SPSS 16 made simple:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hove
Psychology Press
2009
|
| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 639 S. Ill., graph. Darst. |
| ISBN: | 9781841697291 |
Internformat
MARC
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| 020 | |a 9781841697291 |c pbk. |9 978-1-8416-9729-1 | ||
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| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 100 | 1 | |a Kinnear, Paul R. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a SPSS 16 made simple |c Paul R. Kinnear, Colin D. Gray |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Hove |b Psychology Press |c 2009 | |
| 300 | |a XIV, 639 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 630 | 0 | 4 | |a SPSS (Computer file) |
| 650 | 7 | |a SPSS |2 gtt | |
| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Social sciences |x Statistical methods |x Computer programs | |
| 650 | 0 | 7 | |a SPSS 16.0 für WINDOWS |0 (DE-588)7613615-2 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a SPSS 16.0 für WINDOWS |0 (DE-588)7613615-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Gray, Colin D. |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016678332&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016678332 | ||
Datensatz im Suchindex
| _version_ | 1804137929058025472 |
|---|---|
| adam_text | Contents
Preface
xiii
CHAPTER
1
Introduction
1
1.1
MEASUREMENTS AND DATA
/
1.1.1
Variables: quantitative and qualitative
/
1.1.2
Levels of measurement: scale, ordinal and nominal data
1
1.1.3
A grey area: ratings
2
1.2
EXPERIMENTAL VERSUS CORRELATIONAL RESEARCH
3
1.2.1
True experiments
3
1.2.2
Correlational research
3
1.2.3
Quasi-experiments
4
1.3
SOME STATISTICAL TERMS AND CONCEPTS
4
1.3.1
Samples and populations
4
1.3.2
Parameters and statistics
5
1.3.3
Description or confirmation?
5
1.3.4
Statistical inference
6
1.3.5
Effect size
11
1.4
CHOOSING A STATISTICAL TEST: SOME GUIDELINES
12
1.4.1
Considerations in choosing a statistical test
12
1.4.2
Testing a difference between means for significance
14
1.4.3
The design of the experiment: independent versus related samples
14
1.4.4
Flow chart for selecting a suitable test for differences between means
/5
1.4.5
Measuring strength of association between variables
16
1.4.6
Flow chart for selecting a suitable test for association
16
ХАЛ
Measuring association in nominal data: Contingency tables
17
1.4.8
Multi
-way contingency tables
18
1.4.9
Predicting scores or category membership
18
1.4.10
Flow chart for selecting the appropriate procedure for predicting a score or
category membership
18
1.4.11
Simple regression
19
1.4.12
Multiple regression
19
1.4.13
Predicting category membership: Discriminant analysis and logistic regression
20
1.5
ONE-SAMPLE TESTS
20
1.5.1
Flow chart for selecting the appropriate one-sample test
20
1.5.2
Goodness-of-fit: scale data
21
1.5.3
Goodness-of-fit: nominal data
21
1.5.4
Inferences about the mean of a single population
21
in
iv____________________________________________________Contents
1.5.5 Nominal
data: Testing a coin for fairness
22
1.6
FINDING LATENT VARIABLES: FACTOR ANALYSIS AND CANONICAL
CORRELATION
22
1.6.1
Multivariate statistics
22
1.7
A HNAL
COMMENT
23
Recommended reading
24
CHAPTER
2
Getting started with SPSS
16 25
2.1
OUTLINE OF AN SPSS SESSION
25
2.1.1
Entering the data
25
2.1.2
Selecting the exploratory and statistical procedures
26
2.1.3
Examining the output
26
2.1.4
A simple experiment
26
2.1.5
Preparing data for SPSS
27
2.2
OPENING SPSS
28
2.3
THE SPSS DATA EDITOR
29
2.3.1
Working in Variable View
29
2.3.2
Working in Data View
34
2.3.3
Entering the data
35
2.4
A STATISTICAL ANALYSIS
38
2.4.1
An example: Computing means
38
2.4.2
Keeping more than one application open
42
2.5
CLOSING SPSS
42
2.6
RESUMING WORK ON A SAVED DATA SET
42
Exercise
1
Some simple operations with SPSS
16 43
Exercise
2
Questionnaire data
43
CHAPTER
3
Editing and manipulating files
44
3.1
MORE ABOUT THE SPSS DATA EDITOR
44
3.1.1
Working in Variable View
44
3.1.2
Working in Data View
51
3.2
MORE ON THE SPSS VIEWER
58
3.2.1
Editing the output
59
3.2.2
More advanced editing
60
3.2.3
Tutorials in SPSS
65
3.3
SELECTING FROM AND MANIPULATING DATA FILES
65
3.3.1
Selecting cases
65
3.3.2
Aggregating data
68
3.3.3
Sorting data
71
3.3.4
Merging files
72
3.3.5
Transposing the rows and columns of a data set
77
3.4
IMPORTING AND EXPORTING DATA
79
3.4.1
Importing data from other applications
79
3.4.2
Copying output
81
3.5
PRINTING FROM SPSS
83
3.5.1
Printing output from the Viewer
83
Contents
Exercise
3
Merging files
-
Adding cases
&
variables
90
CHAPTER
4
Exploring your data
91
4.1
INTRODUCTION
91
4.2
SOME USEFUL MENUS
92
4.3
DESCRIBING DATA
93
4.3.1
Describing nominal and ordinal data
94
4.3.2
Describing measurements
101
4.4
MANIPULATION OF THE DATA SET
115
A A.
1
Reducing and transforming data
/75
4.4.2
The COMPUTE procedure
116
4 A3
The
RECODE
and VISUAL BINNING procedures
122
Exercise
4
Correcting and preparing your data
729
Exercise
5
Preparing your data (continued)
729
CHAPTER
5
Graphs and charts
130
5.1
INTRODUCTION
730
5.1.1
Graphs and charts on SPSS
730
5.1.2
Viewing a chart
733
5.1.3
Editing charts and saving templates
733
5.2
BAR CHARTS
734
5.2.1
Simple bar charts
134
5.2.2
Clustered bar charts
737
5.2.3
Panelled bar charts
739
5.2.4 3-D
charts
140
5.2.5
Editing a bar chart
142
5.2.6
Chart templates
144
5.3
ERROR BAR CHARTS
747
5.4
BOXPLOTS
148
5.5
PIE CHARTS
750
5.6
LINE GRAPHS
752
5.7
SCATTERPLOTS AND DOT PLOTS
755
5.8
DUAL Y-AXIS GRAPHS
158
5.9
HISTOGRAMS
760
5.10
RECEIVER-OPERATING-CHARACTERISTIC (ROC) CURVE
162
Exercise
6
Charts and graphs
767
Exercise
7
Recoding data; selecting cases; line graph
767
CHAPTER
6
Comparing averages and frequencies: Two-
sample and one-sample tests
168
6.1
OVERVIEW
168
6.2
THE
Τ
TESTS
/77
6.2.1
One-sample and two-sample tests
7 77
6.2.2
Sampling distributions
777
yj
Contents
6.2.3
The t
distribution,
p-
values, effect size
&
confidence intervals
172
6.2.4
The independent samples
t
test
179
6.2.5
The related-samples
t
test
186
6.3
EFFECT SIZE, POWER AND THE NUMBER OF PARTICIPANTS
191
6.3.1
Problems with significance testing
191
6.3.2
How many participants shall I need in my experiment?
193
6.3.3
Useful software
193
6.4
OTHER TESTS FOR COMPARING AVERAGES
193
6.4.1
Nonparametric tests
194
6A.2 Nonparametric equivalents of the
t
tests
795
6.4.3
Independent samples: Mann-Whitney test
195
6.4.4
Related samples: Wilcoxon, Sign and McNemar tests
198
6.4.5
Other nonparametric alternatives to the paired
t
test
200
6.5
ONE-SAMPLE TESTS
202
6.5.1
Goodness-of-fit: scale or continuous data
202
6.5.2
Goodness-of-fit: nominal data
205
6.5.3
Inferences about the mean of a single population
272
6.5.4
Using a confidence interval to test a hypothesis about the mean of a single
population
274
6.5.5
Using a one-sample
t
test to test a hypothesis about the mean of a single
population
214
Recommended reading
276
Exercise
8
Comparing the averages of two independent samples of data
276
Exercise
9
Comparing the averages of two related samples of data
276
Exercise
10
One-sample tests
276
CHAPTER
7
The one-way ANOVA
27 7
7.1
INTRODUCTION
277
7.1.1
An experiment with five treatment conditions
277
7.1.2
Some basic terms in ANOVA
218
7.2
HOW THE ONE-WAY ANOVA WORKS
279
7.2.1
The between and within groups mean squares
222
7.2.2
Testing
F
for significance
224
7.2.3
The special case of two groups: equivalence of
F
and
1
227
7.2.4
The fixed effects model for the one-way ANOVA
228
7.3
THE ONE-WAY ANOVA IN THE COMPARE MEANS MENU
228
7.3.1
Entering the data
229
7.3.2
Running the one-way ANOVA in Compare Means
231
7.4
MEASURES OF EFFECT SIZE IN ONE-WAY ANOVA
233
7.5
THE ONE-WAY ANOVA IN THE GLM MENU
236
7.5.1
Some key terms
236
7.5.2
Using the GLM menu for one-way ANOVA
237
7.5.3
Additional items with GLM Univariate
240
7.6
MAKING COMPARISONS AMONG THE TREATMENT MEANS
245
7.6.1
Unplanned or post hoc multiple comparisons with SPSS
247
7.6.2
Linear contrasts
249
7.7
TREND ANALYSIS
257
Contents
vii
7.7.1 Trend
analysis with SPSS
261
7.8
POWER AND EFFECT SIZE IN THE ONE-WAY ANOVA
263
7.9
ALTERNATIVES TO THE ONE-WAY ANOVA
266
7.9.1
The Kraskal-Wallis k-sample test
267
7.9.2
Dichotomous nominal data: the chi-square test
269
7.10
A FINAL WORD
269
Recommended reading
270
Exercise
11
One-factor between subjects ANOVA
270
CHAPTER
8
Between subjects factorial experiments
271
8.1
INTRODUCTION
271
8.1.1
An experiment with two treatment factors
271
8.1.2
Main effects and interactions
273
8.1.3
Profile plots
273
8.2
HOW THE TWO-WAY ANOVA WORKS
275
8.2.1
Reporting the results of the two-way ANOVA
278
8.2.2
The fixed effects model for the two-way ANOVA
279
8.3
FURTHER ANALYSIS
280
8.3.1
Measuring effect size in the two-way ANOVA
280
8.3.2
How many participants shall I need for my two-factor experiment?
282
8.3.3
Making multiple comparisons among the treatment means
283
8.3.4
The analysis of interactions
283
8.4
THE TWO-WAY ANOVA WITH SPSS
285
8.4.1
Preparing the data for the factorial ANOVA
285
8.4.2
Exploring the data: boxplots
286
8.4.3
Choosing a factorial ANOVA
287
8.4.4
Output for a factorial ANOVA
288
8.5
TESTING FOR SIMPLE MAIN EFFECTS WITH SYNTAX
292
8.5.1
Using the
MÁNOVA
command to ran the univariate ANOVA
293
8.6
MORE COMPLEX EXPERIMENTS
300
8.6.1
Three-way interactions
301
8.6.2
The three-way ANOVA
302
8.6.3
How the three-way ANOVA works
303
8.6.4
Measures of effect size in the three-way ANOVA
305
8.6.5
How many participants shall I need?
305
8.6.6
The three-way ANOVA with SPSS
305
8.6.7
Follow-up analysis following a significant three-way interaction
308
8.6.8
Using SPSS syntax to test for simple interactions and simple, simple main
effects
309
8.6.9
Unplanned multiple comparisons following a significant three-way interaction
312
8.7
A FINAL WORD
315
Recommended reading
315
Exercise
12
Between subjects factorial ANOVA (two-way ANOVA)
315
уііі
_______________________________Contents
CHAPTER
9
Within subjects experiments
316
9.1
INTRODUCTION
316
9.1.1
Rationale of a within subjects experiment
316
9.1.2
How the within subjects
ANO VA
works
317
9.1.3
A within subjects experiment on the effect of target shape on shooting
accuracy
321
9.1.4
Order effects: counterbalancing
322
9.1.5
Assumptions underlying the within subjects
ANO VA:
homogeneity of
covariance
322
9.1.6
Effect size in within subjects
ANO VA
325
9.1.7
Power and effect size: how many participants shall I need?
326
9.2
A ONE-FACTOR WITHIN SUBJECTS ANOVA WITH SPSS
327
9.2.1
Entering the data
327
9.2.2
Exploring the data: Boxplots for within subjects factors
327
9.2.3
Running the within subjects ANOVA
329
9.2.4
Output for a one-factor within subjects ANOVA
332
9.2.5
Unplanned multiple comparisons
337
9.3
NONPARAMETRIC EQUIVALENTS OF THE WITHIN SUBJECTS ANOVA
337
9.3.1
The Friedman test for ordinal data
337
9.3.2
Cochran s
Q
test for nominal data
339
9.4
THE TWO-FACTOR WITHIN SUBJECTS ANOVA
340
9.4.1
Preparing the data set
342
9
A.I Running the two-factor within subjects ANOVA
342
9 .4.3
Output for a two-factor within subjects ANOVA
345
9.4.4
Unpacking a significant interaction with multiple comparisons
349
9.5
A FINAL WORD
352
Recommended reading
352
Exercise
13
One-factor within subjects (repeated measures) ANOVA
353
Exercise
14
Two-factor within subjects ANOVA
353
CHAPTER
10
Mixed factorial experiments
354
10.1
INTRODUCTION
354
10.1.1
Mixed factorial or split-plot designs
354
10.1.2
Rationale of the mixed ANOVA
356
10.2
THE TWO-FACTOR MIXED FACTORIAL ANOVA WITH SPSS
358
1
0.2
Л
Preparing the SPSS data set
358
10.2.2
Exploring the results: Boxplots
359
10.2.3
Running the ANOVA
360
10.2.4
Output for the two-factor mixed ANOVA
362
10.2.5
Simple effects analysis with syntax
367
10.3
THE THREE-FACTOR MIXED ANOVA
371
10.3.1
Two within subjects factors and one between subjects factor: the AxCBxC)
mixed factorial design
372
10.3.2
Using syntax to test for simple effects
375
10.3.3
One within subjects factor and two between subjects factors: the
mixed factorial design
378
Contents ix
10.4 THE MULTIV
ARIATE
ANALYSIS OF VARIANCE
(MÁNOVA)
381
10.4.1
What the
MÁNOVA
does
382
10.4.2
How the
MÁNOVA
works
383
10.4.3
Assumptions of
MÁNOVA
386
10.4.4
Relation of
MÁNOVA
to within subjects
ANO VA
386
10.4.5
Application of
MÁNOVA
to the shape recognition example
387
10.4.6
The
MÁNOVA
output
390
10.5
A FINAL WORD
392
Recommended reading
393
Exercise
15
Mixed
ANO VA:
two-factor experiment
393
Exercise
16
Mixed ANOVA: three-factor experiment
393
CHAPTER
11
Measuring statistical association
394
11.1
INTRODUCTION
394
11.1.1
A correlational study
394
11.1.2
Linear relationships
396
11.2
THE PEARSON CORRELATION
397
11.2.1
Effect size
399
11.3
CORRELATION WITH SPSS
400
11.3.1
Obtaining a scatterplot
401
11.3.2
Obtaining the Pearson correlation
402
11.3.3
Output for the Pearson correlation
403
11.4
OTHER MEASURES OF ASSOCIATION
404
Π
.4.1
Spearman s rank correlation
404
11.4.2
Kendall s
tau
statistics
405
11.4.3
Rank correlations with SPSS
405
11.5
TESTING FOR ASSOCIATION IN NOMINAL DATA
407
11.5.1
The chi-square test for association
407
11.5.2
Measures of strength of association for nominal data
410
11.5.3
Analysis of contingency tables with SPSS
412
11.5.4
Getting help with the output
418
11.5.5
Some cautions and caveats
419
11.6
DO DOCTORS AGREE? COHEN S KAPPA
423
11.7
PARTIAL CORRELATION
425
11.8
CORRELATION IN MENTAL TESTING: RELIABILITY
428
11.9
A FINAL WORD
434
Recommended reading
434
Exercise
17
The Pearson correlation
435
Exercise
18
Other measures of association
435
Exercise
19
The analysis of nominal data
435
CHAPTER
12
Regression
436
12.1
INTRODUCTION
436
12.1.1
Simple, two-variable regression
436
12.1.2
Residuals
438
12.1.3
The least squares criterion
439
x
Contents
12.1.4
Partition
of the sum of squares in regression
439
12.1.5
Effect size in regression
441
12.1.6
Shrinkage
442
12.1.7
Regression models
442
12.1.8
Beta-weights
443
12.1.9
Significance testing in simple regression
444
12.2
SIMPLE REGRESSION WITH SPSS
445
12.2.1
Drawing scatterplots with regression lines
445
12.2.2
A problem in simple regression
447
12.2.3
Procedure for simple regression
448
12.2.4
Output for simple regression
451
12.3
MULTIPLE REGRESSION
456
12.3.1
The multiple correlation coefficient
R
457
12.3.2
Significance testing in multiple regression
458
12.3.3
Partial and semipartial correlation
459
ПА
MULTIPLE REGRESSION WITH SPSS
464
12.4.1
Simultaneous multiple regression
466
12.4.2
Stepwise multiple regression
469
12.5
REGRESSION AND ANALYSIS OF VARIANCE
473
12.5.1
The point-biserial correlation
473
12.5.2
Regression and the one-way
ANO VA
for two groups
474
12.5.3
Regression and dummy coding: the two-group case
476
12.5.4
Regression and the one-way
ANO VA
477
12.6
MULTILEVEL REGRESSION MODELS
482
12.7
A FINAL WORD
482
Recommended reading
482
Exercise
20
Simple, two-variable regression
483
Exercise
21
Multiple regression
483
CHAPTER
13
Analyses of multiway frequency tables
&
multiple
response sets
484
13.1
INTRODUCTION
484
13.2
SOME BASICS OF
LOGLINEAR
MODELLING
485
13.2.1 Loglinear
models and
ANO VA
models
486
13.2.2
Model-building and the hierarchical principle
487
13.2.3
The main-effects-only
loglinear
model and the traditional chi-square test for
association
490
13.2.4
Analysis of the residuals
490
13.3
MODELLING A TWO-WAY CONTINGENCY TABLE
491
13.3.1
SPSS procedures for
loglinear
analysis
492
13.3.2
Fitting an unsaturated model
497
13.3.3
Summary
501
13.4
MODELLING A THREE-WAY FREQUENCY TABLE
507
13.4.1
Exploring the data
502
13.4.2 Loglinear
analysis of the data on gender and helpfulness
503
13.4.3
The main-effects-only model and the traditional chi-square test
507
13
A A Collapsing a multi-way table: the requirement of conditional independence
509
Contents xi
13.4.5 An
alternative data set for the gender and helpfulness experiment
511
13.4.6
Reporting the results of
a
loglinear
analysis
514
13.5
MULTIPLE RESPONSE SETS
514
13.5.1
Multiple response set analysis with SPSS
516
13.6
A FINAL WORD
523
Recommended reading
523
Exercise
22 Loglinear
analysis
524
CHAPTER
14
Discriminant analysis and logistic regression
525
14.1
INTRODUCTION
525
14.1.1
Discriminant analysis
526
14.1.2
Types of discriminant analysis
527
14.1.3
Stepwise discriminant analysis
527
14.1.4
Restrictive assumptions of discriminant analysis
528
14.2
DISCRIMINANT ANALYSIS WITH SPSS
528
14.2.1
Preparing the data set
529
14.2.2
Exploring the data
529
14.2.3
Running discriminant analysis
530
14.2.4
Output for discriminant analysis
532
14.2.5
Predicting group membership
540
14.3
BINARY LOGISTIC REGRESSION
542
14.3.1
Logistic regression
542
14.3.2
How logistic regression works
544
14.3.3
An example of a binary logistic regression with quantitative independent
variables
546
14.3.4
Binary logistic regression with categorical independent variables
555
14.4
MULTINOMIAL LOGISTIC REGRESSION
558
14.4.1
Running multinomial logistic regression
559
14.5
A FINAL WORD
562
Recommended reading
563
Exercise
23
Predicting category membership: Discriminant analysis and binary logistic
regression
563
CHAPTER
15
Latent variables: exploratory factor analysis
&
canonical correlation
564
15.1
INTRODUCTION
564
15.1.1
Stages in an exploratory factor analysis
566
15.1.2
The extraction of factors
567
15.1.3
The rationale of rotation
567
15.1.4
Some issues in factor analysis
567
15.1.5
Some key technical terms
568
15.2
A FACTOR ANALYSIS OF DATA ON SIX VARIABLES
569
15.2.1
Entering the data for a factor analysis
569
15.2.2
Running a factor analysis on SPSS
569
15.2.3
Output for factor analysis
572
15.3
USING SPSS SYNTAX TO RUN A FACTOR ANALYSIS
583
xii___________________________________________________________________Contents
15.3.1
Running a factor analysis with SPSS syntax
583
15.3.2
Using a correlation matrix as input for factor analysis
584
15.3.3
Progressing with SPSS syntax
586
15.4
CANONICAL CORRELATION
587
15.4.1
Running canonical correlation on SPSS
588
15.4.2
Output for canonical correlation
589
15.5
A FINAL WORD
593
Recommended reading
594
Exercise
24
Factor analysis
595
Appendix
596
Glossary
600
References
619
Index
621
|
| adam_txt |
Contents
Preface
xiii
CHAPTER
1
Introduction
1
1.1
MEASUREMENTS AND DATA
/
1.1.1
Variables: quantitative and qualitative
/
1.1.2
Levels of measurement: scale, ordinal and nominal data
1
1.1.3
A grey area: ratings
2
1.2
EXPERIMENTAL VERSUS CORRELATIONAL RESEARCH
3
1.2.1
True experiments
3
1.2.2
Correlational research
3
1.2.3
Quasi-experiments
4
1.3
SOME STATISTICAL TERMS AND CONCEPTS
4
1.3.1
Samples and populations
4
1.3.2
Parameters and statistics
5
1.3.3
Description or confirmation?
5
1.3.4
Statistical inference
6
1.3.5
Effect size
11
1.4
CHOOSING A STATISTICAL TEST: SOME GUIDELINES
12
1.4.1
Considerations in choosing a statistical test
12
1.4.2
Testing a difference between means for significance
14
1.4.3
The design of the experiment: independent versus related samples
14
1.4.4
Flow chart for selecting a suitable test for differences between means
/5
1.4.5
Measuring strength of association between variables
16
1.4.6
Flow chart for selecting a suitable test for association
16
ХАЛ
Measuring association in nominal data: Contingency tables
17
1.4.8
Multi
-way contingency tables
18
1.4.9
Predicting scores or category membership
18
1.4.10
Flow chart for selecting the appropriate procedure for predicting a score or
category membership
18
1.4.11
Simple regression
19
1.4.12
Multiple regression
19
1.4.13
Predicting category membership: Discriminant analysis and logistic regression
20
1.5
ONE-SAMPLE TESTS
20
1.5.1
Flow chart for selecting the appropriate one-sample test
20
1.5.2
Goodness-of-fit: scale data
21
1.5.3
Goodness-of-fit: nominal data
21
1.5.4
Inferences about the mean of a single population
21
in
iv_Contents
1.5.5 Nominal
data: Testing a coin for fairness
22
1.6
FINDING LATENT VARIABLES: FACTOR ANALYSIS AND CANONICAL
CORRELATION
22
1.6.1
Multivariate statistics
22
1.7
A HNAL
COMMENT
23
Recommended reading
24
CHAPTER
2
Getting started with SPSS
16 25
2.1
OUTLINE OF AN SPSS SESSION
25
2.1.1
Entering the data
25
2.1.2
Selecting the exploratory and statistical procedures
26
2.1.3
Examining the output
26
2.1.4
A simple experiment
26
2.1.5
Preparing data for SPSS
27
2.2
OPENING SPSS
28
2.3
THE SPSS DATA EDITOR
29
2.3.1
Working in Variable View
29
2.3.2
Working in Data View
34
2.3.3
Entering the data
35
2.4
A STATISTICAL ANALYSIS
38
2.4.1
An example: Computing means
38
2.4.2
Keeping more than one application open
42
2.5
CLOSING SPSS
42
2.6
RESUMING WORK ON A SAVED DATA SET
42
Exercise
1
Some simple operations with SPSS
16 43
Exercise
2
Questionnaire data
43
CHAPTER
3
Editing and manipulating files
44
3.1
MORE ABOUT THE SPSS DATA EDITOR
44
3.1.1
Working in Variable View
44
3.1.2
Working in Data View
51
3.2
MORE ON THE SPSS VIEWER
58
3.2.1
Editing the output
59
3.2.2
More advanced editing
60
3.2.3
Tutorials in SPSS
65
3.3
SELECTING FROM AND MANIPULATING DATA FILES
65
3.3.1
Selecting cases
65
3.3.2
Aggregating data
68
3.3.3
Sorting data
71
3.3.4
Merging files
72
3.3.5
Transposing the rows and columns of a data set
77
3.4
IMPORTING AND EXPORTING DATA
79
3.4.1
Importing data from other applications
79
3.4.2
Copying output
81
3.5
PRINTING FROM SPSS
83
3.5.1
Printing output from the Viewer
83
Contents
Exercise
3
Merging files
-
Adding cases
&
variables
90
CHAPTER
4
Exploring your data
91
4.1
INTRODUCTION
91
4.2
SOME USEFUL MENUS
92
4.3
DESCRIBING DATA
93
4.3.1
Describing nominal and ordinal data
94
4.3.2
Describing measurements
101
4.4
MANIPULATION OF THE DATA SET
115
A A.
1
Reducing and transforming data
/75
4.4.2
The COMPUTE procedure
116
4 A3
The
RECODE
and VISUAL BINNING procedures
122
Exercise
4
Correcting and preparing your data
729
Exercise
5
Preparing your data (continued)
729
CHAPTER
5
Graphs and charts
130
5.1
INTRODUCTION
730
5.1.1
Graphs and charts on SPSS
730
5.1.2
Viewing a chart
733
5.1.3
Editing charts and saving templates
733
5.2
BAR CHARTS
734
5.2.1
Simple bar charts
134
5.2.2
Clustered bar charts
737
5.2.3
Panelled bar charts
739
5.2.4 3-D
charts
140
5.2.5
Editing a bar chart
142
5.2.6
Chart templates
144
5.3
ERROR BAR CHARTS
747
5.4
BOXPLOTS
148
5.5
PIE CHARTS
750
5.6
LINE GRAPHS
752
5.7
SCATTERPLOTS AND DOT PLOTS
755
5.8
DUAL Y-AXIS GRAPHS
158
5.9
HISTOGRAMS
760
5.10
RECEIVER-OPERATING-CHARACTERISTIC (ROC) CURVE
162
Exercise
6
Charts and graphs
767
Exercise
7
Recoding data; selecting cases; line graph
767
CHAPTER
6
Comparing averages and frequencies: Two-
sample and one-sample tests
168
6.1
OVERVIEW
168
6.2
THE
Τ
TESTS
/77
6.2.1
One-sample and two-sample tests
7 77
6.2.2
Sampling distributions
777
yj
Contents
6.2.3
The t
distribution,
p-
values, effect size
&
confidence intervals
172
6.2.4
The independent samples
t
test
179
6.2.5
The related-samples
t
test
186
6.3
EFFECT SIZE, POWER AND THE NUMBER OF PARTICIPANTS
191
6.3.1
Problems with significance testing
191
6.3.2
How many participants shall I need in my experiment?
193
6.3.3
Useful software
193
6.4
OTHER TESTS FOR COMPARING AVERAGES
193
6.4.1
Nonparametric tests
194
6A.2 Nonparametric equivalents of the
t
tests
795
6.4.3
Independent samples: Mann-Whitney test
195
6.4.4
Related samples: Wilcoxon, Sign and McNemar tests
198
6.4.5
Other nonparametric alternatives to the paired
t
test
200
6.5
ONE-SAMPLE TESTS
202
6.5.1
Goodness-of-fit: scale or continuous data
202
6.5.2
Goodness-of-fit: nominal data
205
6.5.3
Inferences about the mean of a single population
272
6.5.4
Using a confidence interval to test a hypothesis about the mean of a single
population
274
6.5.5
Using a one-sample
t
test to test a hypothesis about the mean of a single
population
214
Recommended reading
276
Exercise
8
Comparing the averages of two independent samples of data
276
Exercise
9
Comparing the averages of two related samples of data
276
Exercise
10
One-sample tests
276
CHAPTER
7
The one-way ANOVA
27 7
7.1
INTRODUCTION
277
7.1.1
An experiment with five treatment conditions
277
7.1.2
Some basic terms in ANOVA
218
7.2
HOW THE ONE-WAY ANOVA WORKS
279
7.2.1
The between and within groups mean squares
222
7.2.2
Testing
F
for significance
224
7.2.3
The special case of two groups: equivalence of
F
and
1
227
7.2.4
The fixed effects model for the one-way ANOVA
228
7.3
THE ONE-WAY ANOVA IN THE COMPARE MEANS MENU
228
7.3.1
Entering the data
229
7.3.2
Running the one-way ANOVA in Compare Means
231
7.4
MEASURES OF EFFECT SIZE IN ONE-WAY ANOVA
233
7.5
THE ONE-WAY ANOVA IN THE GLM MENU
236
7.5.1
Some key terms
236
7.5.2
Using the GLM menu for one-way ANOVA
237
7.5.3
Additional items with GLM Univariate
240
7.6
MAKING COMPARISONS AMONG THE TREATMENT MEANS
245
7.6.1
Unplanned or post hoc multiple comparisons with SPSS
247
7.6.2
Linear contrasts
249
7.7
TREND ANALYSIS
257
Contents
vii
7.7.1 Trend
analysis with SPSS
261
7.8
POWER AND EFFECT SIZE IN THE ONE-WAY ANOVA
263
7.9
ALTERNATIVES TO THE ONE-WAY ANOVA
266
7.9.1
The Kraskal-Wallis k-sample test
267
7.9.2
Dichotomous nominal data: the chi-square test
269
7.10
A FINAL WORD
269
Recommended reading
270
Exercise
11
One-factor between subjects ANOVA
270
CHAPTER
8
Between subjects factorial experiments
271
8.1
INTRODUCTION
271
8.1.1
An experiment with two treatment factors
271
8.1.2
Main effects and interactions
273
8.1.3
Profile plots
273
8.2
HOW THE TWO-WAY ANOVA WORKS
275
8.2.1
Reporting the results of the two-way ANOVA
278
8.2.2
The fixed effects model for the two-way ANOVA
279
8.3
FURTHER ANALYSIS
280
8.3.1
Measuring effect size in the two-way ANOVA
280
8.3.2
How many participants shall I need for my two-factor experiment?
282
8.3.3
Making multiple comparisons among the treatment means
283
8.3.4
The analysis of interactions
283
8.4
THE TWO-WAY ANOVA WITH SPSS
285
8.4.1
Preparing the data for the factorial ANOVA
285
8.4.2
Exploring the data: boxplots
286
8.4.3
Choosing a factorial ANOVA
287
8.4.4
Output for a factorial ANOVA
288
8.5
TESTING FOR SIMPLE MAIN EFFECTS WITH SYNTAX
292
8.5.1
Using the
MÁNOVA
command to ran the univariate ANOVA
293
8.6
MORE COMPLEX EXPERIMENTS
300
8.6.1
Three-way interactions
301
8.6.2
The three-way ANOVA
302
8.6.3
How the three-way ANOVA works
303
8.6.4
Measures of effect size in the three-way ANOVA
305
8.6.5
How many participants shall I need?
305
8.6.6
The three-way ANOVA with SPSS
305
8.6.7
Follow-up analysis following a significant three-way interaction
308
8.6.8
Using SPSS syntax to test for simple interactions and simple, simple main
effects
309
8.6.9
Unplanned multiple comparisons following a significant three-way interaction
312
8.7
A FINAL WORD
315
Recommended reading
315
Exercise
12
Between subjects factorial ANOVA (two-way ANOVA)
315
уііі
_Contents
CHAPTER
9
Within subjects experiments
316
9.1
INTRODUCTION
316
9.1.1
Rationale of a within subjects experiment
316
9.1.2
How the within subjects
ANO VA
works
317
9.1.3
A within subjects experiment on the effect of target shape on shooting
accuracy
321
9.1.4
Order effects: counterbalancing
322
9.1.5
Assumptions underlying the within subjects
ANO VA:
homogeneity of
covariance
322
9.1.6
Effect size in within subjects
ANO VA
325
9.1.7
Power and effect size: how many participants shall I need?
326
9.2
A ONE-FACTOR WITHIN SUBJECTS ANOVA WITH SPSS
327
9.2.1
Entering the data
327
9.2.2
Exploring the data: Boxplots for within subjects factors
327
9.2.3
Running the within subjects ANOVA
329
9.2.4
Output for a one-factor within subjects ANOVA
332
9.2.5
Unplanned multiple comparisons
337
9.3
NONPARAMETRIC EQUIVALENTS OF THE WITHIN SUBJECTS ANOVA
337
9.3.1
The Friedman test for ordinal data
337
9.3.2
Cochran's
Q
test for nominal data
339
9.4
THE TWO-FACTOR WITHIN SUBJECTS ANOVA
340
9.4.1
Preparing the data set
342
9
A.I Running the two-factor within subjects ANOVA
342
9'.4.3
Output for a two-factor within subjects ANOVA
345
9.4.4
Unpacking a significant interaction with multiple comparisons
349
9.5
A FINAL WORD
352
Recommended reading
352
Exercise
13
One-factor within subjects (repeated measures) ANOVA
353
Exercise
14
Two-factor within subjects ANOVA
353
CHAPTER
10
Mixed factorial experiments
354
10.1
INTRODUCTION
354
10.1.1
Mixed factorial or split-plot designs
354
10.1.2
Rationale of the mixed ANOVA
356
10.2
THE TWO-FACTOR MIXED FACTORIAL ANOVA WITH SPSS
358
1
0.2
Л
Preparing the SPSS data set
358
10.2.2
Exploring the results: Boxplots
359
10.2.3
Running the ANOVA
360
10.2.4
Output for the two-factor mixed ANOVA
362
10.2.5
Simple effects analysis with syntax
367
10.3
THE THREE-FACTOR MIXED ANOVA
371
10.3.1
Two within subjects factors and one between subjects factor: the AxCBxC)
mixed factorial design
372
10.3.2
Using syntax to test for simple effects
375
10.3.3
One within subjects factor and two between subjects factors: the
mixed factorial design
378
Contents ix
10.4 THE MULTIV
ARIATE
ANALYSIS OF VARIANCE
(MÁNOVA)
381
10.4.1
What the
MÁNOVA
does
382
10.4.2
How the
MÁNOVA
works
383
10.4.3
Assumptions of
MÁNOVA
386
10.4.4
Relation of
MÁNOVA
to within subjects
ANO VA
386
10.4.5
Application of
MÁNOVA
to the shape recognition example
387
10.4.6
The
MÁNOVA
output
390
10.5
A FINAL WORD
392
Recommended reading
393
Exercise
15
Mixed
ANO VA:
two-factor experiment
393
Exercise
16
Mixed ANOVA: three-factor experiment
393
CHAPTER
11
Measuring statistical association
394
11.1
INTRODUCTION
394
11.1.1
A correlational study
394
11.1.2
Linear relationships
396
11.2
THE PEARSON CORRELATION
397
11.2.1
Effect size
399
11.3
CORRELATION WITH SPSS
400
11.3.1
Obtaining a scatterplot
401
11.3.2
Obtaining the Pearson correlation
402
11.3.3
Output for the Pearson correlation
403
11.4
OTHER MEASURES OF ASSOCIATION
404
Π
.4.1
Spearman's rank correlation
404
11.4.2
Kendall's
tau
statistics
405
11.4.3
Rank correlations with SPSS
405
11.5
TESTING FOR ASSOCIATION IN NOMINAL DATA
407
11.5.1
The chi-square test for association
407
11.5.2
Measures of strength of association for nominal data
410
11.5.3
Analysis of contingency tables with SPSS
412
11.5.4
Getting help with the output
418
11.5.5
Some cautions and caveats
419
11.6
DO DOCTORS AGREE? COHEN'S KAPPA
423
11.7
PARTIAL CORRELATION
425
11.8
CORRELATION IN MENTAL TESTING: RELIABILITY
428
11.9
A FINAL WORD
434
Recommended reading
434
Exercise
17
The Pearson correlation
435
Exercise
18
Other measures of association
435
Exercise
19
The analysis of nominal data
435
CHAPTER
12
Regression
436
12.1
INTRODUCTION
436
12.1.1
Simple, two-variable regression
436
12.1.2
Residuals
438
12.1.3
The least squares criterion
439
x
Contents
12.1.4
Partition
of the sum of squares in regression
439
12.1.5
Effect size in regression
441
12.1.6
Shrinkage
442
12.1.7
Regression models
442
12.1.8
Beta-weights
443
12.1.9
Significance testing in simple regression
444
12.2
SIMPLE REGRESSION WITH SPSS
445
12.2.1
Drawing scatterplots with regression lines
445
12.2.2
A problem in simple regression
447
12.2.3
Procedure for simple regression
448
12.2.4
Output for simple regression
451
12.3
MULTIPLE REGRESSION
456
12.3.1
The multiple correlation coefficient
R
457
12.3.2
Significance testing in multiple regression
458
12.3.3
Partial and semipartial correlation
459
ПА
MULTIPLE REGRESSION WITH SPSS
464
12.4.1
Simultaneous multiple regression
466
12.4.2
Stepwise multiple regression
469
12.5
REGRESSION AND ANALYSIS OF VARIANCE
473
12.5.1
The point-biserial correlation
473
12.5.2
Regression and the one-way
ANO VA
for two groups
474
12.5.3
Regression and dummy coding: the two-group case
476
12.5.4
Regression and the one-way
ANO VA
477
12.6
MULTILEVEL REGRESSION MODELS
482
12.7
A FINAL WORD
482
Recommended reading
482
Exercise
20
Simple, two-variable regression
483
Exercise
21
Multiple regression
483
CHAPTER
13
Analyses of multiway frequency tables
&
multiple
response sets
484
13.1
INTRODUCTION
484
13.2
SOME BASICS OF
LOGLINEAR
MODELLING
485
13.2.1 Loglinear
models and
ANO VA
models
486
13.2.2
Model-building and the hierarchical principle
487
13.2.3
The main-effects-only
loglinear
model and the traditional chi-square test for
association
490
13.2.4
Analysis of the residuals
490
13.3
MODELLING A TWO-WAY CONTINGENCY TABLE
491
13.3.1
SPSS procedures for
loglinear
analysis
492
13.3.2
Fitting an unsaturated model
497
13.3.3
Summary
501
13.4
MODELLING A THREE-WAY FREQUENCY TABLE
507
13.4.1
Exploring the data
502
13.4.2 Loglinear
analysis of the data on gender and helpfulness
503
13.4.3
The main-effects-only model and the traditional chi-square test
507
13
A A Collapsing a multi-way table: the requirement of conditional independence
509
Contents xi
13.4.5 An
alternative data set for the gender and helpfulness experiment
511
13.4.6
Reporting the results of
a
loglinear
analysis
514
13.5
MULTIPLE RESPONSE SETS
514
13.5.1
Multiple response set analysis with SPSS
516
13.6
A FINAL WORD
523
Recommended reading
523
Exercise
22 Loglinear
analysis
524
CHAPTER
14
Discriminant analysis and logistic regression
525
14.1
INTRODUCTION
525
14.1.1
Discriminant analysis
526
14.1.2
Types of discriminant analysis
527
14.1.3
Stepwise discriminant analysis
527
14.1.4
Restrictive assumptions of discriminant analysis
528
14.2
DISCRIMINANT ANALYSIS WITH SPSS
528
14.2.1
Preparing the data set
529
14.2.2
Exploring the data
529
14.2.3
Running discriminant analysis
530
14.2.4
Output for discriminant analysis
532
14.2.5
Predicting group membership
540
14.3
BINARY LOGISTIC REGRESSION
542
14.3.1
Logistic regression
542
14.3.2
How logistic regression works
544
14.3.3
An example of a binary logistic regression with quantitative independent
variables
546
14.3.4
Binary logistic regression with categorical independent variables
555
14.4
MULTINOMIAL LOGISTIC REGRESSION
558
14.4.1
Running multinomial logistic regression
559
14.5
A FINAL WORD
562
Recommended reading
563
Exercise
23
Predicting category membership: Discriminant analysis and binary logistic
regression
563
CHAPTER
15
Latent variables: exploratory factor analysis
&
canonical correlation
564
15.1
INTRODUCTION
564
15.1.1
Stages in an exploratory factor analysis
566
15.1.2
The extraction of factors
567
15.1.3
The rationale of rotation
567
15.1.4
Some issues in factor analysis
567
15.1.5
Some key technical terms
568
15.2
A FACTOR ANALYSIS OF DATA ON SIX VARIABLES
569
15.2.1
Entering the data for a factor analysis
569
15.2.2
Running a factor analysis on SPSS
569
15.2.3
Output for factor analysis
572
15.3
USING SPSS SYNTAX TO RUN A FACTOR ANALYSIS
583
xii_Contents
15.3.1
Running a factor analysis with SPSS syntax
583
15.3.2
Using a correlation matrix as input for factor analysis
584
15.3.3
Progressing with SPSS syntax
586
15.4
CANONICAL CORRELATION
587
15.4.1
Running canonical correlation on SPSS
588
15.4.2
Output for canonical correlation
589
15.5
A FINAL WORD
593
Recommended reading
594
Exercise
24
Factor analysis
595
Appendix
596
Glossary
600
References
619
Index
621 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Kinnear, Paul R. Gray, Colin D. |
| author_facet | Kinnear, Paul R. Gray, Colin D. |
| author_role | aut aut |
| author_sort | Kinnear, Paul R. |
| author_variant | p r k pr prk c d g cd cdg |
| building | Verbundindex |
| bvnumber | BV035009056 |
| callnumber-first | H - Social Science |
| callnumber-label | HA32 |
| callnumber-raw | HA32 |
| callnumber-search | HA32 |
| callnumber-sort | HA 232 |
| callnumber-subject | HA - Statistics |
| classification_rvk | ST 601 |
| ctrlnum | (OCoLC)605360266 (DE-599)BVBBV035009056 |
| dewey-full | 300.285555 005.5/5 |
| dewey-hundreds | 300 - Social sciences 000 - Computer science, information, general works |
| dewey-ones | 300 - Social sciences 005 - Computer programming, programs, data, security |
| dewey-raw | 300.285555 005.5/5 |
| dewey-search | 300.285555 005.5/5 |
| dewey-sort | 3300.285555 |
| dewey-tens | 300 - Social sciences 000 - Computer science, information, general works |
| discipline | Informatik Soziologie |
| discipline_str_mv | Informatik Soziologie |
| edition | 1. ed. |
| format | Book |
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| id | DE-604.BV035009056 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:43:12Z |
| indexdate | 2024-07-09T21:20:06Z |
| institution | BVB |
| isbn | 9781841697291 |
| language | English |
| lccn | 2008025409 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016678332 |
| oclc_num | 605360266 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | XIV, 639 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Psychology Press |
| record_format | marc |
| spelling | Kinnear, Paul R. Verfasser aut SPSS 16 made simple Paul R. Kinnear, Colin D. Gray 1. ed. Hove Psychology Press 2009 XIV, 639 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier SPSS (Computer file) SPSS gtt Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS (DE-588)7613615-2 gnd rswk-swf SPSS 16.0 für WINDOWS (DE-588)7613615-2 s DE-604 Gray, Colin D. Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016678332&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kinnear, Paul R. Gray, Colin D. SPSS 16 made simple SPSS (Computer file) SPSS gtt Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS (DE-588)7613615-2 gnd |
| subject_GND | (DE-588)7613615-2 |
| title | SPSS 16 made simple |
| title_auth | SPSS 16 made simple |
| title_exact_search | SPSS 16 made simple |
| title_exact_search_txtP | SPSS 16 made simple |
| title_full | SPSS 16 made simple Paul R. Kinnear, Colin D. Gray |
| title_fullStr | SPSS 16 made simple Paul R. Kinnear, Colin D. Gray |
| title_full_unstemmed | SPSS 16 made simple Paul R. Kinnear, Colin D. Gray |
| title_short | SPSS 16 made simple |
| title_sort | spss 16 made simple |
| topic | SPSS (Computer file) SPSS gtt Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS (DE-588)7613615-2 gnd |
| topic_facet | SPSS (Computer file) SPSS Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016678332&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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