"Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on the author's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the core principles that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and applied research. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples."--Back cover.

1. Prelude : why do I need to learn statistics? -- Nature of findings and facts in the Behavioral Sciences -- Statistical significance and effect size -- Descriptive and inferential statistics -- A conceptual approach to teaching and learning statistics -- The nature of this book -- How to approach this class and what you should get out of it -- Key terms -- 2. Describing a single variable -- Variables, values, and scores -- Types of variables -- Describing scores for a single variable -- Indices of central tendency -- Indices of variability (and the sheer beauty of standard deviation!) -- Rounding -- Describing frequencies of values for a single variable -- Representing frequency data graphically -- Describing data for a categorical variable -- a real research example -- Summary -- Key terms -- 3. Standardized scores -- When a Z-score equals 0, the raw score it corresponds to must equal the mean -- Verbal scores for the Madupistan Aptitude Measure -- Quantative scores for the Madupistan Aptitude Measure -- Every raw score for any variable corresponds to a particular Z-score -- Computing Z-scores for all students for the Madupistan Verbal test -- Computing raw scores from z-scores -- Comparing your GPA of 3.10 from Solid State University with Pat's GPA of 1.95 from Advanced Technical University -- Each z-score for any variable corresponds to a particular raw score -- Converting z-scores to raw scores (the dorm resident example) -- A real research example -- Summary -- Key terms -- 4. Correlation -- Correlations are summaries -- Representing a correlation graphically -- Representing a correlation mathematically -- Return to Madupistan -- Correlation does not imply causation -- A real research example -- Summary -- Key terms -- 5. Statistical prediction and regression -- Standardized regression -- Predicting scores on Y with different amounts of information -- Beta weight-- Unstandardized regression equation -- The regression line -- Quantitatively estimating the predictive power of your regression model Interpreting rp2s -- A real research example -- Conclusion -- Key terms -- 6. The basic elements of hypothesis testing -- The basic elements of inferential statistics -- The normal distribution -- A real research example -- Summary -- Key terms -- 7. Introduction to hypothesis testing -- The basic rationale of hypothesis testing -- Understanding the broader population of interest -- Population versus sample parameters -- The five basic steps of hypothesis testing -- A real research example -- Summary -- Key terms -- 8. Hypothesis testing in N>1 -- The distribution of means -- Steps in hypothesis testing if N>1 -- Confidence intervals -- A real research example -- Summary -- Key terms. 9. Statistical power -- What is statistical power? -- An example of statistical power -- Factors that affect statistical power -- A real research example -- Summary -- Key terms -- 10. t-tests (one-sample and within-groups) -- One-sample t-test -- Steps for hypothesis testing with a one-sample t-test -- Here are some simple rules to determine the sign of t with a one-sample t-test -- Computing effect size with a one-sample t-test -- how the t-test is biased against small samples -- The within-group t-test --Steps in computing the within-group t-test -- Computing effect size with a within-group t-test -- A real research example -- Summary -- Key terms -- 11. The between-groups t-test -- Elements of the between-groups t-test -- Effect size with the betwee-groups t-test -- Another example -- Real research example -- Summary -- Key terms -- 12. Analysis of variance -- ANOVA as a signal-detection statistic -- An example of the one-way ANOVA -- What can and cannot be inferred from ANOVA (The importance of follow-up tests) -- Estimating effect size with the one-way ANOVA -- Real research example -- Summary -- Key terms -- 13. Chi square and hypothesis-testing with categorical variables -- Chi square test of goodness of fit -- Steps in hypothesis testing with chi square goodness of fit -- What can and cannot be inferred from a significant chi square -- Chi square goodness of fit testing for equality across categories -- Chi square test of independence -- Real research example -- Summary -- Key terms -- Appendix A. Cumulative standardized normal distribution -- Appendix B. t distribution : critical values of t -- Appendix C.F distribution : critical values of F -- Appendix D. Chi square distribution: critical values of xp2s -- Appendix E. Advanced statistics to be aware of (Advance forms of ANOVA) -- Appendix F. Using SPSS -- SPSS data entry lab -- Syntax files, recoding variables, compute statements, out files, and the computation of variables in SPSS -- How to recode items for the Jealousy data and compute composite variables -- Descriptive statistics -- Frequencies, descriptives and histograms -- The continuous variable -- The categorical variable -- Correlations -- Regression -- t-tests -- ANOVA with SPSS -- Post Hoc tests -- Homogeneous subsets -- Factorial ANOVA -- Chi square -- Crosstabs -- Glossary. Why Do I Need to Learn Statistics? -- Describing a Single Variable -- Standardized Scores -- Correlation -- Statistical Prediction and Regression -- The Basic Elements of Hypothesis Testing -- Introduction to Hypothesis Testing -- Hypothesis Testing if N> 1 -- Statistical Power -- t-tests (One-Sample and Within-Groups) -- The Between-Groups t-test -- Analysis of Variance -- Chi-Square and hypothesis-testing with categorical variables.