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1. Analyzing and Interpreting Data 1/21/2015 1

2. ‘All meanings, we know, depend on the key of interpretation.’ -George Eliot 1/21/2015 2

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4. Effective Data Analysis Effective data analysis involves keeping your eye on the main game managing your data engaging in the actual process of quantitative and / or qualitative analysis presenting your data drawing meaningful and logical conclusions 1/21/2015 4

5. Key terms 1/21/2015 5 Variables Level of measurements Statistical Assumptions Reliability and Validity Research Hypotheses and Research Questions Acceptance and Rejection of test Result

6. Variables A variable is defined as a characteristic of the participants or situation for a given study that has different values in that study. Operational definitions of variables. An operational definition describes or defines a variable in terms of the operations or techniques used to make it happen or measure it. Specification of variables Independent Variables Dependent variables Mediating variables Moderating Variables Extraneous Variables 1/21/2015 6

7. Levels of Measurement 1/21/2015 7

8. Levels of Measurement Nominal Gender Male, Female Vaccinations Yes, No, Unsure Ordinal Personal health status Excellent, Very good, Good, Fair, Poor Last 30 days Never used, Not in last 30 days, 1-2 days, 3-5 days, 6-9 days, 10-19 days, 20-29 days, All 30 days Interval Body Mass Index (BMI) Ratio Number of drinks Number of sexual partners Perception percentages Blood alcohol concentration (BAC) 1/21/2015 8

9. Statistical Assumptions 1/21/2015 9 Assumptions explain when it is and isn't reasonable to perform a specific statistical test. Parametric tests. These include most of the familiar ones (e.g., T test, analysis of variance, Pearson correlation) Nonparametric tests. These tests (e.g., chi-square, Spearman) Parametric tests have more assumptions than nonparametric tests. Parametric tests were designed for data that have certain characteristics, including approximately normal distributions. Some parametric statistics have been found to be "robust" to one or more of their assumptions (ANOVA, normality)

10. Common Assumption 1/21/2015 10 Homogeneity of variances (Levene test) Normality (skewness and kurtosis) Independence of observations Linearity

11. Reliability and Validity Reliability The extent to which a test is repeatable and yields consistent scores Validity The extent to which a test measures what it is supposed to measure 1/21/2015 11

12. Research Hypotheses and Research Questions 1/21/2015 12

13. Data collection and Data analysis 1/21/2015 13 Data sources Data collection procedure Questionnaire development Data coding and data entry Data preparing

14. Acceptance and Rejection of test Result 1/21/2015 14 One tailed test, two tailed test Confidence of interval Sig. value

15. SPSS 1/21/2015 15 Preparing Data file Cleaning Data file Data reduction procedure (Validity and Reliability) Exploratory factor analysis Reliability analysis Computing variables Descriptive Analysis Correlation Analysis

16. Regression Analysis 1/21/2015 16 Predict outcome (dependent variable) from one or more independent variables Implies causality Used to explore relationships and assess contributions Types of Regression Multiple regression Stepwise regression Logistic regression Estimation methods Ordinary least squares (Minimize the sum of squared residuals) Generalized least squares (in case of multicollinearity and heteroscedasticity) Maximum-likelihood estimation (  distribution of the error terms is known to belong to a certain parametric family  ƒ θ  of probability distributions )

17. Regression Analysis 1/21/2015 17 Regression equation Assumptions of Regression Linearity Normality Multicollinearity Moderation/Mediation model estimation

18. Stepwise Regression

19. Mediating Model

20. Baron and Kenny’s

21. Four Regression Models NIF-------BI NIF----ATT ATT----BI NIF,ATT-----BI

22. Preacher and Hayes

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24. Preacher and Hayes

25. Moderation 1/21/2015 46 interaction term Four steps

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30. Structural Equation Modeling Structural equation modeling is a general term that has been used to describe a large number of statistical models used to evaluate the validity of substantive theories with empirical data Model specification Measurement Model (CFA) Structural Model (Hypothesis Testing)

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37. Confirmatory Factor Analysis 1/21/2015 58 Square Multiple Correlations (SMC). Relative amount of variance of dependent variable caused by independent variables (>.2) Average Variance Extracted (AVE). The variance of the indicators of latent variables which is explained by the common factor(>.5) Composite Reliability (CR). Multidimensional construct (>.7) Reliability (cronbach alpha)

38. Confirmatory Factor Analysis 1/21/2015 59 Convergent validity. The extent to which different measures of a construct that should be theoretically related are in fact related. Average variance extracted (AVE) values above 0.50 are considered as the evidence of constructs convergent validity. Convergent validity of the construct is established if CR > AVE (Fornell & Larcker, 1981) Discriminant Validity Discriminant Validity The extent to which two constructs are different from one another. Construct discriminant validity is established using correlation analysis. Correlation value less than 0.85 is an indication of scales discriminant validity. Another way of establishing discriminant validity is examination of fit indices i.e. AIC or ECVI.

39. Model Fit Indices

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48. Hypothesis Testing 1/21/2015 69

49. Thank you !!!!