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- 1. Quantitative Analysis June 25th, 2014 By Dr. James Lani Statistics Solutions
- 2. Data Cleaning and Preparation • Select the correct analysis (RQ and level of measurement • Clean your data • Describe variables • Conduct the analyses/assess assumptions • Present the findings • Summarize the findings Putting the Pieces TogetherDescribe Variables Clean Data Present & Summarize Findings Conduct Analyses/ Assess Assumptions
- 3. Quantitative Results Strategy Garbage In, Garbage Out Assess data for outliers (±3.29); Multiple imputation for missing data; Create composite score (with reverse coding if necessary); Conduct Cronbach’s alpha (α); Assess for normality
- 4. Descriptive Statistics Means & Standard Deviations, Frequency & Percentages Variable n % Location Urban 72 48.0 Rural 78 52.0 Ethnicity White 36 24.0 Hispanic 13 28.7 Other 71 47.3 Table 1 Frequencies and Percentages for Nominal Variables
- 5. Chi-Square Goodness of Fit & Test of Independence Chi-square analysis answers what research questions? Assumptions of analysis: • Each cell has count of 1; • 80% of cells have an expected value of 5. Conducting analysis; Presenting findings; Write up in narrative; Tables and figures. Republican Democrat Green Independent Libertarian X X X X X Repub. Democrat Green Indep. Libert. Male X X X X X Female X X X X X
- 6. Pearson Correlation Examines the relationship between two or more scales level variables Assumptions of the analysis: • Linearity • Homoscedacity • Normality Assumptions Linear Non-Linear Homoscedasticity Met Heteroscedasticity Normal Non-normal
- 7. Independent Samples t-test Let’s look at differences in IQ by Gender Examines mean differences on a scale level dependent variable by a dichotomous nominal level independent variable. Assumptions of analysis: • Homogeneity of variance • Normality Males Females Part 1=1 Part 4=2 Part 2=2 Part 5=3 Part 3=3 Part 6=4 X=2 X=3 Males Females Part 1=1.9 Part 4=2.9 Part 2=2.0 Part 5=3.0 Part 3=2.1 Part 6=3.1 X=2 X=3
- 8. One-Way ANOVA Let’s look at differences on Scores by Political Affiliation Examines mean differences on a scale level dependent variable by a dichotomous nominal level independent variable. Assumptions of analysis: • Homogeneity of variance • Normality Males Females Independent Part 1=1.9 Part 4=2.9 Part 7=3.9 Part 2=2.0 Part 5=3.0 Part 8=6.0 Part 3=2.1 Part 6=3.1 Part 9=8.1 X=2 X=3 X=6
- 9. Dependent Samples t-test Let’s look at differences between science scores Pretest vs. Posttest Examines the mean difference between two paired scale level variables. Assumptions of analysis: • Normality Science Pretest Science Posttest Part 1=1 Part 1=2 Part 2=2 Part 2=3 Part 3=3 Part 3=4 X=2 X=3 Science Pretest Science Posttest Part 1=1.9 Part 1=2.9 Part 2=2.0 Part 2=3.0 Part 3=2.1 Part 3=3.1 X=2 X=3
- 10. Repeated-Measures ANOVA Let’s look at differences among test scores Pretest vs. Posttest vs. Follow Up Examines mean differences among two or more scale level variables Assumptions of analyses: • Sphericity • Homogeneity of variance Science Pretest Science Posttest Science Follow-Up Part 1=1.9 Part 1=2.9 Part 1= Part 2=2.0 Part 2=3.0 Part 2= Part 3=2.1 Part 3=3.1 Part 3= X=2 X=3 X=
- 11. Linear Regression Does IQ predict Creativity? Examines if one or more scale, ordinal, or nominal level independent variables predict a scale level dependent variable. Assumptions of analysis: • Normality, Multicollineality, Homoscedastcity IV DV
- 12. Regressions: Multiple, Logistic, Ordinal, Multinomial It’s all about the level of measurement of the DV
- 13. Mediation Analysis Does Education mediate the relationship between IQ and Creativity? Examines if one scale level mediator variable explains the relationship between a scale level independent variable and a scale level dependent variable Assumptions of analysis: • Assumptions of regression 3 Regression Equations IV M; must be significant IV DV; must be significant M, IV M, IV DV; IV is no longer significant Education (M) IQ (IV) Creativity (DV)
- 14. Moderation Analysis Does Age moderate the relationship between IQ and Creativity? Examines if one scale level moderator variable strengthens or weakens the relationship between a scale level independent variable and a scale level dependent variable Assumptions of analysis: • Assumptions of regression Regression with 2 blocks Step 1: IQ and Age entered Step 2: Interaction term entered Moderation is supported if interaction is significant. Age (Mod) IQ (IV) IQ x Age Interaction Creativity Note. To avoid multicollinearity, center IV/Mod (subtract mean), then create the interaction term.
- 15. Moderation Analysis (Continued) Does Age moderate the relationship between IQ and Creativity? Variable names Name of independent variable: IV Meaning of moderator value “0” Men Intercept/Constant: 3 Meaning of moderator value “1” Women Unstandardised Regression Coefficients Independent variable: 0.6 Moderator: 0.4 Interaction: -0.8 Means/SD’s of variables Mean of independent variable:0 SD of independent variable: 1 www.jeremydawson.co.uk/slopes.html
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- 17. I’m All Yours: Questions and Answers
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