Beginning Statistics for Psychology

I. INTRODUCTION TO PSYCHOLOGICAL RESEARCH. 1. An introduction to psychological research. Two basic research designs. Some psychological studies. Study 1: Teaching social skills to intellectually disabled students. Study 2: Children's concepts of illness and the body. Study 3: Subclassification of hyperactive children. Study 4: The effects of television cartoons and associated toys on children's behavior. Summary and plan for the book. 2. Research design. Types of validity. Completely randomized two-group designs. Two matched-group designs. Matched-subjects designs. Repeated-measures (or within-subjects) designs. Quasi-experimental two-group designs. Observational studies. Research ethics. Summary. Problem set. II. DESCRIBING DATA. 3. Types of data. Types of data. Measurement scales. Data analysis. Summary. Problem set. 4. Frequency distributions. Frequency distributions. Graphing distributions. Stem and leaf displays. Cumulative frequency distributions. Software examples. Summary. Problem set. 5. Summarizing distributions. Measuring the center of a distribution. The mean. The median. The mode. Measuring the spread of a distribution. The standard deviation. Quartiles and percentiles. The semi-interquartile range. Boxplots. Transformations to symmetry. Standard scores. Software examples. Summary. Problem set. III. INTRODUCTION TO STATISTICAL INFERENCE. 6. Population distributions. Population distributions, mean and variance. The normal distribution. The binomial distribution. Software examples. Summary. Problem set. 7. Samples and populations. Random sampling. Other random sampling methods. Sampling variability. Sampling distribution of the mean. Summary. Problem set. 8. Probability. Assigning numerical values to probability. Rules for probability of composite events. Probability distributions. The normal distribution. The binomial distribution. Software examples. Summary. Problem set. 9. Hypothesis testing. Testing a hypothesis. Calculating the P-value. One- or two-tailed tests? Hypothesis testing - another view. Summary. Problem set. 10. Hypothesis testing and design issues. The outcome of the study. Effect size. Defining the population of interest. Type of design. Sample sizes. Defining treatments and measures. Summary. Problem set. IV. ONE-SAMPLE TESTS. 11. Introduction to statistical tests: the Z-test. Statistical testing procedure. The Z-test. The test assumptions. Software examples. Summary. Problem set. 12. An introduction to confidence intervals. Confidence interval for ...109 when known. Software examples. Summary. Problem set. 13. The t-test of ...109 = ...109o. The test procedure. The test assumptions. Confidence interval for ...109 when is unknown. Software examples. Summary. Problem set. 14. The binomial test. The sign test. A generalization: the binomial test. Procedure for large samples. Confidence interval. Software examples. Summary. Problem set. 15. The chi-square goodness-of-fit test. The test procedure. The test assumptions. Software examples. Summary. Problem set. V. TWO-SAMPLE TESTS. 16. The t-test of equality of means for independent samples. The test procedure. The test assumptions. Confidence interval for ...1091 - ...1092 from independent samples. Software examples. Summary. Problem set. 17. Rank test for equality of locations of independent samples. The test procedure. Test assumptions. Procedure for tied ranks. Using the test with ordinal data. Test procedure for large samples. Software examples. Summary. Problem set. 18. The chi-square test for homogeneity of proportions. The chi-square test for homogeneity of proportions in 2 x 2 contingency tables. The chi-square test for homogeneity of proportions in r x c contingency tables. Further analysis of an r x c table. Software examples. Summary. Problem set. 19. The t-test of equality of means for related samples. The test procedure. Confidence intervals. The test assumptions. Software examples. Summary. Problem set. 20. The rank test for equality of location for related samples. The test procedure. Procedure for tied ranks. Test assumptions. Test procedure for large samples. Software examples. Summary. Problem set. Tests for related samples for categorical measures. Test procedure for two response categories. Test for symmetry with more than two response categories. Software examples. Summary. Problem set. VI. BIVARIATE DATA. 22. Measuring association between variables. The correlation coefficient. The rank correlation coefficient. Measures of association for categorical measures. Hypothesis testing for correlation coefficients. The chi-square test of independence of two categorical variables. Correlation and causation. Software examples. Summary. Problem set. 23. An introduction to linear regression. The least squares regression line. Measuring spread around the regression line. Correlation and the regression line. Testing the slope of the regression line. Assumptions. The three-median regression line. Software examples. Summary. Problem set. 24. Postscript. APPENDICES. A. Data from the four studies. B. An introduction to the software packages. MINITAB. SPSS/PC+. SYSTAT. C. Statistical tables.