An Introduction to Statistical Concepts, Second Edition

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2007-04-12
  • Publisher: Lawrence Erlbau
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Supplemental Materials

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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.


Unlike many other statistics texts, this one is comprehensive and flexible enough for either a single or a two-semester course. Instructors can select only the topics that are most appropriate for their course.Its intuitive approach helps students more easily understand the concepts and interpret software results. Throughout the text, the author demonstrates how many statistical concepts relate to one another. Only the most crucial equations are included.The new edition features: SPSS sections throughout with input, output, and APA style write-ups using the book's dataset A CD with every example and problem dataset used in the text in SPSS format; More information on confidence intervals, effect size measures, power, and regression models A revised sequence of the regression and ANOVA chapters for enhanced conceptual flow De-emphasized computations to provide more discussion of concepts and software More end of chapter problems with more realistic data and a greater emphasis on interpretation Many more references An Instructor's Resource CD with all of the solutions to the problems and other teaching aids.The first five chapters cover basic descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution and other standard scores, and probability and sampling.The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance (ANOVA) and regression models. It contains a number of topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple regression models.Realistic examples from education and the behavioral sciences illustrate the concepts. Each example includes an examination of the various procedures and necessary assumptions, tips on developing an APA style write-up, and sample SPSS output. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. Each chapter concludes with conceptual and computational problems, about a third of which are new to this edition. Answers to the odd-numbered problems are provided.Intended for a one- or a two-semester course in introductory statistics taught in education and/or behavioral science departments. Although used predominantly at the master's or doctoral level, the book is also used at the undergraduate level. Only a rudimentary knowledge of algebra is required.

Author Biography

Richard Lomax, Ph.D., is a Professor in the College of Education at The Ohio State University. He received his Ph.D. in Educational Research Methodology from the University of Pittsburgh.

Table of Contents

Prefacep. ix
Introductionp. 1
What is the value of statistics?p. 3
Brief introduction to the history of statisticsp. 5
General statistical definitionsp. 5
Types of variablesp. 7
Scales of measurementp. 8
Summaryp. 12
Data Representationp. 16
Tabular display of distributionsp. 18
Graphical display of distributionsp. 23
Percentilesp. 29
SPSSp. 33
Summaryp. 34
Univariate Population Parameters and Sample Statisticsp. 39
Summation notationp. 40
Measures of central tendencyp. 41
Measures of dispersionp. 45
SPSSp. 53
Summaryp. 55
The Normal Distribution and Standard Scoresp. 59
The normal distributionp. 60
Standard scoresp. 65
Skewness and kurtosis statisticsp. 68
SPSSp. 72
Summaryp. 73
Introduction to Probability and Sample Statisticsp. 77
Brief introduction to probabilityp. 78
Sampling and estimationp. 81
Summaryp. 87
Introduction to Hypothesis Testing: Inferences About a Single Meanp. 92
Types of hypothesesp. 93
Types of decision errorsp. 95
Level of significance [Alpha]p. 98
Overview of steps in the decision-making processp. 100
Inferences about [Mu] when [sigma] is knownp. 101
Type II error [Beta] and power [1 - Beta]p. 105
Statistical versus practical significancep. 108
Inferences about [Mu] when [sigma] is unknownp. 109
SPSSp. 113
Summaryp. 114
Inferences About the Difference Between Two Meansp. 119
New conceptsp. 120
Inferences about two independent meansp. 122
Inferences about two dependent meansp. 129
SPSSp. 133
Summaryp. 134
Inferences About Proportionsp. 140
Inferences about proportions involving the normal distributionp. 141
Inferences about proportions involving the chi-square distributionp. 151
SPSSp. 156
Summaryp. 158
Inferences About Variancesp. 162
New conceptsp. 163
Inferences about a single variancep. 164
Inferences about two dependent variancesp. 166
Inferences about two or more independent variances (homogeneity of variance tests)p. 168
SPSSp. 172
Summaryp. 173
Bivariate Measures of Associationp. 176
Scatterplotp. 177
Covariancep. 179
Pearson product-moment correlation coefficientp. 182
Inferences about the Pearson product-moment correlation coefficientp. 183
Some issues regarding correlationsp. 186
Other measures of associationp. 188
SPSSp. 191
Summaryp. 192
One-Factor Analysis of Variance-Fixed-Effects Modelp. 196
Characteristics of the one-factor ANOVA modelp. 198
The layout of the datap. 200
ANOVA theoryp. 201
The ANOVA modelp. 206
Assumptions and violation of assumptionsp. 210
The unequal n's or unbalanced designp. 213
Alternative ANOVA proceduresp. 213
SPSSp. 215
Summaryp. 217
Multiple Comparison Proceduresp. 222
Concepts of multiple comparison proceduresp. 224
Selected multiple comparison proceduresp. 228
SPSSp. 241
Summaryp. 242
Factorial Analysis of Variance-Fixed-Effects Modelp. 247
The two-factor ANOVA modelp. 249
Three-factor and higher-order ANOVAp. 265
Factorial ANOVA with unequal n'sp. 267
SPSSp. 268
Summaryp. 269
Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model With a Single Covariatep. 277
Characteristics of the modelp. 278
The layout of the datap. 281
The ANCOVA modelp. 281
The ANCOVA summary tablep. 282
Partitioning the sums of squaresp. 283
Adjusted means and related proceduresp. 283
Assumptions and violation of assumptionsp. 286
An examplep. 289
ANCOVA without randomizationp. 292
More complex ANCOVA modelsp. 293
Nonparametric ANCOVA proceduresp. 293
SPSSp. 293
Summaryp. 294
Random-and Mixed-Effects Analysis of Variance Modelsp. 301
The one-factor random-effects modelp. 303
The two-factor random-effects modelp. 306
The two-factor mixed-effects modelp. 309
The one-factor repeated measures designp. 313
The two-factor split-plot or mixed designp. 319
SPSSp. 325
Summaryp. 331
Hierarchical and Randomized Block Analysis of Variance Modelsp. 335
The two-factor hierarchical modelp. 336
The two-factor randomized block design for n = 1p. 343
The two-factor randomized block design for n > 1p. 350
The Friedman testp. 350
Comparison of various ANOVA modelsp. 351
SPSSp. 353
Summaryp. 357
Simple Linear Regressionp. 361
The concepts of simple linear regressionp. 362
The population simple linear regression modelp. 364
The sample simple linear regression modelp. 365
SPSSp. 381
Summaryp. 383
Multiple Regressionp. 387
Partial and semipartial correlationsp. 388
Multiple linear regressionp. 390
Other regression modelsp. 403
SPSSp. 408
What's next?p. 408
Summaryp. 410
Referencesp. 415
Appendix Tablesp. 425
Answersp. 449
Indexp. 463
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