2009 - Andy FIELD - Discovering Statistics Using SPSS

7/24/2019 2009 - Andy FIELD - Discovering Statistics Using SPSS

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THIRD EDITION

DISCOVERING STATISTICS

USING SPSS

and sex and drugs and rock n roll)

N Y FIELD



ONT NTS

Preface

How to use this book

Acknowledgements

Dedication

Symbols used in this book

Some maths revision

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Why is my evi ecturer forcing me to earn statistics

1 1 What will this chapter tell me? <D

1 2 What the hell am I doing here? I don t belong here <D

1 2 1 The research process <D

1 3 Initial observation: finding something that needs explaining <D

1 4 Generating theories and testing them <D

1 5 Data collection 1 : what to measure <D

1 5 1 Variables <D

1 5 2 Measurement error <D

1 5 3 Validity and reliability <D

1 6 Data collection 2: how to measure <D

1 6 1 Correlational research methods <D

1 6 2 Experimental research methods <D

1 6 3

Randomization

<D

1 7 Analysing data

<D

1 7 1 Frequency distributions <D

1 7 2 The centre of a distribution <D

1 7 3 The dispersion in a distribution

<D

1 7 4 Using a frequency distribution to go beyond the data <D

1 7 5 Fitting statistical modeis to the data <D

What have I discovered about statistics? <D

Key terms that I ve discovered

Smart Alex s stats quiz

Further reading

Interesting real research

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vi

DISCOVERING STATISTICS USING SPSS

Everything you ever wanted to

know about

statistics

well, sort of

2 What will this chapter tell me? CD 31

2.2. Building statistical mod eis CD 32

2.3.

Populations and samples

CD

34

2.4. Simple statistical modeis CD 35

2.4.1. The mean a very simple statistical model CD 35

2.4.2. Assessing the fit of the mean: sums of squares variance and standard

deviations CD 35

2.4.3. Expressing the mean as amodel ® 38

2.5. Going beyond the data CD 40

2.5.1.

The standard error

CD 40

2.5.2.

Confidence intervals

®

43

2.6. Using statistical modeis to test research questions CD 48

2.6.1.

Test statistics

CD

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2.6.2.

One- and two-tailed tests

CD

54

2.6.3. Type I and Type II errors CD 55

2.6.4. Effect sizes

®

56

2.6.5. Statistical power ® 58

What have I discovered about statistics? CD 59

Key terms that I ve discovered 59

Smart Alex s stats quiz 59

Further reading 60

Interesting real research 60

4

The SPSSenvironment

3.1. What will this chapter tell me? CD

3.2.

Versions of SPSS CD

3.3. Getting started

CD

3 4

The data editor CD

3.4.1. Entering data into the data editor CD

3.4.2. The Variable View CD

3.4.3. Missing values CD

3.5.

The SPSS viewer

CD

3.6.

The SPSS SmartViewer CD

3.7. The syntax window

@

3.8. Saving files CD

3.9. Retrieving a file CD

What have I discovered about statistics? CD


Smart Alex s tasks

Further reading

Online tutoriais

Exploring data with graphs

4.1.

What will this chapter tell me?

CD

4.2. The art of presenting data CD

4.2.1.

What makes a good graph?

CD

4.2.2. Ues damned lies and ... erm graphs CD

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CONTENTS

4 3 The SPSS Chart Builder

CD

4 4 Histograms a good way to spot obvious problems CD

4 5 Boxplots box-whisker diagrams)

CD

4 6 Graphing means: bar charts and error bars CD

4.6.1. Simple bar charts for independent means CD

4.6.2. Clustered bar charts for independent means CD

4.6.3. Simple bar charts for related means

CD

4.6.4. Clustered bar charts for related means CD

4.6.5. Clustered bar charts for mixed designs CD

4 7 Line charts CD

4 8

Graphing relationships the scatterplot

CD

4.8.1. Simple scatterplot

CD

4.8.2. Grouped scatterplot CD

4.8.3. Simple and grouped 3-0 scatterplots CD

4.8.4. Matrix scatterplot CD

4.8.5. Simple dot plot or density plot

CD

4.8.6. orop-line graph CD

4 9 Editing graphs CD



Smart Alex s tasks

Further reading

Online tutorial


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5

ExpLoring assumptions

5 What will this chapter tell me? CD

5 2

What are assumptions? CD

5 3 Assumptions of parametric data

CD

5 4 The assumption of normality CD

5.4.1. Oh no, it s that pesky frequency distribution again checking

normality visually CD

5.4.2. Quantifying normality with numbers

CD

5.4.3. Exploring groups of data CD

5 5 Testing whether a distribution is normal CD

5.5.1. ooing the Kolmogorov-Smirnov test on SPSS

CD

5.5.2. Output from the explore procedure CD

5.5.3. Reporting the K-S test CD

5 6 Testing for homogeneity of variance CD

5.6.1. Levene s test CD

5.6.2. Reporting Levene s test

CD

5 7 Correcting problems in the data ®

5.7.1. oealing with outliers ®

5.7.2. oealing with non-normality and unequal variances ®

5.7.3. Transforming the data using SPSS

®

5.7.4. When it all goes horribly wrong @

What have I discovered about statistics?

CD

Key terms that I v e discovered

Smart Alex s tasks

Online tutorial

Further reading

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6

Correlation


7

6 What will this chapter tell me? CD

6 2

Looking at relationships

CD

6 3

How do we measure relationships?

CD

6.3.1. A detour into the murky world of covariance

CD

6.3.2. Standardization and the correlation coefficient CD

6.3.3. The significance of the correlation coefficient @

6.3.4. Confidence intervals for

r@

6.3.5. A word of warning about interpretation: causality

CD

6 4

Data entry for correlation analysis using SPSS

CD

6 5 Bivariate correlation CD

6.5.1. General procedure for running correlations on SPSSCD

6.5.2. Pearson s correlation coefficient

CD

6.5.3. Spearman s correlation coefficient

CD

6.5.4. Kendall s tau non-parametric)

CD

6.5.5. Biserial and point-biserial correlations @

6 6 Partial correlation ®

6.6.1. The theory behind part and partial correlation ®

6.6.2. Partial correlation using SPSS®

6.6.3. Semi-partial or part) correlations ®

6 7

Comparing correlations @

6.7.1. Comparing independent

@

6.7.2. Comparing dependent

@

6 8 Calculating the effect size CD

6 9

How to report correlation coefficents

CD



Smart Alex s tasks

Further reading

Online tutorial


Regression

7


CD

7 2 An introduction to regression CD

7.2.1. Some important information about straight lines eD

7.2.2. The method of least squares CD

7.2.3. Assessing the goodness of fit: sums of squares,

and

CD

7.2.4. Assessing individual predictors

CD

7 3 Doing simple regression on SPSS CD

7 4

Interpreting a simple regression CD

7.4.1. Overall fit of the model

CD

7.4.2. Model parameters

CD

7.4.3. Using the model

CD

7 5 Multiple regression: the basics ®

7.5.1. An example of a multiple regression model®

7.5.2. SulTisof squares, and ®

7.5.3. Methods of regression

®

7 6

How accurate is my regression model? ®

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ONTENTS

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7.6.1. Assessing the regression modeil: diagnostics ®

7.6.2. Assessing the regression modelll: generalization ®

How to do multiple regression using SPSS ®

7.7.1. Some things to think about before the analysis

®

7.7.2. Main options ®

7.7.3. Statistics

®

7.7.4. Regression plots ®

7.7.5. Saving regression diagnostics ®

7.7.6. Further options ®

Interpreting multiple regression ®

7.8.1. Descriptives ®

7.8.2. Summary of model ®

7.8.3. Model parameters ®

7.8.4. Excluded variables ®

7.8.5. Assessing the assumption of no multicollinearity ®

7.8.6. Casewise diagnostics

®

7.8.7. Checking assumptions

®

What

if

I violate an assumption? ®

How to report multiple regression ®

Categorical predictors and multiple regression ®

7.11.1. Dummy coding ®

7.11.2. SPSS output for dummy variables ®

What have I discovered about statistics? eD


Smart Alex s tasks

Further reading

Online tutorial


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Logistic regression

64

8 1 What will this chapter tell me?

eD

264

8

Background to logistic regression

eD

265

8 3 What are the principles behind logistic regression? ® 265

8.3.1. Assessing the model the log likelihood statistic® 267

8.3.2. Assessing the model

and

®

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8.3.3. Assessing the contribution of predictors the Wald statistic® 269

8.3.4. The odds ratio Exp B ® 270

8.3.5. Methods of logistic regression

®

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8 4

Assumptions and things that can go wrong 273

8.4.1. Assumptions ® 273

8.4.2. Incomplete information from the predictors 273

8.4.3. Complete separation 274

8.4.4. Overdispersion 276

8 5

Binary logistic regression an example that will make you feel eel ® 277

8.5.1. The main analysis ® 278

8.5.2. Method of regression ® 279

8.5.3. Categorical predictors ® 279

8.5.4. Obtaining residuals ® 280

8.5.5. Further options ® 281

8 6

Interpreting logistic regression ® 282



x DISCOVERING STATISTICS USING SPSS

8 7

8 8

8 9

8.6.1. The initial model ®

8.6.2. Step l intervention @

8.6.3. Listing predicted probabilities ®

8.6.4. Interpreting residuals ®

8.6.5. Calculating the effect size ®

How to report logistic regression

®

Testing assumptions another example ®

8.8.1. Testing for linearity of the logit @

8.8.2. Testing for multicollinearity @

Predicting several categories multinomiallogistic regression @

8.9.1. Running multinomiallogistic regression in SPSS@

8.9.2. Statistics @

8.9.3. Other options @

8.9.4. Interpreting the multinomiallogistic regression output @

8.9.5. Reporting the results



Smart Alex s tasks

Further reading

Online tutorial


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Comparing two means

9 What will this chapter tell me? eD

9

Looking at differences

eD

9.2.1. A problem with error bar graphs of repeated measures designs eD

9.2.2. Step 1 calculate the mean for each participant ®

9.2.3. Step 2 calculate the grand mean®

9.2.4. Step 3 calculate the adjustment factor ®

9.2.5. Step 4 create adjusted values for each variable ®

9 3 The t t st

eD

9.3.1. Rationale for the t t st eD

9.3.2. Assumptions of the t t st eD

9 4 The dependent t t st

eD

9.4.1. Sampling distributions and the standard error

eD

9.4.2. The dependent t t st equation explained eD

9.4.3. The dependent t t st and the assumption of normality

eD

9.4.4. Dependent t tests using SPSS eD

9.4.5. Output from the dependent t t st eD

9.4.6. Calculating the effect size®

9.4.7. Reporting the dependent t t st

eD

9 5 The independent t t st eD

9.5.1. The independent

t t st

equation explained eD

9.5.2. The independent t t st using SPSS

eD

9.5.3. Output from the independent

t t st eD

9.5.4. Calculating the effect size®

9.5.5. Reporting the independent t t st eD

9 6

Between groups or repeated measures? eD

9 7 The t t st as a generallinear model ®

9 8

What if my data are not normally distributed? ®

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CONTENTS


CD


Smart Alex s task

Further reading

Online tutorial


10 Comparing several means: ANOVA GLM 1

1 1

What will this chapter tell me? CD

1 2

The theory behind ANOVA

®

1 2 1

Inflated error rates

®

1 2 2

Interpreting F

®

1 2 3

ANOVA as regression

®

1 2 4

Logic of the F-ratio ®

1 2 5

Total sum of squares SST)

®

1 2 5

Model sum of squares SSM)

®

1 2 7

Residual sum of squares SSR)®

1 2 8

Mean squares ®

1 2 9

The F-ratio ®

1 2 1

Assumptions of ANOVA @

1 2 11

Planned contrasts

®

1 2 12 Post hoc

procedures

®

1 3 Running one-way ANOVA on SPSS ®

1 3 1

Planned comparisons using SPSS ®

1 3 2 Post hoc

tests in SPSS

®

1 3 3

Options ®

1 4

Output from one-way ANOVA ®

1 4 1

Output for the main analysis ®

1 4 2

Output for planned comparisons

®

1 4 3

Output for post hoc tests ®

1 5

Calculating the effect size

®

1 6

Reporting results from one-way independent ANOVA

®

10.7. Violations of assumptions in one-way independent ANOVA ®



Smart Alex s tasks

Further reading

Online tutoriais


11 Analysis of covariance, ANCOVA GLM 2

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What will this chapter tell me? ®

What is ANCOVA?

®

Assumptions and issues in ANCOVA @

11 3 1

Independence of the covariate and treatment effect @

11 3 2

Homogeneity of regression slopes @

Conducting ANCOVA on SPSS

®

11 4 1

Inputting data

CD

11 4 2

Initial considerations: testing the independence of the independent

variable and covariate ®

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DISCDVERING STATISTICS USING SPSS

11 5

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4 3 The main analysis ®

4 4 Contrasts and other options ®

Interpreting the output from ANCOVA ®

5 What happens when the covariate is exciuded7

®

5 2 The main analysis ®

5 3

Contrasts

®

5 4 Interpreting the covariate ®

ANCOVA run as a multiple regression ®

Testing the assumption of homogeneity of regression slopes

®

Calculating the effect size ®

Reporting results ®

What to do when assumptions are violated in ANCOVA ®

What have I discovered about statistics? ®


Smart Alex s tasks

Further reading

Online tutoriais


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2 FactoriaL ANOVA GLM 3

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®

Theory of factorial ANOVA between-groups) ®

2 2 Factorial designs ®

2 2 2 An example with two independent variables ®

2 2 3 Total sums of squares SST)®

2 2 4 The model sum of squares SSM)®

2 2 5

The residual sum of squares SSR)

®

2 2 6 The F-ratios ®

Factorial ANOVA using SPSS ®

2 3 Entering the data and accessing the main dialog box

®

2 3 2 Graphing interactions

®

2 3 3 Contrasts ®

2 3 4 Post hoc

tests ®

2 3 5

Options

®

Output from factorial ANOVA

®

2 4

Output for the preliminary analysis

®

2 4 2 Levene s test ®

2 4 3 The main ANOVA table ®

2 4 4 Contrasts ®

2 4 5 Simple effects analysis ®

2 4 6 Post hoc

analysis ®

Interpreting interaction graphs ®

Calculating effect sizes ®

Reporting the results of two-way ANOVA

®

Factorial ANOVA as regression ®

What to do when assumptions are violated in factorial ANOVA

®


®


Smart Alex s tasks

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ONTENTS

Further reading

Online tutoriais


13 Repeated-measures designs GLM 4

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13 1

13 2

13 3

13.4.

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13 11

13 12

What will this chapter tell me? ®

Introduction to repeated-measures designs ®

13.2.1. The assumption of sphericity

®

13.2.2. How is sphericity measured? ®

13.2.3. Assessing the severity of departures from sphericity ®

13.2.4. What is the effect of violating the assumption of sphericity?

®

13.2.5. What do you do if you violate sphericity? ®

Theory of one-way repeated-measures ANOVA ®

13.3.1. The total sum of squares SST)®

13.3.2. The within-participant SSw)

®

13.3.3. The model sum of squares SSM)®

13.3.4. The residual sum of squares SSR)®

13.3.5. The mean squares

®

13.3.6. The F-ratio ®

13.3.7. The between-participant sum of squares®

One-way repeated-measures ANOVA using SPSS ®

13.4.1. The main analysis ®

13.4.2. Defining contrasts for repeated-measures ®

3 4 3 Post hoc tests and additional options ®

Output fo\ one-way repeated-measures ANOVA

®

13.5.1. Descriptives and other diagnostics

13.5.2. Assessing and correcting for sphericity: Mauchly s test ®

13.5.3. The main ANOVA®

13.5.4. Contrasts ®

3 5 5 Post hoc tests ®

Effect sizes for repeated-measures ANOVA ®

Reporting one-way repeated-measures ANOVA ®

Repeated-measures with several independent variables ®

13.8.1. The main analysis ®

13.8.2. Contrasts

®

13.8.3. Simple effects analysis®

13.8.4. Graphing interactions ®

13.8.5. Other options ®

Output for factorial repeated-measures ANOVA ®

13.9.1. Descriptives and main analysis ®

13.9.2. The effect of drink ®

13.9.3. The effect of imagery

®

13.9.4. The interaction effect drink x imagery) ®

13.9.5. Contrasts for repeated-measures variables ®

Effect sizes for factorial repeated-measures ANOVA ®

Reporting the results from factorial repeated-measures ANOVA

®

What to do when assumptions are violated in repeated-measures ANOVA ®



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Smart Alex s tasks

Further reading

Online tutoriais


14 Mixed design ANOVA GLM 5

OISCOVERING STATISTICS USING SPSS

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14.7.

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What will this chapter tell me? e

Mixed designs

®

What do men and women look for in a partner?

®

Mixed ANOVA on SPSS

®

14.4.1. The main analysis

®

14.4.2. Other options

®

Output for mixed factorial ANOVA: main analysis

14.5.1. The main effect of gender ®

14.5.2. The main effect of looks

®

14.5.3. The main effect of charisma ®

14.5.4. The interaction between gender and looks ®

14.5.5. The interaction between gender and charisma

®

14.5.6. The interaction between attractiveness and charisma ®

14.5.7. The interaction between looks charisma and gender

14.5.8. Conclusions

Calculating effect sizes

Reporting the results of mixed ANOVA ®

What to do when assumptions are violated in mixed ANOVA


®


Smart Alex s tasks

Further reading

Online tutoriais


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15 Non-parametric tests

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e

When to use non-parametric tests

e

Comparing two independent conditions: the Wilcoxon rank-sum test and

Mann-Whitney test

e

15.3.1. Theory

®

15.3.2. Inputting data and provisional analysis

e

15.3.3. Running the analysis e

15.3.4. Output from the Mann-Whitney test

e

15.3.5. Calculating an etfect size ®

15.3.6. Writing the results

e

Comparing two related conditions: the Wilcoxon signed-rank test

e

15.4.1. Theory of the Wilcoxon signed-rank test

®

15.4.2. Running the analysis

e

15.4.3. Output for the ecstasy group

e

15.4.4. Output for the alcohol group

e

15.4.5. Calculating an effect size

®

15.4.6. Writing the results

e

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CONTENTS

5 5

Differences between several independent groups the Kruskal Wallis test <D 559

15.5.1. Theory of the Kruskal Wallis test

®

560


<D

562

15.5.3. Doing the Kruskal Wallis test on SPSS <D 562

15.5.4. Output from the Kruskal Wallis test

<D

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5 5 5 Post hoc

tests for the Kruskal Wallis test

®

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15.5.6. Testing for trends the Jonckheere Terpstra test

®

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®

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15.5.8. Writing and interpreting the results <D 571

5 6 Differences between several related groups: Friedman s ANOVA

<D

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15.6.1. Theory of Friedman s ANOVA

®

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<D

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15.6.3. Doing Friedman s ANOVA on SPSS

<D

575

15.6.4. Output from Friedman s ANOVA <D 576

5 6 5 Post hoc tests for Friedman s ANOVA

®

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®

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15.67. Writing and interpreting the results

<D

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<D


Smart Alex s tasks

Further reading

Online tutorial


16 MuLtivariate anaLysis of variance MANOVA

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®

When to use MANOVA

®

Introduction similarities and differences to ANOVA

®

16.3.1. Words of warning

®

16.3.2. The example for this chapter

®

Theory of MANOVA

®

16.4.1. Introduction to matrices

®

16.4.2. Some important matrices and their functions

®

16.4.3. Calculating MANOVA by hand a worked example

®

16.4.4. Principle of the MANOVAtest statistic

@

Practical issues when conducting MANOVA

®

16.5.1. Assumptions and how to check them

®

16.5.2. Choosing a test statistic

®

16.5.3. Follow up analysis ®

MANOVA on SPSS ®

16.6.1. The main analysis

®

16.6.2. Multiple comparisons in MANOVA

®

16.6.3. Additional options

®

Output from MANOVA

®

16.7.1. Preliminary analysis and testing assumptions

®

16.7.2. MANOVA test statistics

®

16.7.3. Univariate test statistics

®

16.7.4. SSCP Matrices

®

16.7.5. Contrasts

®

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DISCDVERING STATISTICS USING SPSS

16 8

eporting results from MANOVA ®

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ollowing up MANOVA with discriminant analysis ®

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he linai interpretation @

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nivariate ANOVA or discriminant analysis?

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xplor tory f ctor n lysis

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hat will this chapter tell me?

<D

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hen to use factor analysis ®

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actors ®

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raphical representation

1

lactors ®

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athematical representation

1

lactors ®

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actor scores ® 633

iscovering factors ®

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hoosing a method ®

636

ommunality ®

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actor analysis vs. principai component analysis ®

638

heory behind principai component analysis ®

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actor extraction: eigenvalues and the scree plot ®

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mproving interpretation: factor rotation ®

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esearch example ®

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efore you begin ®

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unning the analysis ®

65actor extraction on SPSS® 65otation®

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cores ®

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ptions®

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nterpreting output from SPSS ®

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reliminary analysis ®

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actor extraction ® 66actor rotation ®

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actor scores ®

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ummary®

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ow to report factor analysis

<D

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eliability analysis ®

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easures of reliability®

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nterpreting Cronbach s some cautionary tales ...) ®

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leliability analysis on SPSS®

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nterpreting the output ®

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CONTENTS



Smart Alex s tasks

Further reading

Online tutorial


CategoricaL data

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18 1

18 2

18 3

18 4

18 5

18 6

18 7

18 8

18 9

18 1

18 11

18 12

What will this chapter tell me? eD

Analysing categorical data eD

Theory of analysing categorical data eD

18 3 1 Pearson s chi square test eD

18 3 2 Fisher s exact test eD

18 3 3 The Iikelihood ratio ®

18 3 4

Yates correction

®

Assumptions of the chi square test eD

Doing chi square on SPSS eD

18 5 1 Entering data raw scores eD

18 5 2 Entering data weight cases eD

18 5 3 Running the analysis eD

18 5 4 Output for the chi square test eD

18 5 5 Breaking down a significant chi square test with standardized residuals

®

18 5 5 Calculating an effect size

®

18 5 7 Reporting the results of chi square eD

Several categorical variables: loglinear analysis

®

18 5 1 Chi square as regression @

18 5 2 Loglinear analysis

®

Assumptions in loglinear analysis

®

Loglinear analysis using SPSS ®

18 8 1 Initial considerations ®

18 8 2 The loglinear analysis ®

Output from loglinear analysis ®

Following up loglinear analysis

®

Effect sizes in loglinear analysis

®

Reporting the results of loglinear analysis

®



Smart Alex s tasks

Further reading

Online tutorial


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7 2

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9 MuLtiLeveLLinear modeLs 725

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19 2

19 3

What will this chapter tell me? eD

Hierarchical data

®

19 2 1 The iJ;1traclasscorrelation ®

19 2 2 Benefits of multilevel modeis ®

Theory of multilevellinear modeis

®

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xviii


19.3.1. An example

19.3.2. Fixed and random coefficients

9 4

The multilevel model

19.4.1. Assessing the fit and comparing multilevel modeis

19.4.2. Typesof covariance structures @

9 5

Some practical issues

19.5.1. Assumptions @

19.5.2. Sampie size and power

19.5.3. Centring variables

9 6

Multilevel modelling on SPSS

19.6.1. Entering the data

19.6.2. Ignoring the data structure ANOVA

19.6.3. Ignoring the data structure ANCOVA

19.6.4. Factoring in the data structure: random intercepts @

19.6.5. Factoring in the data structure: random intercepts and slopes

19.6.6. Adding an interaction to the model

9 7 Growth modeis

@

19.7.1. Growth curves polynomials) @

19.7.2. An example: the honeymoon period

19.7.3. Restructuring the data @

19.7.4. Running a growth model on SPSS

19.7.5. Further analysis @

9 8 How to report a multilevel model



Smart Alex s tasks

Further reading

Online tutorial


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Epilogue

Glossary

Appendix

A l

A 2

A 3

A 4

References

Index

Table of the standard normal distribution

Critical values of the t-distribution

Critical values of the F-distribution

Critical values of the chi-square distribution

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8 4

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8 9

8 6

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2009 - Andy FIELD - Discovering Statistics Using SPSS

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