Statistical methods for communication science:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Mahwah, NJ [u.a.]
Erlbaum
2005
|
| Series: | LEA's communication series
|
| Subjects: | |
| Online Access: | Table of contents only Publisher description Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke Includes bibliographical references (p. 491-506) and index |
| Physical Description: | XVIII, 517 S. graph. Darst. 26 cm CD-ROM (12 cm) |
| Format: | No info to include. |
| ISBN: | 0805854878 |
Staff View
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| 245 | 1 | 0 | |a Statistical methods for communication science |c Andrew F. Hayes |
| 264 | 1 | |a Mahwah, NJ [u.a.] |b Erlbaum |c 2005 | |
| 300 | |a XVIII, 517 S. |b graph. Darst. |c 26 cm |e CD-ROM (12 cm) | ||
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| 490 | 0 | |a LEA's communication series | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 500 | |a Includes bibliographical references (p. 491-506) and index | ||
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Record in the Search Index
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| adam_text | CONTENTS
Contents vii
Preface xv
1 Statistics and Communication Science 1
1.1 Welcome 1
1.2 Why Do Science? 2
1.3 Assumptions and Philosophies of Scientific Investigation 5
1.4 Building Your Statistical Vocabulary 8
1.5 The Role of Statistics in Scientific Investigation 12
1.6 Summary 14
2 Fundamentals of Measurement 16
2.1 Measurement Concepts 16
2.1.1 Methods of Measurement 17
2.1.2 Operationalization 18
2.1.3 Levels of Measurement 20
2.1.4 Measurement Precision 23
2.1.5 Qualitative Data versus Quantitative Measurement 24
2.2 Measurement Quality 24
2.2.1 Reliability of Measurement 24
2.2.2 Validity of Measurement 25
2.3 Summary 30
3 Sampling 31
3.1 Population Inference 31
3.1.1 The Literary Digest Poll: Population Inference Gone Awry ... 33
3.1.2 Population Inference Through Representativeness 35
3.2 Sampling Methods 36
3.2.1 Nonprobability Sampling 36
3.2.2 Probability Sampling 38
3.3 Is Nonprobability Sampling Really So Bad? 41
3.4 Summary 44
viii CONTENTS
4 Data Description and Visualization 45
4.1 Graphical and Tabular Descriptions of Data 45
4.1.1 Frequency Tables 45
4.1.2 The Histogram 48
4.1.3 Describing the Shape of a Distribution 49
4.2 Measures of Central Tendency 51
4.2.1 The Mode 51
4.2.2 The Median 52
4.2.3 The Arithmetic Mean 53
4.2.4 Choosing a Measure of Central Tendency 54
4.3 Measures of Variation 56
4.3.1 The Range and Interquartile Range 57
4.3.2 The Standard Deviation 57
4.3.3 The Variance 59
4.4 Quantifying Skewness and Kurtosis 59
4.5 Another Graphical Tool: The Box Plot 60
4.6 Standardization 61
4.7 Describing Association Between Quantitative Variables 63
4.7.1 Pearson s Coefficient of Correlation 64
4.7.2 Alternative Measures of Association 68
4.7.3 Cautions When Interpreting Correlation 70
4.8 Visualizing Correlation: The Scatterplot 73
4.9 Descriptively Comparing Groups 76
4.10 Data Screening and Missing Data 78
4.11 Introducing Some Common Symbolic Notation 80
4.12 Summary 81
5 Fundamentals of Probability 83
5.1 Defining Probability 84
5.2 Laws of Probability 86
5.2.1 The Additive Law of Probability 86
5.2.2 The Multiplicative Law of Probability 88
5.3 Probability Distributions 90
5.3.1 The Binomial Probability Distribution 91
5.3.2 The Normal Probability Distribution 94
5.3.3 Chebychev s Theorem 99
5.4 Random Variables and Expected Values 99
5.5 Summary 102
6 Assessing and Quantifying Reliability 103
6.1 Classical Test Theory 105
6.1.1 Pardoning Measurements into Their Components 105
6.1.2 The Definition of Reliability Under Classical Test Theory .... 108
6.2 Estimating the Reliability of Quantitative Measurements 110
6.2.1 Estimating Reliability From Repeated Measurements
Over Time HO
6.2.2 Estimating Reliability From Internal Consistency of
Indicator Scores HI
6.2.3 Reliability of Method or of Measurement? 117
6.3 Reliability of Subjective Categorical Judgments 118
CONTENTS ix
6.3.1 Holsti s Method 121
6.3.2 Correcting for Chance Agreement: Scott s tt and Cohen s k . . . 121
6.3.3 Using an Agreement Index 126
6.4 How High is High Enough? 128
6.5 Summary 128
7 Parameter Estimation 130
7.1 The Theory of Estimation 130
7.1.1 The Sampling Distribution 131
7.1.2 Properties of the Sampling Distribution of the Sample Mean . . 135
7.1.3 Deriving the Probability of Obtaining Certain Sample Means . . 141
7.2 Interval Estimates 143
7.2.1 The Confidence Interval 144
7.2.2 A More Realistic Approach to Interval Estimation 146
7.2.3 The Relationship Between Confidence and Precision 149
7.2.4 Computing the Probability of a Sample Mean, Revisited 150
7.2.5 Interval Estimates Derived Prom Samples of Convenience .... 151
7.3 Estimating a Population Proportion 151
7.4 Bootstrapping a Confidence Interval 154
7.5 Summary 157
8 Hypothesis Testing Concepts 158
8.1 Hypothesis Testing Steps 160
8.1.1 Step 1: Translate the Research Hypothesis or Question
into a Statistical Hypothesis 160
8.1.2 Step 2: Quantify the Obtained Result 162
8.1.3 Step 3: Derive the p-value 163
8.1.4 Step 4: Decide Between the Null and Alternative Hypothesis . . 165
8.1.5 Step 5: Interpret the Result of the Test in Substantive Terms . . 167
8.2 Testing a Hypothesis About a Population Proportion 167
8.2.1 Testing a Nondirectional ( Two-tailed ) Hypothesis 168
8.2.2 Testing a Directional ( One-tailed ) Hypothesis 173
8.3 Decision Errors, Power, and Validity 175
8.3.1 Type I, Type II, and Type III errors 175
8.3.2 The Validity and Power of a Statistical Test 178
8.4 Hypothesis Test or Confidence Interval? 179
8.5 Summary 182
9 Testing a Hypothesis About a Single Mean 183
9.1 The One-Sample t test 183
9.1.1 Testing a Directional Hypothesis About a Single Mean 185
9.1.2 Testing a Nondirectional Hypothesis 191
9.1.3 Conducting the One-Sample t test With a Computer 193
9.1.4 Statistical Assumptions 194
9.1.5 Large Versus Small Samples 195
9.1.6 Confidence Intervals 195
9.1.7 Bootstrapping the p-value 196
9.2 Comparing the Means of Paired Responses 196
9.2.1 The Paired-Samples t test 197
9.2.2 Paired-Sample Inference From Nonrandom Samples 201
x CONTENTS
9.3 Summary 209
10 Comparing Two Independent Groups 210
10.1 The Independent Groups t test 211
10.1.1 The Pooled Variance Approach 213
10.1.2 The Welch-Satterthwaite Approach 217
10.1.3 The Conditional Decision Rule 218
10.1.4 The Behrens-Fisher Problem 219
10.1.5 Violations of the Normality Assumption 220
10.1.6 Confidence Intervals for the Mean Difference 221
10.1.7 Bootstrapping Confidence Intervals and p-values 221
10.1.8 Effect Size 223
10.2 Testing for Group Differences in Variability 224
10.2.1 Levene sTest 225
10.2.2 The Brown:Forsythe Test 226
10.2.3 The .F-ratio Test: A Test to Avoid 228
10.3 Comparing Two Groups from Nonrandom Samples 228
10.3.1 The Random Assignment Model of Chance 233
10.3.2 Inference Without Random Sampling or Random Assignment . . 237
10.4 Thinking Clearly About Inference 241
10.5 Comparing Two Independent Proportions ¦ . . 242
10.6 Summary 242
11 Some Tests for Categorical Variables 244
11.1 Testing a Hypothesis About a Frequency Distribution 244
11.1.1 Testing the Hypothesis of Equal Relative Frequencies 245
11.1.2 Testing the Fit of Observed Frequencies to Any Expectation . . 250
11.1.3 Statistical Assumptions 251
11.1.4 Alternative Tests for the k = 2 Case 252
11.1.5 Testing Goodness of Fit for Process Inference 254
11.2 Association Between Two Categorical Variables 256
11.2.1 Testing for Independence in a 2x2 Crosstabulation 257
11.2.2 Quantifying Strength of Association in a 2 x 2 Table 262
11.2.3 An Alternative Test: The Z test for Independent Proportions . . 264
11.2.4 Another Example With a 3 x 2 Crosstabulation 265
11.2.5 Testing for Independence in Larger Tables 269
11.2.6 Quantifying Association in a Table of Any Size 269
11.3 Summary 270
12 Simple Linear Regression 271
12.1 The Simple Linear Regression Model 274
12.1.1 The Simple Regression Line 275
12.1.2 The Least Squares Criterion 279
12.1.3 The Standard Error of Estimation 282
12.1.4 The Standardized Regression Equation 283
12.1.5 Variance Explained by a Regression Model 285
12.1.6 More on Residuals 287
12.1.7 The Dangers of Extrapolating Away From the Data 289
12.2 Population Inference in Linear Regression 289
12.2.1 Testing a Hypothesis About the Population Regression Weight . 290
CONTENTS xi
12.2.2 Confidence Intervals for p 293
12.2.3 Refraining Inference In Terms of the Population Correlation . . . 293
12.2.4 Statistical Assumptions 296
12.3 Inference in Nonrandom Samples: The Permutation Test 301
12.4 Detecting Influential Cases 305
12.4.1 Distance, Leverage, and Influence 305
12.4.2 Influence as Change in the Model When a Case is Excluded . . . 306
12.5 Summary 309
13 Multiple Linear Regression 310
13.1 The Multiple Regression Model 312
13.2 Quantifying and Testing for Multivariate Association 314
13.2.1 R2 as an Index of Multivariate Association 314
13.2.2 Average Estimation Error 319
13.2.3 Population Inferences About Multivariate Association 319
13.2.4 Adjusted R2 322
13.3 Partial Association 323
13.3.1 Experimental Versus Statistical Control 323
13.3.2 Mechanisms That Can Produce Association Between
Two Variables 325
13.3.3 Measures of Partial Association 328
13.3.4 Partial Versus Semipartial Correlation 334
13.3.5 The Standardized Partial Regression Coefficient 336
13.3.6 Inference for Measures of Partial Association 337
13.3.7 Confidence Intervals for Measures of Partial Association 339
13.3.8 Assumptions for Statistical Inference and Detecting
Influential Cases 340
13.4 Setwise Partial Association and Hierarchical Regression 342
13.4.1 Setwise Partial and Semipartial Correlations 343
13.4.2 Calculating SR2 and PR2 344
13.4.3 Inference for Measures of Setwise Partial Association 345
13.4.4 Interpreting Regression Statistics for the Predictor Variables . . 347
13.5 Modeling and Testing for Nonlinearity 349
13.5.1 The Quadratic Regression Model 350
13.5.2 Interpreting the Coefficients in a Quadratic Regression Model . . 352
13.6 Miscellaneous Issues in Multiple Regression 353
13.6.1 A Reminder About Causality 353
13.6.2 Number of Cases Required for a Regression Analysis 354
13.6.3 Multicollinearity 354
13.6.4 Selecting Predictor Variables to Include in a Regression Model . 357
13.6.5 The Relative Importance of Predictor Variables 359
13.6.6 The Effects of Measurement Error 361
13.6.7 Categorical, Ordinal, and Bounded Outcome Variables 363
13.7 Summary 365
14 Single Factor Analysis of Variance 366
14.1 Analysis of Variance 367
14.1.1 Partitioning the Outcome Variable into its Sources of Variation . 368
14.1.2 Total, Between-, and Within-Group Variability in Y 370
14.1.3 The F ratio 371
xii CONTENTS
14.1.4 Underlying Statistical Theory 373
14.1.5 Statistical Assumptions of ANOVA 374
14.1.6 Revisiting the Pooled Variance t test 376
14.1.7 Quantifying Effect Size 376
14.1.8 Why Not Multiple t tests? . . . 378
14.1.9 Inference with Nonrandom Samples 380
14.2 Pairwise Mean Comparisons 384
14.2.1 The Multiple Test Problem Resurfaces 385
14.2.2 The Bonferroni Correction 386
14.2.3 Holm s Sequential Rejection Method 387
14.2.4 The Games-Howell Method 389
14.2.5 Using a Pooled Error Term 389
14.3 Focused Contrasts 391
14.3.1 Focused t tests 391
14.3.2 Contrast Coefficients 392
14.3.3 Scheffe s Test 397
14.4 ANOVA as a Special Case of Multiple Regression 399
14.4.1 Coding a Categorical Variable for a Regression Analysis 399
14.4.2 Testing the Omnibus Null Hypothesis Using Regression 400
14.4.3 Interpreting the Regression Model 401
14.5 Some Controversies in the Comparison of Multiple Groups 402
14.5.1 Planned Versus Unplanned Comparisons: To Correct or Not? . . 402
14.5.2 Are We A Bit Fickle About The Multiple Test Problem? .... 404
14.5.3 Do We Really Need ANOVA? 405
14.6 Summary 406
15 Analysis of Covariance: ANOVA With Statistical Controls 407
15.1 Analysis of Covariance as Multiple Regression 408
15.1.1 Conducting an ANCOVA With Regression 409
15.1.2 Partitioning Variability in Y in ANCOVA 410
15.1.3 Measures of Effect Size 414
15.1.4 Adjusted Means 416
15.1.5 Focused Contrasts Between Adjusted Means . . . 417
15.1.6 Statistical Assumptions 418
15.1.7 Multiple Covariates 418
15.2 Analysis of Covariance in Experimental Designs 421
15.2.1 Controlling for Variables Unaffected by the Manipulation .... 421
15.2.2 Controlling for Variables Affected by the Manipulation 424
15.2.3 Mediation 425
15.3 Summary 427
16 Interaction 428
16.1 Interaction in Communication Research and Theory 429
16.2 Factorial Analysis of Variance 433
16.2.1 Partitioning Variance in Y in a Balanced Factorial Design .... 435
16.2.2 Main and Interaction Effects 438
16.2.3 The Regression Equivalent of a 2 x 2 Factorial ANOVA 440
16.2.4 Factorial ANOVA and Unbalanced Designs 441
16.2.5 Probing an Interaction in a Factorial ANOVA 446
16.2.6 Quantifying Effect Size 449
CONTENTS xiii
16.3 Moderated Multiple Regression 451
16.3.1 Interaction Between a Dichotomous and a Quantitative
Variable 452
16.3.2 Interaction Between Two Quantitative Variables 458
16.3.3 Interaction Between a Quantitative and a Multicategorical
Variable 461
16.3.4 Mean Centering of Predictors 465
16.3.5 Differences In Regression Coefficients vs. Differences in
Correlations 468
16.3.6 Statistical Control of Covariates 469
16.3.7 Required Terms in a Moderated Multiple Regression Model . . . 469
16.3.8 The Pitfalls of Subgroup Analyses to Test Moderation 470
16.4 Simplifying the Hunt for Interactions 471
16.5 Why Not to Categorize Prior to Testing for Interaction 473
16.5.1 Classification Errors 474
16.5.2 Smaller Effect Sizes and Reduced Statistical Power 475
16.5.3 Spurious Statistical Significance 477
16.5.4 Artifactual Failures to Replicate Findings 477
16.5.5 Is Categorization Ever Sensible? 479
16.6 Summary 479
Appendices 483
A: Table of Right-Tail Normal Probabilities 483
B: Table of Right-Tail Critical t Values 484
C: Table of Right-Tail Critical 2 Values 486
Dl: Table of Critical F Ratios, p .05 487
D2: Table of Critical F Ratios, p .01 489
References 491
Index 507
|
| any_adam_object | 1 |
| author | Hayes, Andrew F. |
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| author_facet | Hayes, Andrew F. |
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| bvnumber | BV035769430 |
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| callnumber-subject | P - Philology and Linguistics |
| classification_rvk | AP 13900 |
| ctrlnum | (OCoLC)314582710 (DE-599)BVBBV035769430 |
| dewey-full | 302.2021 302.2/02/1 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 302 - Social interaction |
| dewey-raw | 302.2021 302.2/02/1 |
| dewey-search | 302.2021 302.2/02/1 |
| dewey-sort | 3302.2021 |
| dewey-tens | 300 - Social sciences |
| discipline | Allgemeines Soziologie |
| format | Book |
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| id | DE-604.BV035769430 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:04:07Z |
| institution | BVB |
| isbn | 0805854878 |
| language | English |
| lccn | 2005040570 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018629156 |
| oclc_num | 314582710 |
| open_access_boolean | |
| owner | DE-824 DE-473 DE-BY-UBG |
| owner_facet | DE-824 DE-473 DE-BY-UBG |
| physical | XVIII, 517 S. graph. Darst. 26 cm CD-ROM (12 cm) |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Erlbaum |
| record_format | marc |
| series2 | LEA's communication series |
| spelling | Hayes, Andrew F. Verfasser (DE-588)103663003X aut Statistical methods for communication science Andrew F. Hayes Mahwah, NJ [u.a.] Erlbaum 2005 XVIII, 517 S. graph. Darst. 26 cm CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier LEA's communication series Hier auch später erschienene, unveränderte Nachdrucke Includes bibliographical references (p. 491-506) and index No info to include. Empirische Sozialforschung swd Kommunikationsforschung swd Statistik swd Communication Statistical methods Communication Research Anwendung (DE-588)4196864-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Kommunikation (DE-588)4031883-7 gnd rswk-swf Kommunikationswissenschaft (DE-588)4120588-1 gnd rswk-swf Statistik (DE-588)4056995-0 s Kommunikation (DE-588)4031883-7 s Anwendung (DE-588)4196864-5 s DE-604 Kommunikationswissenschaft (DE-588)4120588-1 s 1\p DE-604 http://www.loc.gov/catdir/enhancements/fy0662/2005040570-t.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0668/2005040570-d.html Publisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018629156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hayes, Andrew F. Statistical methods for communication science Empirische Sozialforschung swd Kommunikationsforschung swd Statistik swd Communication Statistical methods Communication Research Anwendung (DE-588)4196864-5 gnd Statistik (DE-588)4056995-0 gnd Kommunikation (DE-588)4031883-7 gnd Kommunikationswissenschaft (DE-588)4120588-1 gnd |
| subject_GND | (DE-588)4196864-5 (DE-588)4056995-0 (DE-588)4031883-7 (DE-588)4120588-1 |
| title | Statistical methods for communication science |
| title_auth | Statistical methods for communication science |
| title_exact_search | Statistical methods for communication science |
| title_full | Statistical methods for communication science Andrew F. Hayes |
| title_fullStr | Statistical methods for communication science Andrew F. Hayes |
| title_full_unstemmed | Statistical methods for communication science Andrew F. Hayes |
| title_short | Statistical methods for communication science |
| title_sort | statistical methods for communication science |
| topic | Empirische Sozialforschung swd Kommunikationsforschung swd Statistik swd Communication Statistical methods Communication Research Anwendung (DE-588)4196864-5 gnd Statistik (DE-588)4056995-0 gnd Kommunikation (DE-588)4031883-7 gnd Kommunikationswissenschaft (DE-588)4120588-1 gnd |
| topic_facet | Empirische Sozialforschung Kommunikationsforschung Statistik Communication Statistical methods Communication Research Anwendung Kommunikation Kommunikationswissenschaft |
| url | http://www.loc.gov/catdir/enhancements/fy0662/2005040570-t.html http://www.loc.gov/catdir/enhancements/fy0668/2005040570-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018629156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hayesandrewf statisticalmethodsforcommunicationscience |