1 / 14

Chapter 14

Chapter 14. Inferential Data Analysis. Analysis of Variance (ANOVA). Used when protocol involves more than two treatment groups Total variability in a set of scores is divided into two or more components Variability values are called sums of squares (SS)

radley
Download Presentation

Chapter 14

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 14 Inferential Data Analysis Conducting & Reading Research Baumgartner et al

  2. Analysis of Variance (ANOVA) • Used when protocol involves more than two treatment groups • Total variability in a set of scores is divided into two or more components • Variability values are called sums of squares (SS) • Determine df for total variability and each SS • Mean square (MS) = SS/df • Ratio of MS values gives F statistic Conducting & Reading Research Baumgartner et al

  3. SST = SSA + SSW SSA = Indication of differences between groups SSW = Indication of differences within a group Conducting & Reading Research Baumgartner et al

  4. Determining the test statistic • dfT = dfA + dfW • dfT = N-1, dfA = K-1, dfW = N-K • MSA = SSA/dfA • MSW = SSW/dfW • F = MSA/MSW with df = (K-1) & (N-K) Conducting & Reading Research Baumgartner et al

  5. Skip: • Repeated Measures ANOVA • Random Blocks ANOVA • Two-way ANOVA, Multiple Scores per Cell • Other ANOVA Designs Conducting & Reading Research Baumgartner et al

  6. Assumptions Underlying Statistical Tests • Interval or continuous scores • Random sampling • Independence of groups • Normal distribution of scores in population (check sample) • When using multiple samples, populations being represented are assumed to be equally variable Conducting & Reading Research Baumgartner et al

  7. Effect Size Is a statistically significant difference also practically significant? ES = (mean group A = mean group B) SD one group or SD pooled groups Conducting & Reading Research Baumgartner et al

  8. Two-Group Comparisons • Aka multiple comparisons or a posteriori comparisons • Typically used to compare groups two at a time after significant F test using ANOVA • Issues to consider: • Per-comparison error rate: • Experiment-wise error rate: • Statistical power: Conducting & Reading Research Baumgartner et al

  9. Per-comparison error rate Experiment-wise error rate Statistical power Conducting & Reading Research Baumgartner et al

  10. Nonparametric tests • Data not interval • Or, data not normal • (often used for small samples) Conducting & Reading Research Baumgartner et al

  11. One-Way Chi-Square Test • Used to test whether hypothesized population distribution is actually observed • Hypothesized percentages = • Compare to • Bigger difference between observed and expected frequencies corresponds to bigger chi-square statistic Conducting & Reading Research Baumgartner et al

  12. Two-Way Chi-Square Test • Used to test whether two variables are independent of each other or correlated • Testing whether frequency of one variable is different in two groups (e.g. by gender) Conducting & Reading Research Baumgartner et al

  13. Multivariate Tests • Each participant contributes multiple scores • ANOVA example: • Use multiple scores to form a composite score which is then tested to see if there is a difference between groups Conducting & Reading Research Baumgartner et al

  14. Prediction-Regression Analysis • Correlation: • Regression: • Prediction: Conducting & Reading Research Baumgartner et al

More Related