Research Methods in Human-Computer Interaction Chapter 4- Statistical Analysis

Overview Preparing data for statistical analysis Descriptive statistics Comparing means T tests Analysis of variance (ANOVA) Assumptions of t tests and F tests Identifying relationships Regression Nonparametric statistical tests

Preparing data for analysis Cleaning up data Detect errors Formatting Coding Types of data that need to be coded Be consistent Organizing the data Accommodate to the requirements of statistical software

Descriptive statistics Measures of central tendency Mean Median Mode Measures of spread Range Variance Standard deviations

Comparing means Summary of methods

Comparing 2 means: T tests Independent-samples t test: between-group design

Comparing 2 means: T tests Paired-sample t test: within-group design

Comparing 2 or more means: Analysis of variance (ANOVA) Also called F tests One-way ANOVA: for between-group design Data layout: Table 4.6 Results summary:

Factorial ANOVA For between-group design 2 or more independent variables involved Data layout: table 4.9

Factorial ANOVA Summary results

Repeated measures ANOVA For within-group design Can investigate one or more variables One-way ANOVA

Repeated measures ANOVA One way ANOVA summary report:

Repeated measures ANOVA Two way ANOVA experiment design:

Repeated measures ANOVA Two way ANOVA data layout

Repeated measures ANOVA Two way ANOVA summary report:

Split-plot ANOVA Involves both between-group and within-group factors Experiment design

Split-plot ANOVA data layout

Split-plot ANOVA summary report

Assumptions of t tests and F tests Errors should be independent of each other Errors should be identically distributed Errors should be normally distributed

Identify relationships Correlation: Two factors are correlated if there is a relationship between them Most commonly used test for correlation is the Pearson’s product moment correlation coefficient test Pearson’s r: ranges between -1 to 1 Pearson’s r square represents the proportion of the variance shared by the two variables

Identify relationships Correlation does not imply causal relationship

Identify relationships Regression: can investigate the relationship between one DV and multiple IVs Regression is used for 2 purposes: Model construction Prediction Different regression procedures Simultaneous Hierarchical

Non-parametric tests Non-parametric tests are used when: The error is not normally distributed The distances between any two data units are not equal The variance of error is not equal

Non-parametric tests CHI-square test Used to analyze categorical data Table of counts (contingency table) Assumptions of the test Data points need to be independent The sample size should not be too small

Non-parametric tests Two groups of data Three or more groups of data For between-group design: Mann–Whitney U test or the Wald–Wolfowitz runs test For within-group design: Wilcoxon signed ranks test Three or more groups of data For between-group design: Kruskal–Wallis one-way analysis of variance by ranks For within-group design: Friedman’s two-way analysis of variance test

End-of-chapter Summary Discussion questions Research design exercise