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ISBN: 9781292134246
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Understanding Statistics in Psychology with SPSS, 7e (e-Book VS 12m)


By Dennis Howitt

Descripción:

Understanding Statistics in Psychology with SPSS 7th edition, offers students a trusted, straightforward, and engaging way of learning how to carry out statistical analyses and use SPSS with confidence.

Comprehensive and practical, the text is organised by short, accessible chapters, making it the ideal text for undergraduate psychology students needing to get to grips with Statistics in class or independently.

Clear diagrams and full colour screenshots from SPSS make the text suitable for beginners while the broad coverage of topics ensures that students can continue to use it as they progress to more advanced techniques.

Key features

·    Now combines coverage of statistics with full guidance on how to use SPSS to analyse data

·    Suitable for use with all versions of SPSS

·    Examples from a wide range of real psychological studies illustrate how statistical techniques are used in practice

·    Includes clear and detailed guidance on choosing tests, interpreting findings and reporting and writing up research

·    Student focused pedagogical approach including

o   Key concept boxes detailing important terms

o   Focus on sections exploring complex topics in greater depth

o   ‘Explaining statistics sections clarify important statistical concepts’.



Contenido:

1 Why statistics?

 

Overview

1.1 Introduction

1.2 Research on learning statistics

1.3 What makes learning statistics difficult?

1.4 Positive about statistics

1.5 What statistics doesn’t do

1.6 Easing the way

1.7 What do I need to know to be an effective user of statistics?

1.8 A few words about SPSS

1.9 Quick guide to the book’s procedures and statistical tests

Key points

Computer analysis: SPSS Analyze Graphs and Transform drop-down menus

 

Part 1 Descriptive statistics

 

2 Some basics: Variability and measurement

 

Overview

2.1 Introduction

2.2 Variables and measurement

2.3 Major types of measurement

Key points

Computer analysis: Some basics of data entry using SPSS

 

3 Describing variables: Tables and diagrams

 

Overview

3.1 Introduction

3.2 Choosing tables and diagrams

3.3 Errors to avoid

Key points

Computer analysis: Tables, diagrams and recoding using SPSS

 

4 Describing variables numerically: Averages, variation and spread

 

Overview

4.1 Introduction

4.2 Typical scores: mean, median and mode

4.3 Comparison of mean, median and mode

4.4 Spread of scores: range and interquartile range

4.5 Spread of scores: variance

Key points

Computer analysis: Descriptive statistics using SPSS

 

5 Shapes of distributions of scores

 

Overview

5.1 Introduction

5.2 Histograms and frequency curves

5.3 Normal curve

5.4 Distorted curves

5.5 Other frequency curves

Key points

Computer analysis: Frequencies using SPSS

 

6 Standard deviation and z-scores: Standard unit of measurement in statistics

 

Overview

6.1 Introduction

6.2 Theoretical background

6.3 Measuring the number of standard deviations – the z-score

6.4 Use of z-scores

6.5 Standard normal distribution

6.6 Important feature of z-scores

Key points

Computer analysis: Standard deviation and z-scores using SPSS

 

7 Relationships between two or more variables: Diagrams and tables

Overview

7.1 Introduction

7.2 Principles of diagrammatic and tabular presentation

7.3 Type A: both variables numerical scores

7.4 Type B: both variables nominal categories

7.5 Type C: one variable nominal categories, the other numerical scores

Key points

Computer analysis: Crosstabulation and compound bar charts using SPSS

 

8 Correlation coefficients: Pearson’s correlation and Spearman’s rho

 

Overview

8.1 Introduction

8.2 Principles of the correlation coefficient

8.3 Some rules to check out

8.4 Coefficient of determination

8.5 Significance testing

8.6 Spearman’s rho – another correlation coefficient

8.7 Example from the literature

Key points

Computer analysis: Correlation coefficients using SPSS

Computer analysis: Scattergram using SPSS

 

9 Regression: Prediction with precision

 

Overview

9.1 Introduction

9.2 Theoretical background and regression equations

9.3 Confidence intervals and standard error: how accurate are the predicted score and the regression

Key points

Computer analysis: Simple regression using SPSS

 

Part 2 Significance testing

 

10 Samples from populations

 

Overview

10.1 Introduction

10.2 Theoretical considerations

10.3 Characteristics of random samples

10.4 Confidence intervals

Key points

Computer analysis: Selecting a random sample using SPSS

 

11 Statistical significance for the correlation coefficient: Practical introduction to statistical

 

Overview

11.1 Introduction

11.2 Theoretical considerations

11.3 Back to the real world: null hypothesis

11.4 Pearson’s correlation coefficient again

11.5 Spearman’s rho correlation coefficient

Key points

Computer analysis: Correlation coefficients using SPSS

 

12 Standard error: Standard deviation of the means of samples

 

Overview

12.1 Introduction

12.2 Theoretical considerations

12.3 Estimated standard deviation and standard error

Key points

Computer analysis: Standard error using SPSS

 

13 Related t-test: Comparing two samples of related/correlated/paired scores

 

Overview

13.1 Introduction

13.2 Dependent and independent variables

13.3 Some basic revision

13.4 Theoretical considerations underlying the computer analysis

13.5 Cautionary note

Key points

Computer analysis: Related/correlated/paired t-test using SPSS

 

14 Unrelated t-test: Comparing two samples of unrelated/uncorrelated/independent scores

 

Overview

14.1 Introduction

14.2 Theoretical considerations

14.3 Standard deviation and standard error

14.4 Cautionary note

Key points

Computer analysis: Unrelated/uncorrelated/independent t-test using SPSS

 

15 What you need to write about your statistical analysis

 

Overview

15.1 Introduction

15.2 Reporting statistical significance

15.3 Shortened forms

15.4 APA (American Psychological Association) style

Key points

 

16 Confidence intervals

 

Overview

16.1 Introduction

16.2 Relationship between significance and confidence intervals

16.3 Regression

16.4 Writing up a confidence interval using APA style

16.5 Other confidence intervals

Key points

Computer analysis: Examples of SPSS output containing confidence intervals

 

17 Effect size in statistical analysis: Do my findings matter?

 

Overview

17.1 Introduction

17.2 Statistical significance and effect size

17.3 Size of the effect in studies

17.4 Approximation for nonparametric tests

17.5 Analysis of variance (ANOVA)

17.6 Writing up effect sizes using APA style

17.7 Have I got a large, medium or small effect size?

17.8 Method and statistical efficiency

Key points

 

18 Chi-square: Differences between samples of frequency data

 

Overview

18.1 Introduction

18.2 Theoretical issues

18.3 Partitioning chi-square

18.4 Important warnings

18.5 Alternatives to chi-square

18.6 Chi-square and known populations

18.7 Chi-square for related samples – the McNemar test

18.8 Example from the literature

Key points

Computer analysis: Chi-square using SPSS

Recommended further reading

 

19 Probability

 

Overview

19.1 Introduction

19.2 Principles of probability

19.3 Implications

Key points

 

20 One-tailed versus two-tailed significance testing

 

Overview

20.1 Introduction

20.2 Theoretical considerations

20.3 Further requirements

Key points

Computer analysis: One- and two-tailed statistical significance using SPSS

 

21 Ranking tests: Nonparametric statistics

 

Overview

21.1 Introduction

21.2 Theoretical considerations

21.3 Nonparametric statistical tests

21.4 Three or more groups of scores

Key points

Computer analysis: Two-group ranking tests using SPSS

Recommended further reading

 

Part 3 Introduction to analysis of variance

 

22 Variance ratio test: F-ratio to compare two variances

 

Overview

22.1 Introduction

22.2 Theoretical issues and application

Key points

Computer analysis: F-ratio test using SPSS

 

23 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA

 

Overview

23.1 Introduction

23.2 Some revision and some new material

23.3 Theoretical considerations

23.4 Degrees of freedom

23.5 Analysis of variance summary table

Key points

Computer analysis: Unrelated one-way analysis of variance using SPSS

 

24 ANOVA for correlated scores or repeated measures

 

Overview

24.1 Introduction

24.2 Theoretical considerations underlying the computer analysis

24.3 Examples

Key points

Computer analysis: Related analysis of variance using SPSS

 

25 Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?

 

Overview

25.1 Introduction

25.2 Theoretical considerations

25.3 Steps in the analysis

25.4 More on interactions

25.5 Three or more independent variables

Key points

Computer analysis: Unrelated two-way analysis of variance using SPSS

 

26 Multiple comparisons with in ANOVA: A priori and post hoc tests

 

Overview

26.1 Introduction

26.2 Planned (a priori) versus unplanned (post hoc) comparisons

26.3 Methods of multiple comparisons testing

26.4 Multiple comparisons for multifactorial ANOVA

26.5 Contrasts

26.6 Trends

Key points

Computer analysis: Multiple comparison tests using SPSS

Recommended further reading

 

27 Mixed-design ANOVA: Related and unrelated variables together

 

Overview

27.1 Introduction

27.2 Mixed designs and repeated measures

Key points

Computer analysis: Mixed design analysis of variance using SPSS

Recommended further reading

 

28 Analysis of covariance (ANCOVA): Controlling for additional variables

 

Overview

28.1 Introduction

28.2 Analysis of covariance

Key points

Computer analysis: Analysis of covariance using SPSS

Recommended further reading

 

29 Multivariate analysis of variance (MANOVA)

 

Overview

29.1 Introduction

29.2 MANOVA’s two stages

29.3 Doing MANOVA

29.4 Reporting your findings

Key points

Computer analysis: Multivariate analysis of variance using SPSS

Recommended further reading

 

30 Discriminant (function) analysis – especially in MANOVA

 

Overview

30.1 Introduction

30.2 Doing the discriminant function analysis

30.3 Reporting your findings

Key points

Computer analysis: Discriminant function analysis using SPSS

Recommended further reading

 

31 Statistics and analysis of experiments

 

Overview

31.1 Introduction

31.2 The Patent Stats Pack

31.3 Checklist

31.4 Special cases

Key points

Computer analysis: Selecting subsamples of your data using SPSS

Computer analysis: Recoding groups for multiple comparison tests using SPSS

 

Part 4 More advanced correlational statistics

 

32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables

 

Overview

32.1 Introduction

32.2 Theoretical considerations

32.3 Doing partial correlation

32.4 Interpretation

32.5 Multiple control variables

32.6 Suppressor variables

32.7 Example from the research literature

32.8 Example from a student’s work

Key points

Computer analysis: Partial correlation using SPSS

 

33 Factor analysis: Simplifying complex data

 

Overview

33.1 Introduction

33.2 A bit of history

33.3 Concepts in factor analysis

33.4 Decisions, decisions, decisions

33.5 Exploratory and confirmatory factor analysis

33.6 Example of factor analysis from the literature

33.7 Reporting the results

Key points

Computer analysis: Principal components analysis using SPSS

Recommended further reading

 

34 Multiple regression and multiple correlation

 

Overview

34.1 Introduction

34.2 Theoretical considerations

34.3 Assumptions of multiple regression

34.4 Stepwise multiple regression example

34.5 Reporting the results

34.6 Example from the published literature

Key points

Computer analysis: Stepwise multiple regression using SPSS

Recommended further reading

 

35 Path analysis

 

Overview

35.1 Introduction

35.2 Theoretical considerations

35.3 Example from published research

35.4 Reporting the results

Key points

Computer analysis: Hierarchical multiple regression using SPSS

Recommended further reading

 

36 Analysis of a questionnaire/survey project

 

Overview

36.1 Introduction

36.2 Research project

36.3 Research hypothesis

36.4 Initial variable classification

36.5 Further coding of data

36.6 Data cleaning

36.7 Data analysis

Key points

Computer analysis: Adding and averaging components of a measure using SPSS

 

Part 5 Assorted advanced techniques

 

37 Meta-analysis: Combining and exploring statistical findings from previous research

 

Overview

37.1 Introduction

37.2 Pearson correlation coefficient as the effect size

37.3 Other measures of effect size

37.4 Effects of different characteristics of studies

37.5 First steps in meta-analysis

37.6 Illustrative example

37.7 Comparing a study with a previous study

37.8 Reporting the results

Key points

Computer analysis: Some meta-analysis software

Recommended further reading

 

38 Reliability in scales and measurement: Consistency and agreement

 

Overview

38.1 Introduction

38.2 Item-analysis using item–total correlation

38.3 Split-half reliability

38.4 Alpha reliability

38.5 Agreement among raters

Key points

Computer analysis: Cronbach’s alpha and kappa using SPSS

Recommended further reading

 

39 Influence of moderator variables on relationships between two variables

 

Overview

39.1 Introduction

39.2 Statistical approaches to finding moderator effects

39.3 Hierarchical multiple regression approach to identifying moderator effects (or interactions)

39.4 ANOVA approach to identifying moderator effects (i.e. interactions)

Key points

Computer analysis: Regression moderator analysis using SPSS

Recommended further reading

 

40 Statistical power analysis: Getting the sample size right

 

Overview

40.1 Introduction

40.2 Types of statistical power analysis and their limitations

40.3 Doing power analysis

40.4 Calculating power

40.5 Reporting the results

Key points

Computer analysis: Power analysis with G*Power

 

Part 6 Advanced qualitative or nominal techniques

 

41 Log-linear methods: Analysis of complex contingency tables

 

Overview

41.1 Introduction

41.2 Two-variable example

41.3 Three-variable example

41.4 Reporting the results

Key points

Computer analysis: Log-linear analysis using SPSS

Recommended further reading

 

42 Multinomial logistic regression: Distinguishing between several different categories or groups

 

Overview

42.1 Introduction

42.2 Dummy variables

42.3 What can multinomial logistic regression do?

42.4 Worked example

42.5 Accuracy of the prediction

42.6 How good are the predictors?

42.7 Prediction

42.8 Interpreting the results

42.9 Reporting the results

Key points

Computer analysis: Multinomial logistic regression using SPSS

 

43 Binomial logistic regression

 

Overview

43.1 Introduction

43.2 Typical example

43.3 Applying the logistic regression procedure

43.4 Regression formula

43.5 Reporting the results

Key points

Computer analysis: Binomial logistic regression using SPSS

 

Appendices

 

Appendix A Testing for excessively skewed distributions

Appendix B1 Large-sample formulae for the nonparametric tests

Appendix B2 Nonparametric tests for three or more groups

Computer analysis: Kruskal–Wallis and Friedman nonparametric tests using SPSS

Appendix C Extended table of significance for the Pearson correlation coefficient

Appendix D Table of significance for the Spearman correlation coefficient

Appendix E Extended table of significance for the t-test

Appendix F Table of significance for chi-square

Appendix G Extended table of significance for the sign test

Appendix H Table of significance for the Wilcoxon matched pairs test

Appendix I Tables of significance for the Mann–Whitney U-test

Appendix J Tables of significance values for the F-distribution

Appendix K Table of significance values for t when making multiple t-tests

 

Glossary

References

Index