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Myers J.L., Well A.D. — Research design and statistical analysis

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Название: Research design and statistical analysis

Авторы: Myers J.L., Well A.D.

Аннотация:

Adopting an intuitive, informal style, this text emphasizes the statistical concepts and assumptions needed to describe and make inferences about real data. The volume's 21 chapters address topics including univariate distributions; the chi-square and F distributions; contrasts among means; trend analysis; repeated- measures designs; hierarchical designs; Latin squares and related designs; correlation and bivariate regression; and multiple regression. Includes major content and organizational revisions from the previous edition. Suitable for graduate and advanced undergraduate students learning about data analysis, as well as for researchers.

Язык:

Рубрика: Математика/Вероятность/Статистика и приложения/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: second edition

Год издания: 2003

Количество страниц: 760

Добавлена в каталог: 05.06.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

kurtosis      31—32
Kutner, M.H.      56 737
Latin-square designs      457—477 see
LaVange, L.M.      363 737
Least absolute deviation criterion      548 see also Regression
Least-squares criterion      51—52 60
Lee, D.M.      629 737
Lee, W-C.      493—494 735
Leeuw, J. de      551 735
Lehmann, E.L.      372 735
Leighton, J.      424 734
Lepine, D.      335 739
Leroy, A.M.      56 542 585 739
Levene, H.      161 185 222 735
Levenetest      161 185 222
Leverage of       528 584 726
Levin, J.R.      253 724 736
Levine, D.W.      122 735
Lindauer, P.      436 735
Lindeman, R.H.      507 736
Lindquist, E.F.      205 217 736
Line graphs      25
Linear combinations      136—139 722 see Regression
Linear equation      44
Linear independence      616
Linear relations      43—44
Linn, R.L.      403 736
Lix, L. M.      221 225—226 736
Location      see central tendency
Lorch, R.F.      444 549 607 736 737
LOWESS      42 533
Ludwig, T.E.      479 741
Lunney, G.H.      217 736
MacGillivray, H.L.      32 729 731
Mahalanobis distance      545—548 see
Main effect contrasts      see Contrasts among means
Main effects      202 289—293 307 325
Mann— Whitney U test      220
Mans, E.      522 736
Marascuilo, L.A.      220 724 736
Marginal probability      see Probability
Matched-pair design      see Repeated-measures design
Matthews, C.E.      726 736
Matthews, K.A.      4 738
Mauchly test for sphericity      357
Mauchly, J.W.      357 736
Maxwell, S.E.      209 361 370 403 428—429 588 590—591 736
McCann, J.      163 737
McLean, R.A.      403 734
Mead, R.      333—334 736
Meade, M.L.      339 739
Mean of a linear combination      137—138
Mean of a population      79—80
Mean of a sample      11 20—23
Mean, harmonic mean      340
Mean, properties of      22
Mean, standard error of the mean (SEM)      24—25
Mean, trimmed mean      24 111 222
Mean, weighted mean      22
Measurement error and correlation      486
Measurement error and regression      531—532
Measures of importance      see Effect size
Median      11 13—15
Median, depth of      13—15
Mendoza, J.L.      357 739
Meng, X-L.      501 736
Merenda, P.F.      507 736
Merriam, P.A.      726 736
Micceri, T.      12 106 493 736
Midsummary scores      30
Miller, D.J.      531 737
Miller, R.G.      179 737
Miller, R.G., Jr.      252 737
Min F’      370 384—385
Missing scores, estimating      349—350 465
Mittlehammer, R.C.      531 726 737
Mixed designs      191 386—408 see
Mixed-normal distribution      111
Moderator variable      601
Morrison, D.F.      360 737
Morrow, L.M.      6 737
Mosteller, F.      33 179 732 734 737 739
Mulaik, S.A.      212 733 740
Muller, K.E.      363 737
Multicollinearity      570—572 596—598 see
Multifactorial ANOVA designs      see Analysis of variance
Multilevel modeling      551 see also Regression
Multiple correlation coefficient, R      200—201 524 565—580;
Multiple regression      see Regression
Multivariate analysis of variance (Manova)      359—361 see
Murray, J.E.      363—364 367 371 437 737
Muticorr program      501
Myers, J.L.      163 193 203 217 234 246 318 357 359 376 396 412 415 429 444—445 464 469 549 607 626 724 725 729 731 736 737 738
Myers, N.A.      61 75 84 737
Nachtscheim, C.J.      56 737
Namboodiri, N.K.      476 737
Negatively biased test      216 333
Nested factors      389—390 437 see Hierarchical
Neter, J.      56 531 542 585 737
Newman, D.      255 737
Neyman, J.      424 734 735
Nicewander, W.A.      46 738
Nonadditivity      see Analysis of variance
Noncentral distributions, the F      210
Noncentral distributions, the t      147—148
Noncentrality parameters      148 162 210—211 317 491 588—590
Nonorthogonal designs      319—324 623—629
Nonparametric tests      152 217 219—220 372—377
Noon, S.N.      492—493 732
Normal approximation to the binomial      128—129
Normal distribution      see Distributions
Normal equations in regression      60
Normality assumption      124 178 184—185 217 287
Notation and summation operators      see Appendix A 641—648
Nuisance variable      see Variable
Null hypothesis      see Hypothesis testing
Observational study      3
Ockene, I.S.      726 736
Odeh, R.E.      372 737
Oilman, D.O.      225 731
Olkin, I.      146 210 501 586—587 733 735 737
Omega squared, $\omega^2$       see Effect size
Omnibus null hypothesis      192 233
Omnibus null hypothesis for correlation matrices      500
One-tailed tests      see Hypothesis testing
Orthogonal contrasts      259—260
Orthogonal designs      621—623
Orthogonal polynomials      see Trend analysis
Oshima, T.C.      220 737
Outer fence      16
Outliers      15—17 55—56 542—548
Overall, J.C.      428 731
Overall, J.E.      627 629 737 738
Overton, R.C.      548 738
Owens, J.F.      4 738
O’Brien, E.J.      469 731
pairwise comparisons      see Contrasts among means
Parks, C.D.      436 443 740
Part correlation      see Correlation
Partial correlation      see Correlation
Partial F tests      582—583 see
Partial regression coefficients      564 see
Partial slope coefficients      564 see
Partitioning variability      196—197 230—231 388—390 437-444 517—518 525 573—577
Pearson, E.S.      726 738
Pedhazur, E.J.      489 502 593 738
Peritz, E.      255 738
Perlmutter, J.      257 738
Peterson, N.L.      493 733
Petrie, G.      436 735

Phi coefficient      see Correlation
Planned contrasts      see Contrasts among means
Point estimates      89 113
Point-biserial correlation coefficient      see Correlation
Pollatsek, A.      477 738
Polynomial analysis of covariance      see Analysis of covariance
pooling      332—334 441—443 see Hierarchical
Population parameters      2
Populations      2
Populations, distinguished from samples      2
Positively biased test      217
Post hoc contrasts      see Contrasts among means
Power for analysis of covariance      430
Power for bivariate regression      527—528
Power for multifactor between-subjects ANOVA      318—319
Power for multiple regression      587—591 726
Power for one-factor between-subjects ANOVA      212—215
Power for one-sample t test      147—151
Power for repeated-measures designs      363
Power for t test of two independent means      157—158
Power for test of a single correlation      495—496
Power for test of the equality of independent correlations      498
Power for tests using the binomial distribution      83—86
Power for z test      119—122
Power transformation      223—224 351—352
Power, a priori calculation      147
Power, factors affecting power      121—122
Power, functions for the binomial distribution      85
Power, GPOWER      see GPOWER
Power, post hoc calculation      147
Power, specific alternative hypothesis      84
Power, SYSTAT power module      493 497 725
Pretest-posttest designs      402—403
Probability distributions      see Distributions
Probability of unions of events      69
Probability plot      287
Probability, conditional      69—70 97—99
Probability, definition      68 72
Probability, density function      101
Probability, introduction to      67—72
Probability, joint      68
Probability, marginal      68
Probability, posterior      98
Probability, prior      98
Probability, rules of      71 72
Pseudogroup procedure      444
Pun, F.C.      592 738
Pure error      537—539
Quasi-F tests      368—370 444—445 451—452
R2 program      586
Raaijmakers, J.G.W.      473 738
Raekkoenen, K.      4—5 738
Ragosa, D.      522 738
Ramey, C.T.      363 737
Ramey, S.L.      363 737
Ramsey, P.H.      255 738
Randies, R.K.      111 734
Random assignment      4
Random coefficients modeling      see Multilevel modeling
random selection      68
Random-effects variables      192
Random-effects variables as opposed to fixed effects variables      202 371—372
Rank-transformation F test      219 373
Rasmussen, J.L.      496 738
Ratcliff, R.      121 738
Raudenbush, S. W.      551 730
Raw-score formulas      see Computational formulas
Reference group      see Coding of categorical variables
Regression analysis      see Regression
Regression with categorical variables      614—637
Regression, adjusted (shrunken) multiple correlation coefficient R      524 577—580
Regression, bivariate regression      51—54 519—561
Regression, bivariate regression, coefficients are unbiased estimators      558—559
Regression, bivariate regression, coefficients as linear combinations of the Y scores      557—559
Regression, bivariate regression, egression from the mean      522
Regression, bivariate regression, inference, see Regression      Inference about bivariate regression
Regression, bivariate regression, least absolute deviation criterion      548
Regression, bivariate regression, least-squares criterion      51—52 60
Regression, bivariate regression, partitioning of variability      517—518 525
Regression, bivariate regression, regression coefficients      522—523
Regression, bivariate regression, regression toward the mean      519—522
Regression, bivariate regression, standardized coefficients      525
Regression, bivariate regression, two-group ANOVA as a special case      529—531
Regression, capitalization on chance      577—580
Regression, checking for violations of assumptions independence, and the Durbin-Watson statistic      533 541—542
Regression, checking for violations of assumptions, linearity      537—539 598—599
Regression, checking for violations of assumptions, using residuals      536—538
Regression, coding of categorical variables      615—621 727
Regression, coding of categorical variables, dummy coding      617 620—621 727
Regression, coding of categorical variableseffect coding      617—621
Regression, coefficients as linear combinations of Y scores      559—561
Regression, cross-validation      577—580
Regression, curvilinearity, testing for      598—601
Regression, direct and indirect effects      596
Regression, factorial designs      621—629
Regression, factorial designs, nonorthogonal designs      623—629
Regression, factorial designs, orthogonal designs      621—623
Regression, homogeneity of regression slopes      630—631
Regression, independent slopes, testing for equality      548—549
Regression, inference about bivariate regression      522—532
Regression, inference about bivariate regression, estimators of regression coefficients      530 (table)
Regression, inference about bivariate regression, homoscedasticity      522 539—540
Regression, inference about bivariate regression, independence      541—542
Regression, inference about bivariate regression, inference about $\beta_0$ and $\beta_1$       523—527
Regression, inference about bivariate regression, inference about a new value of Y at       528—529
Regression, inference about bivariate regression, inference about the population regression line      528
Regression, inference about bivariate regression, leverage of       528
Regression, inference about bivariate regression, models for regression, model for nonexperimental research      531—532
Regression, inference about bivariate regression, models for regression, model with fixed-effect predictors      522
Regression, inference about bivariate regression, normality      540—541
Regression, inference about bivariate regression, power calculations      527—528 see
Regression, inference about bivariate regression, robust regression      548
Regression, inference about bivariate regression, standard error of estimate      524—525
Regression, inference about bivariate regression, standard errors for and       525—526
Regression, inference about bivariate regression, standard errors for and derivation      559—560
Regression, inference about multiple regression confidence intervals for the squared multiple correlation coefficient      585—587
Regression, inference about multiple regression, inference about a single coefficient      581—582
Regression, inference about multiple regression, inference about predictions of Y      583—584
Regression, inference about multiple regression, models and assumptions      580
Regression, inference about multiple regression, power      587—591
Regression, inference about multiple regression, testing the hypothesis that all the partial coefficients are zero      581
Regression, interactions      601—606
Regression, interactions between a quantitative and a dichotomous variable      604—606
Regression, interactions between quantitative variables      601—604
Regression, matrix algebra approach to      see Supplementary Materials folder of CD
Regression, measurement error      531—532
Regression, multicollinearity (collinearity)      570—572 596—598
Regression, multicollinearity (collinearity), centering      598
Regression, multicollinearity (collinearity), condition index      597
Regression, multicollinearity (collinearity), tolerance      570—571 597
Regression, multicollinearity (collinearity), variance inflation factor      571 597
Regression, multicollinearity (collinearity), variance proportions      597—598
Regression, multilevel modeling      551
Regression, multiple correlation coefficient R      524 573—578
Regression, multiple regression      562—608
Regression, multiple regression, analysis of covariance as a special case      632—634
Regression, multiple regression, analysis of variance as a special case      615—629 634—637
Regression, multiple regression, explanation versus prediction      593—594
Regression, multiple regression, general linear model      614—637
Regression, multiple regression, inference      see Regression Inference
Regression, multiple regression, interpretation of the coefficients      572—573 593-596
Regression, multiple regression, multiple correlation coefficient      573—580
Regression, multiple regression, partial F tests      582—583
Regression, multiple regression, partial regression coefficients      564
Regression, multiple regression, partial slope coefficients      564
Regression, multiple regression, partitioning of variability      573—577
Regression, multiple regression, specification errors      594—596
Regression, multiple regression, specification errors, including irrelevant variables      595—596
Regression, multiple regression, specification errors, omitting relevant variables      594—595
Regression, multiple regression, standard error of estimate      565—567

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