Students will learn to use the statistical methods common in linguistics and related fields in order to apply them in the design and analysis of their own research. Methods covered will include ANOVA, ANCOVA, correlation, regression, and non-parametric tests, as well as some specialized analyses suc…
Not pooling items or repetitions, working backwards from degrees of freedom, correlation and regression, multiple regression, measurement scales, non-parametric statistics, chi-squared
review of reporting ANOVA, requirements for presentations
By-subjects and by-items ANOVAs continued, counterbalancing factors, adding a counterbalanced group factor in the by-subjects ANOVA, random factors, Linear Mixed Effects Modeling, regression, intro. to correlation
subjects and items continued, by-subjects and by-items ANOVAs, factors in by-subjects and by-items ANOVAs
not-fully-crossed designs, smaller ANOVAs within not-fully-crossed designs, the concept of multiple items for each subject and condition
Mixed designs (examples in SPSS), splitting a factor, collapsing a between-subjects factor, collapsing a within-subjects factor, write-up
Mixed Designs, within and between subjects factors, follow-up tests, interactions, collapsing factors, splitting factors
Unequal number of subjects cont'd, balance of some data vs. no data vs. enough data, varying the order of conditions, practice effects, blocking
1-factor within subjects design cont'd, Unequal number of Subjects, Review of types of comparisons
1-factor within-subjects design, sphericity assumption, error term, AxS interaction
4-factor, 5-factor, 6-factor designs (between subjects), interactions, follow-up tests, intro to ANCOVA
3-factor design, continued (between-subjects), 3-way interactions, follow-up tests, interpreting interactions, review for midterm
3-factor between subjects design, 3-way interactions, follow-up tests
2-factor ANOVA, continued: interaction, example of SPSS output, interpreting the results, testing simple effects
Familywise error, correcting for familywise error, power, and introduction to 2-factor ANOVA
Statistical power continued, how to increase power, error variability, number of subjects, Type I errors, effect size
Statistical power, Effect size, how to get greater power, how to calculate effect size, power at the planning stage vs. post hoc power analysis
Planned and post hoc comparisons, pairwise and complex comparisons, continued
Violations of assumptions of ANOVA continued, comparisons (planned vs. post hoc, pairwise vs. complex)
Counfounds in experimental design, random assignment of subjects to conditions, violations of assumptions of ANOVA
How to do a 1-factor ANOVA, continued. Calculating the F ratio.
Intro. to how to do a 1-factor between subjects ANOVA