PASW statistics 17 made simple:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hove [u.a.]
Psychology Press
2010
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Rev. ed. of: SPSS 16 made simple. 2008. Includes bibliographical references and index |
| Beschreibung: | XVI, 645 S. Ill., graph. Darst. |
| ISBN: | 9781848720268 |
Internformat
MARC
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| 100 | 1 | |a Kinnear, Paul R. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a PASW statistics 17 made simple |c Paul R. Kinnear and Colin D. Gray |
| 264 | 1 | |a Hove [u.a.] |b Psychology Press |c 2010 | |
| 300 | |a XVI, 645 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Rev. ed. of: SPSS 16 made simple. 2008. | ||
| 500 | |a Includes bibliographical references and index | ||
| 630 | 0 | 4 | |a PASW (Computer file) |
| 630 | 0 | 4 | |a SPSS (Computer file) |
| 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 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Gray, Colin D. |e Sonstige |4 oth | |
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| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018632071&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
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| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804140702825709568 |
|---|---|
| adam_text | Contents
Preface
xv
CHAPTER
1
Introduction
1
1.1
MEASUREMENTS AND DATA
1
1.1.1
Variables: quantitative and qualitative
1
1.1.2
Levels of measurement: scale, ordinal and nominal data
2
1.1.3
A grey area: ratings
2
1.1.4
Univariate, bivariate and multivariate data sets
3
1.2
EXPERIMENTAL VERSUS CORRELATIONAL RESEARCH
3
1.2.1
A simple experiment
3
1.2.2
A more complex experiment
4
1.2.3
Correlational research
5
1.2.4
The Pearson correlation coefficient
7
1.2.5
Correlation and causation
8
1.2.6
Quasi-experiments
8
1.3
CHOOSING A STATISTICAL TEST: SOME GUIDELINES
8
1.3.1
Considerations in choosing a statistical test
9
1.3.2
Five common research situations
9
1.4
IS A DIFFERENCE SIGNIFICANT?
10
1.4.1
The design of the experiment: independent versus related samples
10
1.4.2
Flow chart for selecting a suitable test for differences between means
11
1.5
ARE TWO VARIABLES ASSOCIATED?
13
1.5.1
Flow chart for selecting a suitable test for association
13
1.5.2
Measuring association in ordinal data
14
1.5.3
Measuring association in nominal data: Contingency tables
14
1.5.4
Multi-way contingency tables
/5
1.6
CAN WE PREDICT A SCORE FROM SCORES ON OTHER VARIABLES
? 15
1.6.1
Flow chart for predicting a score or category membership
15
1.6.2
Simple regression
16
1.6.3
Multiple regression
16
1.6.4
Predicting category membership: Discriminant analysis and logistic regression
17
1.7
FROM SAMPLE TO POPULATION
17
1.7.1
Flow chart for selecting the appropriate one-sample test
17
1.7.2
Goodness-of-fit: scale data
18
1.7.3
Goodness-of-fit: nominal data
18
1.7.4
Inferences about the mean of a single population
18
1.8
THE SEARCH FOR LATENT VARIABLES
19
1.9
MULTIVARIATE
STATISTICS
19
1.10
SOME STATISTICAL TERMS AND CONCEPTS
20
1.10.1
Description or confirmation?
20
1.10.2
Samples and populations
20
1.10.3
Parameters and statistics
21
1.10.4
Statistical inference
21
1.10.5
One-sample and two-sample tests of hypotheses about means
24
1.10.6
Sampling distributions
25
1.10.7
The standard normal distribution
26
1.10.8
When the population variance and standard deviation are unknown: the
t
distribution
28
1.10.9
Errors in hypothesis testing
32
1.11
A FINAL WORD
34
Recommended reading
35
CHAPTER
2
Getting started with PASW Statistics
17.0 36
2.1
OUTLINE OF A PASW SESSION
36
2.1.1
Entering the data
36
2.1.2
Selecting the exploratory and statistical procedures
37
2.1.3
Examining the output
37
2.
1
.4
A simple experiment
37
2.1.5
Preparing data for PASW
38
2.2
OPENING PASW
39
2.3
THE PASW STATISTICS DATA EDITOR
40
2.3.1
Working in Variable View
40
2.3.2
Working in Data View
45
2.3.3
Entering the data
45
2.4
A STATISTICAL ANALYSIS
49
2.4.1
An example: Computing means
49
2.4.2
Keeping more than one application open
55
2.5
CLOSING PASW
55
2.6
RESUMING WORK ON A SAVED DATA SET
55
Exercise
1
Some simple operations with PASW Statistics
17.0 53
Exercise
2
Questionnaire data
53
CHAPTER
3
Editing and manipulating files
54
3.1
MORE ABOUT THE PASW STATISTICS DATA EDITOR
54
3.1.1
Working in Variable View
54
3.1.2
Working in Data View
61
3.2
MORE ON THE PASW STATISTICS VIEWER
68
3.2.1
Editing the output
69
3.2.2
More advanced editing
70
3.2.3
Tutorials in PASW
74
3.3
SELECTING FROM AND MANIPULATING DATA FILES
74
3.3.1
Selecting cases
74
3.3.2
Aggregating data
77
3.3.3
Sorting data
79
3.3.4
Merging files
80
3.3.5
Transposing the rows and columns of a data set
85
3.4
IMPORTING AND EXPORTING DATA
87
3.4.1
Importing data from other applications
87
3.4.2
Copying output
90
3.5
PRINTING FROM PAS
W
92
3.5.1
Printing output from the Viewer
92
Exercise
3
Merging files
-
Adding cases
&
variables
97
CHAPTER
4
Exploring your data
98
4.1
INTRODUCTION
95
4.1.1
The influence of outliers and asymmetry of distribution
99
4.2
SOME USEFUL MENUS
99
4.3
DESCRIBING DATA
101
4.3.1
Describing nominal and ordinal data
101
4.3.2
Describing measurements
108
4.4
MANIPULATION OF THE DATA SET
122
4.4.1
Reducing and transforming data
122
4.4.2
The COMPUTE procedure
123
4.4.3
The
RECODE
and VISUAL BINNING procedures
129
Exercise
4
Correcting and preparing your data
136
Exercise
5
Preparing your data (continued)
136
CHAPTER
5
Graphs and charts
137
5.1
INTRODUCTION
137
5.1.1
Graphs and charts on PASW
137
5.1.2
Viewing a chart
140
5.1.3
Editing charts and saving templates
140
5.2
BAR CHARTS
141
5.2.1
Simple bar charts
141
5.2.2
Clustered bar charts
144
5.2.3
Panelled bar charts
146
5.2.4 3-D
charts
147
5.2.5
Editing a bar chart
149
5.2.6
Chart templates
151
5.3
ERROR BAR CHARTS
154
5.4
BOXPLOTS
155
5.5
PIE CHARTS
157
5.6
LINE GRAPHS
159
5.7
SCATTERPLOTS AND DOT PLOTS
162
5.7.1
Scatterplots
162
5.7.2
Dot plots
164
5.8
DUAL Y-AXIS GRAPHS
165
5.9
HISTOGRAMS
167
5.10
RECEIVER-OPERATING-CHARACTERISTIC (ROC) CURVE
169
5.10.1
The PASW ROC curve
770
5.10.2
The
ď
statistic
173
Exercise
6
Charts and graphs
174
Exercise? Recoding data; selecting cases; line graph
174
CHAPTER
6
Comparing averages: Two-sample and one-
sample tests
175
6.1
OVERVIEW
775
6.2
COMPARING MEANS: THE INDEPENDENT-SAMPLES
T
TEST WITH PASW
776
6.2.1
Preparing the data file
776
6.2.2
Exploring the data
777
6.2.3
Running the
í
test
7 79
6.2.4
Interpreting the output
181
6.2.5
Two-tailed and one-tailed p-values
182
6.2.6
The effects of extreme scores and outliers in a small data set
7
Si
6.2.7
Measuring effect size
183
6.2.8
Reporting the results of a statistical test
785
6.3
THE RELATED-SAMPLES (OR PAIRED-SAMPLES)
T
TEST WITH PASW
186
6.3.1
Preparing the data file
187
6.3.2
Exploring the data
187
6.3.3
Running the
t
test
188
6.3.4
Interpreting the output
189
6.3.5
Measuring effect size
790
6.3.6
Reporting the results of the test
790
6.3.7
A one-sample test
797
6.4
THE MANN-WHITNEY
U
TEST
797
6.4.1
Nonparametric tests in PASW
797
6.4.2
Independent samples: The Mann-Whitney
U
test
792
6.4.3
Output for the Mann-Whitney
U
test
194
6.4
A Effect size
194
6.4.5
The report
795
6.5
THE WILCOXON MATCHED-PAIRS TEST
7 96
6.5.1
The Wilcoxon matched-pairs tests in PASW
196
6.5.2
The output
797
6.5.3
Effect size
798
6.5.4
The report
798
6.6
THE SIGN AND BINOMIAL TESTS
798
6.6.1
The sign test in PASW
799
6.6.2
Bernoulli trials: the binomial test
207
6.7
EFFECT SIZE, POWER AND THE NUMBER OF PARTICIPANTS
204
6.7.1
How many participants shall I need in my experiment?
204
6.8
A FINAL WORD
206
Exercise
8.
Comparing the averages of two independent samples of data
206
Exercise
9.
Comparing the averages of two related samples of data
206
Exercise
10.
One-sample tests
206
CHAPTER
7
The one-way ANOVA
207
7.1
INTRODUCTION
207
7.1.1
A more complex drag experiment
207
7.1.2
ANOVA models
208
7.1.3
The one-way ANOVA
208
7.2
THE ONE-WAY ANOVA (COMPARE MEANS MENU)
215
7.2.1
Entering the data
215
7.2.2
Running the one-way ANOVA
218
7.2.3
The output
218
7.2.4
Effect size
219
7.2.5
Report of the primary analysis
222
7.2.6
The two-group case: equivalence of
F
and
1
222
7.3
THE ONE-WAY ANOVA (GLM MENU)
223
7.3.1
Factors with fixed and random effects
223
7.3.2
The analysis of covariance
(ANCOVA)
224
7.3.3
Univariate versus multivariate statistical tests
224
7.3.4
The one-way ANOVA with GLM
224
7.3.5
The GLM output
226
7.3.6
Requesting additional items
227
13Л
Additional output from GLM
229
7.4
MAKING COMPARISONS AMONG THE TREATMENT MEANS
232
7.4.1
Planned and unplanned comparisons
232
7.4.2
Linear contrasts
236
7.5
TREND ANALYSIS
247
7.5.1
Polynomials
248
7.6
POWER AND EFFECT SIZE IN THE ONE-WAY ANOVA
249
7.6.1
How many participants shall I need? Using G*Power
3 250
7.7
ALTERNATIVES TO THE ONE-WAY ANOVA
252
7.7.1
The Kruskal-Wallis k-sample test
252
7.7.2
Dichotomous nominal data: the chi-square test
259
7.8
A FINAL WORD
259
Recommended reading
260
Exercise
11
One-factor between subjects ANOVA
260
Appendix
7.4.2.4
Partition of the between groups sum of squares into the sums of squares
of the contrasts in an orthogonal set
260
Appendix
7.5.1
An illustration of trend analysis
261
CHAPTER
8
Between subjects factorial experiments
265
8.1
INTRODUCTION
265
8.1.1
An experiment with two treatment factors
265
8.1.2
Main effects and interactions
267
8.1.3
Profile plots
267
8.2
HOW THE TWO-WAY ANOVA WORKS
269
8.2.1
The two-way ANOVA
269
8.2.2
Degrees of freedom
270
8.2.3
The two-way ANOVA summary table
2 71
8.3
THE T
WO-WA
Y
ANO VA
WITH
PAS W
2 72
8.3.1
Entering the data for the factorial ANOVA
273
8.3.2
Exploring the data: boxplots
274
8.3.3
Choosing a factorial ANOVA
274
8.3.4
Output for a factorial ANOVA
276
8.3.5
Measuring effect size in the two-way ANOVA
278
8.3.6
Reporting the results of the two-way ANOVA
281
8.4
FURTHER ANALYSIS
282
8.4.1
The danger with multiple comparisons
282
8.4.2
Unpacking significant main effects: post hoc tests
282
8.4.3
The analysis of interactions
283
8.5
TESTING FOR SIMPLE MAIN EFFECTS WITH SYNTAX
285
8.5.1
The syntax editor
285
8.5.2
Building syntax files automatically
286
8.5.3
Using the
MÁNOVA
command to run the univariate ANOVA
286
8.6
HOW MANY PARTICIPANTS SHALL I NEED FOR MY TWO-FACTOR
EXPERIMENT?
294
8.7
MORE COMPLEX EXPERIMENTS
294
8.7.1
Three-way interactions
295
8.7.2
The three-way ANOVA
296
8.7.3
How the three-way ANOVA works
297
8.7.4
Measures of effect size in the three-way ANOVA
299
8.7.5
How many participants shall I need?
299
8.7.6
The three-way ANOVA with PASW
299
8.7.7
Follow-up analysis following a significant three-way interaction
302
8.7.8
Using PASW syntax to test for simple interactions and simple, simple main
effects
303
8.7.9
Unplanned multiple comparisons following a significant three-way interaction
306
8.8
A FINAL WORD
309
Recommended reading
309
Exercise
12
Between subjects factorial ANOVA (two-way ANOVA)
309
CHAPTER
9
Within subjects experiments
310
9.1
INTRODUCTION
310
9.1.1
Rationale of a within subjects experiment
310
9.1.2
How the within subjects ANOVA works
311
9.1.3
A within subjects experiment on the effect of target shape on shooting
accuracy
314
9.1.4
Order effects: counterbalancing
315
9.1.5
Assumptions underlying the within subjects ANOVA: homogeneity of
covariance
5/5
9.2
A ONE-FACTOR WITHIN SUBJECTS ANOVA WITH PASW
317
9.2.1
Entering the data
317
9.2.2
Exploring the data: Boxplots for within subjects factors
317
9.2.3
Running the within subjects ANOVA
319
9.2.4
Output for a one-factor within subjects ANOVA
323
9.2.5
Effect
size in the within subjects ANOVA
327
9 3
POWER AND EFFECT SIZE: HOW MANY PARTICIPANTS SHALL I NEED?
329
9Л
NONPARAMETRIC EQUIVALENTS OF THE WITHIN SUBJECTS ANOVA
330
9.4.1
The Friedman test for ordinal data
330
9.4.2
Cochran s
Q
test for nominal data
333
9.5
THE TWO-FACTOR WITHIN SUBJECTS ANOVA
334
9.5.1
Preparing the data set
336
9.5.2
Running the two-factor within subjects ANOVA
336
9.5.3
Output for a two-factor within subjects ANOVA
339
9.5.4
Unpacking a significant interaction with multiple comparisons
343
9.6
A FINAL WORD
345
Recommended reading
346
Exercise
13
One-factor within subjects (repeated measures) ANOVA
346
Exercise
14
Two-factor within subjects ANOVA
346
CHAPTER
10
Mixed factorial experiments
347
10.1
INTRODUCTION
347
10.1.1
A mixed factorial experiment
347
10.1.2
Classifying mixed factorial designs
348
10.1.3
Rationale of the mixed ANOVA
349
10.2
THE TWO-FACTOR MIXED FACTORIAL ANOVA WITH PASW
351
10.2.1
Preparing the PASW data set
351
10.2.2
Exploring the results: Boxplots
352
10.2.3
Running the ANOVA
555
10.2.4
Output for the two-factor mixed ANOVA
355
10.2.5
Simple effects analysis with syntax
360
10.3
THE THREE-FACTOR MIXED ANOVA
365
10.3.1
The two three-factor designs
365
10.3.2
Two within subjects factors
366
10.3.3
Using syntax to test for simple effects
371
10.3.4
One within subjects factor and two between subjects factors: the AxBx(C)
mixed factorial design
375
10.4
THE MULTIV
ARIATE
ANALYSIS OF VARIANCE
(MÁNOVA)
382
10.4.1
What the
MÁNOVA
does
382
10.4.2
How the
MÁNOVA
works
384
10.4.3
Assumptions of
MÁNOVA
387
10.4.4
Application of
MÁNOVA
to the shape recognition experiment
387
10.5
A FINAL WORD
391
Recommended reading
392
Exercise
15
Mixed ANOVA: two-factor experiment
392
Exercise
16
Mixed ANOVA: three-factor experiment
392
CHAPTER
11
Measuring statistical association
393
11.1
INTRODUCTION
393
11.1.1
A correlational study
394
11.1.2
Linear relationships
395
11.1.3
Error
in measurement
395
11.2
THE PEARSON CORRELATION
396
11.2.1
Formula for the Pearson correlation
396
11.2.2
The range of values of the Pearson correlation
397
11.2.3
The sign of a correlation
397
11.2.4
Testing an obtained value of
r
for significance
398
11.2.5
A word of warning about the correlation coefficient
399
11.2.6
Effect size
399
11.3
CORRELATION WITH PASW
401
11.3.1
Preparing the PASW data set
402
11.3.2
Obtaining the scatterplot
402
11.3.3
Obtaining the Pearson correlation
403
11.3.4
Output for the Pearson correlation
404
11.4
OTHER MEASURES OF ASSOCIATION
405
11.4.1
Spearman
s
rank correlation
405
11.4.2
Kendall
s tau
statistics
406
11.4.3
Rank correlations with PASW
406
11.5
NOMINAL DATA
408
11.5.1
The approximate chi-square goodness-of-fit test with three or more categories
408
11.5.2
Running a chi-square goodness-of-fit test on PASW
409
11.5.3
Measuring effect size following a chi-square test of goodness-of-fit
412
11.5.4
Testing for association between two qualitative variables in a contingency
table
414
11.5.5
Analysis of contingency tables with PASW
419
11.5.6
Getting help with the output
425
11.5.7
Some cautions and caveats
426
11.5.8
Other problems with traditional chi-square analyses
431
11.6
DO DOCTORS AGREE? COHEN
S
KAPPA
432
11.7
PARTIAL CORRELATION
434
11.7.1
Correlation does not imply causation
434
11.7.2
Meaning of partial correlation
435
11.8
CORRELATION IN MENTAL TESTING: RELIABILITY
437
11.8.1
Reliability and number of items: coefficient alpha
438
11.8.2
Measuring agreement among judges: the intraclass correlation
440
11.8.3
Reliability analysis with PASW
441
11.9
A FINAL WORD
443
Recommended reading
443
Exercise
17
The Pearson correlation
443
Exercise
18
Other measures of association
443
Exercise
19
The analysis of nominal data
443
CHAPTER
12
Regression
444
12.1
INTRODUCTIONS
12.1.1
Simple, two-variable regression
444
12.1.2
Residuals
446
12.1.3
The least squares criterion
447
12.1 -4
Partition of the sum of squares in regression
447
12.1.5
Effect size in regression
449
12.1.6
Shrinkage
450
12.1.7
Regression models
450
12.1.8
Beta- weights
451
12.1.9
Significance testing in simple regression
452
12.2
SIMPLE REGRESSION WITH PASW
453
12.2.1
Drawing scatterplots with regression lines
453
12.2.2
A problem in simple regression
455
12.2.3
Procedure for simple regression
456
12.2
A Output for simple regression
459
12.3
MULTIPLE REGRESSION
464
12.3.1
The multiple correlation coefficient
R
465
12.3.2
Significance testing in multiple regression
466
12.3.3
Partial and semipartial correlation
467
12.4
MULTIPLE REGRESSION WITH PASW
472
12.4.1
Simultaneous multiple regression
474
12.4.2
Stepwise multiple regression
477
12.5
REGRESSION AND ANALYSIS OF VARIANCE
480
12.5.1
The point-biserial correlation
480
12.5.2
Regression and the one-way
ANO VA
for two groups
481
12.5.3
Regression and dummy coding: the two-group case
483
12.5.4
Regression and the one-way
ANO VA
485
12.6
MULTILEVEL REGRESSION MODELS
489
12.7
A FINAL WORD
489
Recommended reading
489
Exercise
20
Simple, two-variable regression
490
Exercise
21
Multiple regression
490
CHAPTER
13
Analyses of multiway frequency tables
&
multiple
response sets
491
13.1
INTRODUCTION
491
13.1.1
Muliple response sets
492
13.2
SOME BASICS OF
LOGLINEAR
MODELLING
492
13.2.1 Loglinear
models and ANOVA models
493
13.2.2
Model-building and the hierarchical principle
494
13.2.3
The main-effects-only
loglinear
model and the traditional chi-square test for
association
497
13.2.4
Analysis of the residuals
497
13.3
MODELLING A TWO-WAY CONTINGENCY TABLE
498
13.3.1
PASW procedures for
loglinear
analysis
498
13.3.2
Fitting an unsaturated model
504
13.3.3
Summary
508
13.4
MODELLING A THREE-WAY FREQUENCY TABLE
508
13.4.1
Exploring the data
509
13.4.2 Loglinear
analysis of the data on gender and helpfulness
510
13.4.3
The main-effects-only model and the traditional chi-square test
514
13.4.4
Collapsing a multi-way table: the requirement of conditional independence
576
13.4.5
An alternative data set for the gender and helpfulness experiment
518
13.4.6
Reporting the results of
a
loglinear
analysis
521
13.5
MULTIPLE RESPONSE SETS
521
13.5.1
How PASW produces multiple response profiles
522
13.6
A FINAL WORD
530
Recommended reading
530
Exercise
22 Loglinear
analysis
55/
CHAPTER
14
Discriminant analysis and logistic regression
532
14.1
INTRODUCTION
532
14.1.1
Discriminant analysis
533
14.1.2
Types of discriminant analysis
534
14.1.3
Stepwise discriminant analysis
534
14.1.4
Restrictive assumptions of discriminant analysis
535
14.2
DISCRIMINANT ANALYSIS WITH PASW
535
14.2.1
Preparing the data set
536
14.2.2
Exploring the data
536
14.2.3
Running discriminant analysis
537
14.2.4
Output for discriminant analysis
539
14.2.5
Predicting group membership
547
14.3
BINARY LOGISTIC REGRESSION
549
14.3.1
Logistic regression
549
14.3.2
How logistic regression works
551
14.3.3
An example of a binary logistic regression with quantitative independent
variables
553
14.3.4
Binary logistic regression with categorical independent variables
562
14.4
MULTINOMIAL LOGISTIC REGRESSION
565
14.4.1
Running multinomial logistic regression
566
14.5
A FINAL WORD
569
Recommended reading
570
Exercise
23
Predicting category membership: Discriminant analysis and binary logistic
regression
570
CHAPTER
15
Latent variables: exploratory factor analysis
&
canonical correlation
571
15.1
INTRODUCTION
571
15.1.1
Stages in an exploratory factor analysis
573
15.1.2
The extraction of factors
574
15.1.3
The rationale of rotation
574
15.1.4
Some issues in factor analysis
574
15.1.5
Some key technical terms
575
15.2
A FACTOR ANALYSIS OF DATA ON SIX VARIABLES
576
15.2.1
Entering the data for a factor analysis
576
15.2.2
Running a factor analysis on PASW
576
15.2.3
Output
for factor analysis
579
15.3
USING PASW SYNTAX TO RUN A FACTOR ANALYSIS
590
15.3.1
Running a factor analysis with PASW syntax
590
15.3.2
Using a correlation matrix as input for factor analysis
590
15.3.3
Progressing with PASW syntax
593
15.4
CANONICAL CORRELATION
593
15.4.1
Running canonical correlation on PASW
594
15.4.2
Output for canonical correlation
595
15.5
A FINAL WORD
600
Recommended reading
601
Exercise
24
Factor analysis
601
Appendix
602
Glossary
605
References
624
Index
626
SPSS IS NOW
Reflecting the latest developments in statistics software from SPSS Inc., this new edition of one of
the most widely read textbooks in its field keeps the reader abreast of the latest improvements in
PASW Statistics
17
(the new name for SPSS Statistics
17).
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most powerful and versatile statistical packages on the market.The new edition combines clarity
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the coverage has been extended to include topics in which our readers have expressed particular
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the book will continue to be useful to readers with previous versions (SPSS
1 6
and earlier).
Each statistical technique is presented in a realistic research context and is fully illustrated with
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each technique can be found in the body of the book.The next chapters introduce the reader to
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In summary, PASW Statistics
17
Made Simple:
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Reviews the elements of statistical inference.
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Helps you to choose appropriate statistical techniques.
♦
Warns you of the pitfalls arising from the misuse of statistics,
v
Shows you how to report the results of a statistical analysis.
Shows you how to use syntax to implement some useful procedures and operations.
Has a comprehensive index, which allows you to find a topic by several different routes.
Has a comprehensive glossary.
The book is accompanied by online instructor resources, including a PowerPoint lecture course
and a multiple-choice question bank.The book s dedicated website also features a comprehensive
set of exercises to familiarise the reader with inputting data and choosing statistical techniques.
|
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| callnumber-sort | HA 232 |
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| classification_rvk | QH 200 |
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| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.5/5 |
| dewey-search | 005.5/5 |
| dewey-sort | 15.5 15 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Soziologie Wirtschaftswissenschaften |
| format | Book |
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| language | English |
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| spelling | Kinnear, Paul R. Verfasser aut PASW statistics 17 made simple Paul R. Kinnear and Colin D. Gray Hove [u.a.] Psychology Press 2010 XVI, 645 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Rev. ed. of: SPSS 16 made simple. 2008. Includes bibliographical references and index PASW (Computer file) SPSS (Computer file) 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 1\p DE-604 Gray, Colin D. Sonstige oth Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018632071&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018632071&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kinnear, Paul R. PASW statistics 17 made simple PASW (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS (DE-588)7613615-2 gnd |
| subject_GND | (DE-588)7613615-2 |
| title | PASW statistics 17 made simple |
| title_auth | PASW statistics 17 made simple |
| title_exact_search | PASW statistics 17 made simple |
| title_full | PASW statistics 17 made simple Paul R. Kinnear and Colin D. Gray |
| title_fullStr | PASW statistics 17 made simple Paul R. Kinnear and Colin D. Gray |
| title_full_unstemmed | PASW statistics 17 made simple Paul R. Kinnear and Colin D. Gray |
| title_short | PASW statistics 17 made simple |
| title_sort | pasw statistics 17 made simple |
| topic | PASW (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS (DE-588)7613615-2 gnd |
| topic_facet | PASW (Computer file) SPSS (Computer file) Sozialwissenschaften Social sciences Statistical methods Computer programs SPSS 16.0 für WINDOWS |
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