Study Guide Exam 3 Fall 2014 Based on chapters 10, 11, & 15. This is just a guide, read the assigned chapters. Also, keep in mind that all statistics is partially-cumulative (i.e., you will see concepts covered before in other chapters such as mean, standard deviation, SS etc.) Independent samples t-test: - When to use (also comparison with related/dependent samples t-test) - Advantages/disadvantages over dependent/related samples - Hypothesis testing - Null and alternative hypothesis - Calculations: df, pooled variance, estimated standard error, t - Using the t table - Example calculations: - Computing pooled variance from SS and n - Computing estimated std. error from SS and n - Finding critical t based on n and/or df for alpha levels of .05 and .01 Dependent (Related) samples t-test: - When to use (also comparison with independent samples t-test) - How does a matched-subjects design look like and when it is used - Advantages/disadvantages over independent samples - Order effects - Hypothesis testing - Null and alternative hypothesis - Calculations: all based on sample of difference scores - Using the t table - Example calculations: - Computing MD from raw scores - Finding critical t based on n and/or df for alpha levels of .05 and .01Correlation - Linear relationship - Pearson’s r: meaning, when to use - Positive and negative r, strength of the relationship, statistical significance - Computing r, df, SS for x and y, SP - Computing SP from raw data - Computing r from SSx , SSy and SP - Computing df from n and n from df - Null and alternative hypothesis for correlation - Accuracy in prediction (squaring r) - Hypothesis testing - Identifying positive and negative correlations from graphs and description of studies - What a scatter plot tells us: “data points that are clustered close to a line that slopes down to the right” SPSS: - Interpreting SPSS output from independent, related/dependent and correlation analysis