Introduces undergraduates to the design and statistical analysis of common experiments. Concepts are explained with step-by-step descriptions, worked examples, and an extensive series of exercises. Written for students who meet the standard quantitative prerequisites for entry into most colleges and universities.
Part 1 Experimenta l design and preliminary data analysis: introduction to experimental design - getting started, how do psychologists conduct research?, experimental research design, summary, exercises; preliminary data analysis - the mean as a measure of central tendency, the variance as a measure of variability, additional descriptive techniques, summary, exercises. Part 2 The analysis of single-factor experiments: the logic of hypothesis testing - neutralizing nuisance variables through randomization, index of the treatment effects, hypothesis testing, summary, exercises; calculating the F ratio - design and notation, partitioning the total sum of squares, sums of squares - computational formulas, the analysis of variance, summary, exercises; evaluating the F ratio - the sampling distribution of F, determining the critical value of F, forming the decision rule, assumptions underlying the analysis, a complete numerical example, special analysis with two treatment conditions, summary, exercises, appendix: an explanation of the correction for unequal variances; analytical comparisons in the single-factor design - the nature of analytical comparisons, an example of the relationship between research hypotheses and analytical comparisons, analyzing differences between pairs of means, more complex analytical comparisons, summary, exercises, appendix: using the t test to analyze single-df comparisons; estimating population means and effect size - interval estimation in experiments, the magnitude of treatment effects, summary, exercises; errors of hypothesis testing and statistical power - statistical errors in hypothesis testing, cumulative type 1 error, using power to estimate sample size, summary, exercises, appendix: an alternative method for estimating sample size. Part 3 The analysis of factorial designs: introduction to the analysis of factorial experiments - the factorial experiment, main effects and interaction, identifying basic deviations, the analysis of variance, calculating sums of squares, a numerical example, summary, exercises; analytical comparisons in the factorial design - interpreting F tests in the factorial design, the detailed analysis of main effects, analyzing simple effects, analyzing simple comparisons, an overall plan of analysis, summary, exercises, appendix: analyzing interaction contrasts. Part 4 The analysis of within-subjects designs: the single-factor within-subjects design - reducing error variance, logic of the analysis of within-subjects designs, computational formulas for the analysis of variance, a numerical example, analytical comparisons, planning within-subjects designs, summary, exercises, appendix: using separate error terms to evaluate analytical comparisons; the mixed within-subjects factorial design - a comparison of factorial designs, the logic of the analysis, analysis of variance, a numerical example, analytical comparisons, summary, exercises; part contents.