T-test: Interpretation • For the variable “affcomm” • Levene’s Test for Equality of Variances shows that F (1.048) is not significant (0.306)* therefore the “Equal variances assumed” row will be used for the t-test. * This score (sig.) has to be 0.05 or less to be considered significant.

  22. T-test: Interpretation • Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. • The score is 0.035 (which is less than 0.05), therefore there is a significant difference between the means of the two groups.

  23. T-test: Interpretation 2 1 3 • Sig. is 0.021 (<0.05), there is significant difference between the variances • The row “Equal variances not assumed” is used for interpreting the t-test • The relevant significant level for t-test is 0.503 (>0.05) • Therefore, there is no significant difference between the two groups

  24. T-test: Interpretation • For the variable “concomm” • Levene’s Test for Equality of Variances shows that F (5.353) is significant (0.021)* therefore the “Equal variances not assumed” row will be used for the t-test. * This score (sig.) is less than 0.05, so there is significant different in the variances of the two groups.

  25. T-test: Interpretation • Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances not assumed”. • The score is 0.503 (which is more than 0.05), therefore there is no significant difference between the means of the two groups.

  26. T-test: Interpretation 2 1 • The sig. is 0.418 (>0.05) so there is no significant difference between the variances • “Equal variances assumed” will be used to determine t-test • The Sig. of t-test is 0.497 (>0.05) • Therefore there is no significant difference between the means of the two groups

  27. T-test: Interpretation • For the variable “norcomm” • Levene’s Test for Equality of Variances shows that F (0.656) is not significant (0.418)* therefore the “Equal variances are assumed” row will be used for the t-test. * This score (sig.) is more than 0.05, so there is no significant different in the variances of the two groups.

  28. T-test: Interpretation • Under the “t-test for Equality of Means” look at “Sig. (2-tailed)” for “Equal variances assumed”. • The score is 0.497 (which is more than 0.05), therefore there is no significant difference between the means of the two groups.

  29. Hands-on exercise Use survey3ED.sav from www.allenandunwin.com/spss OR http://rosseni.wordpress.com/2011/07/15/spss-for-beginners/

  30. Procedure for independent-sample t-test 1. Analyze > Compare means > independent samples t-test 2. Move the dependent (continuos) variable (e.g. total self-esteem) > Test Variable Box 3. Move the independent (categorical) variable (e.g. sex) > Grouping Variable

  31. Procedure for independent-sample t-test 4. Click define groups > type in the numbers used in the data set to code each group. In the curent data file, 1=males, 2=females; therefore, in the Group 1 box type 1; Group 2 box type 2; * if you cannot remember the codes used, right click on the variable name and then choose Variable Information from the pop-up box that appears. This will list the codes and labels 5. Click continue > ok

  32. Intro to SEM Structural Equation Modeling

  33. Purpose of the Study The study  development of a model for meaningful e-Training by blending conventional and computer mediated communication to cater to learners with differentiated LS preferences. In this study we call it the Hybrid eTrainingmethod.

  34. The Extension: Conceptual Framework of a Hybrid E-Training System (HiTs)

  35. Overall Research Framework Develop, implement and evaluate a hybrid system implementation that caters learners with differentiated learning style preferences, achieve meaningful learning

  36. Content Cooperativity Delivery Intentionality Service Construction Structure Activity Meaningful e-Training (MeT) Hybrid e-Training (HiTs) Outcome Authenticity Learning Style Preference (LSP) Individual Kinesthetic Tactual Auditory Visual Group Overall Research Framework

  37. n = 213 ICT trainers/trainees studying as postgraduate students/graduating fourth year students participated in the Technology for Thinking/Computer Education course in the year 2008

  38. Overview of the Analytical Approach Prelim Analysis Modeling Procedures FF analysis 3 2a 2b 1a 1b Validate measurementmodels: HiT model specification; estimation; fit assessment; path adequacy; SMC Validateother CFA models : MeT and LSPmodel specification; estimation; fit assessment; path adequacy; SMC Test the full-fledged model Formulate hypotheses; operationalize variables; examine distributional assumption Item analysis; reliability analysis principal Component analysis; 2c Test structural model: (1) HiT MeTand (2) LSP  HiTmodel specifications; estimation; fit assessment; path adequacy; SMC Confirmatory Modeling Strategy

  39. Hypothesized HiT in relation to MeT and LSP . . .

  40. Reliability of the Instruments Hybrid E-Training (HiT)Measureα =.93 Learning Style Preference (LSP) α =.88 Meaningful e-Training (MeT) Measure  α=.89

  41. HiT Measurement Model

  42. LSP Measurement Model MeT Measurement Model

  43. Structural Relationship of HiTMeT

  44. Structural Relationship of LSPHiTs

  45. Coverage I. Statement of problem • Objectives of the study • Extension of the current hybrid model II. Method • Setting; sample; • Modeling procedure III. Results • Measurement model • Structural model • Full-fledged model IV. Conclusion

  46. Overview of the Analytical Approach Prelim Analysis Modeling Procedures FF analysis 3 2a 2b 1a 1b Validate measurement models: HiT model specification; estimation; fit assessment; path adequacy; SMC Validate other CFA models : MeT and LSP model specification; estimation; fit assessment; path adequacy; SMC Test the full-fledged model Formulate hypotheses; operationalize variables; examine distributional assumption Item analysis; reliability analysis principal Component analysis; 2c Test structural model: (1) HiT MeTand (2) LSP  HiTmodel specifications; estimation; fit assessment; path adequacy; SMC Confirmatory Modeling Strategy

  47. Adequacy of the full fledge Integrated Meaningful Hybrid E-Training (I-MeT) Model

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