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Produktbild: Straightforward Statistics

Straightforward Statistics Understanding the Tools of Research

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.05.2014

Verlag

Oxford University Press

Seitenzahl

476

Maße (L/B/H)

26/18,3/3 cm

Gewicht

898 g

Sprache

Englisch

ISBN

978-0-19-975176-1

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.05.2014

Verlag

Oxford University Press

Seitenzahl

476

Maße (L/B/H)

26/18,3/3 cm

Gewicht

898 g

Sprache

Englisch

ISBN

978-0-19-975176-1

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  • Produktbild: Straightforward Statistics

    • Preface

    • 1: Why Do I Need to Learn Statistics?

    • - Examples of statistics in the real world

    • - The nature of findings and facts in the behavioral sciences

    • - Descriptive and Inferential Statistics

    • - A conceptual approach to teaching and learning statistics

    • - What you should get out of this class

    • 2. Describing a Single Variable

    • - The nature of variables: continuous vs. categorical

    • - Frequency distributions as descriptions of single variables

    • - Creating frequency distributions

    • - Representing frequency distributions graphically

    • - Interpreting frequency distributions

    • - Mean, median, and mode

    • - Why is the mean the most-utilized index of central tendency?

    • - The conceptual elements of standard deviation

    • - Computing standard deviation

    • 3. Standardized Scores

    • - Why are standardized scores needed in the real world?

    • - Why are standardized scores needed in statistics?

    • - Computing Z scores

    • - Interpreting Z scores

    • - A real research example

    • - Summary

    • 4. Correlation

    • - Real-world examples of correlations

    • - Representing correlations graphically (the scatterplot)

    • - Representing correlations quantitatively (Pearson's r: an index of correlation strength and direction)

    • - Computing r using Z-scores

    • - Interpreting r (what you can and cannot conclude knowing that a correlation between two variables exists)

    • - A real research example

    • - Summary

    • 5. Statistical Prediction and Regression

    • - The basic rationale underlying regression

    • - Standardized model of bivariate regression

    • - Raw-score model of bivariate regression

    • - The regression line

    • - Estimating error of prediction

    • - Basic rationale underlying multiple regression

    • - A real research example

    • - Summary

    • 6. The Basic Elements of Hypothesis Testing

    • - Probability

    • - The normal distribution

    • - Estimating likelihood of outcomes

    • - A real research example

    • - Summary

    • 7. Introduction to Hypothesis Testing

    • - Basic rationale underlying hypothesis testing

    • - What is meant by statistical significance?

    • - The five steps of hypothesis testing:

    • - Stating the null and research hypotheses

    • - Delineating the nature of the comparison distribution

    • - Determining alpha (by defining a part of the comparison distribution is highly unlikely)

    • - Comparing a sample from the special population with the comparison distribution

    • - Commenting on the null hypothesis

    • - A real research example

    • - Summary

    • > 1

    • - The basic steps of hypothesis testing always remain the same

    • - The comparison distribution needed for comparing a sample mean: The distribution of means

    • - Hypothesis testing using the distribution of means

    • - Confidence intervals

    • - A real research example

    • - Summary

    • 9. Statistical Power

    • - Defining Power (p(rejecting the null hypothesis when the research hypothesis is true) and Beta (p(Type-II error))

    • - How N, population-level standard deviation, and effect size affect power

    • - Computing power

    • - How power affects real research

    • - A real research example

    • - Summary

    • 10. t-tests (One-Sample and Within-Groups)

    • - How a t-test differs from a Z-test

    • - The nature of the t-distribution (and why it varies as it does)

    • - Computing a one-sample t-test

    • - Computing a repeated-measures t-test

    • - A real research example

    • - Summary

    • 11. t-tests: Between-Groups

    • - The basic rationale of the between-groups t-test

    • - Computing the between-groups t-test

    • - Interpreting results

    • - A real research example

    • - Summary

    • 12. Analysis of Variance

    • - Basic reasoning of F as a ratio between effect and error variance

    • - Concepts underlying a one-way ANOVA

    • - Computing a one-way ANOVA

    • - Factorial ANOVA

    • - What results from an ANOVA can and cannot tell you

    • - Post-hoc tests

    • - A real research example

    • - Summary

    • 13. Chi-Square

    • - What happens when all our variables are categorical?

    • - Basic rationale underlying goodness of fit test

    • - Computing the chi-square goodness of fit

    • - Computing the chi-square test of independence

    • - Interpreting chi-square results

    • - A real research example

    • - Summary

    • Appendix A: Normal Curve (Z) Table

    • Appendix B: t Table

    • Appendix C: F Table

    • Appendix D: Chi Square Table

    • Appendix E: Advanced Statistics You May Run Into

    • - Factor Analysis

    • - Multiple regression

    • - Structural Equation Modeling

    • - Repeated-Measures ANOVA

    • - Mixed-Design ANOVA

    • - MANOVA

    • Appendix F: Using SPSS to Compute Basic Statistics

    • - Benefits of SPSS

    • - Different kinds of SPSS files

    • - Entering data with SPSS

    • - Computing frequency distributions with SPSS

    • - Describing variables with SPSS

    • - Using SPSS to examine correlations

    • - Using SPSS for a repeated-measures test

    • - Using SPSS for a between-groups test

    • - Using SPSS for a one-way ANOVA

    • Glossary

    • Answers to Set B Homework Problems

    • References

    • Index