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Straightforward statistics : understanding the tools of research

Author: Glenn Geher; Sara Hall
Publisher: New York ; Oxford : Oxford University Press, [2014]
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"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  Read more...
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Document Type: Book
All Authors / Contributors: Glenn Geher; Sara Hall
ISBN: 9780199751761 0199751765
OCLC Number: 858799800
Description: xvii, 455 pages : illustrations ; 27 cm
Contents: 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 r² --
A real reasearch 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 x² --
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.
Responsibility: Glenn Geher and Sara Hall.

Abstract:

This book is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Students should find this book easy useful and engaging in its presentation while instructors  Read more...

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"The book is very readable for students that may not be comfortable with a large number of formulas. Explanations are extensively textual in form and diagrams are used where needed. If you are Read more...

 
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