Research Methods for the Behavioral Sciences, 4th editon ( PDFDrive )

GLOSSARY 427 Simple effect The effect of one factor within one mean deviation) expressed in standard deviation level of another factor. units: Simple random sampling A probabilistic sampling x2x technique in which each person in the popula- tion has an equal chance of being included in the s sample. Standardization of conditions The goal of treat- Simulation study Research in which participants ing all experimental participants in exactly the are fully informed about the nature of the research same way, with the single exception of the manipu- and asked to behave “as if” they were in a social lation itself. setting of interest. States Personality variables that are expected to Simultaneous multiple regression A multiple change within the same person over a short period regression analysis in which all of the predictor of time. variables are simultaneously used to predict the outcome variable. Statistically nonsignificant The conclusion to not reject the null hypothesis, made when the p-value Single-group before-after design Research that is greater than alpha (p . .05). uses a single group of participants who are mea- sured before and after they have had the experi- Statistically significant The conclusion to re- ence of interest. ject the null hypothesis, made when the p-value is smaller than alpha (p , .05). Single-group design Research that uses a single group of participants who are measured after they Statistics Mathematical methods used to sys- have had the experience of interest. tematically organize and analyze data. Single-participant research designs Research in Stem and leaf plot A method of graphically sum- which a single individual, or a small group of indi- marizing raw data such that the original data values viduals, is studied over a period of time. can still be seen. Skewed In relation to a distribution of scores, not Stepwise multiple regression A multiple re- symmetrical. gression analysis in which the predictor variables are entered into the analysis according to the ex- Snowball sampling A nonprobabilistic sampling tent to which they increase the multiple R. technique in which one or more members of a population are located and used to lead the re- Strata Population subgroups used in stratified searchers to other members of the population. sampling. Social desirability A type of reactivity in which re- Stratified sampling A probability sampling tech- search participants present themselves in a positive nique that involves dividing a sample into sub- or socially acceptable way to the researcher. groups (or strata) and then selecting samples from each of these groups. Split-half reliability A measure of internal con- sistency that involves correlating the respondents’ Structural equation analysis A multivariate statisti- scores on one half of the items with their scores on cal procedure that tests whether the actual relation- the other half of the items. ships among a set of collected variables conform to a theoretical prediction about how those variables Spurious relationship A relationship between two should be related. variables that is produced by a common-causal variable. Structured interview An interview that uses fixed- format, self-report questions. Standard deviation (s) A measure of dispersion equal to the square root of the variance. Student’s t A statistic, derived from a theoretical set of sampling distributions that become smaller Standard error See Standard error of the mean. as the degrees of freedom increase, that is used in testing differences between means and in creating Standard error of the mean The theoretical stan- confidence intervals. dard deviation of the means in the sampling distri- bution of the mean. Sum of squares (SS) The sum of the squared mean Standard normal distribution A hypothetical deviations of a variable: g 1 X 2 x 2 2. population distribution of standard scores. Survey A series of self-report measures admin- Standard score A number that represents the dis- istered through either an interview or a written tance of a score from the mean of the variable (the questionnaire. Suspicion check One or more questions asked of participants at the end of research to determine

428 GLOSSARY whether they believed the experimental manipula- Tukey honestly significant difference (HSD) tion or guessed the research hypothesis. test A post hoc means comparison test that con- trols for experimentwise alpha. Systematic error The influence on a measured variable of other conceptual variables that are not Two-sided p-values P-values that consider the like- part of the conceptual variable of interest. lihood that a relationship can occur either in the expected or the unexpected direction. Systematic observation Observation following a fixed set of decisions about which observations are Type 1 error Rejection of the null hypothesis when to be made on which people and in which times it is really true; Type 1 errors occur with probability and places. equal to alpha. Systematic random sampling A probability sam- Type 2 error Failure to reject the null hypothesis pling technique that involves selecting every nth when the null hypothesis is really false. Type 2 er- person from a sampling frame. rors occur with probability equal to beta. T test A statistical test used to determine whether Unbiased estimator A statistic, such as the sample two observed means are statistically different. T is a mean, that does not consistently overestimate or special case of the F statistic. underestimate the population parameter. Tautological A characteristic of a theory or research Univariate statistics Data analysis procedures that hypothesis such that it cannot be dis-confirmed. use one dependent variable. Test-retest reliability The extent to which scores Unrelated-experiments technique An exper- on the same measured variable correlate with each imental technique in which participants are told other on two different measurements given at two that they will be participating in two separate ex- different times. periments. In reality, there is only one experiment, and the experimental manipulation is created in the Theory An integrated set of principles that explains “first experiment,” and the dependent measure is and predicts many, but not all, observed relation- collected in the “second experiment.” ships within a given domain of inquiry. Unstructured interview An interview that uses Think-aloud protocol A free-response measure in free-format, self-report questions. which participants verbalize the thoughts they are having as they complete a task. Values Personal beliefs of an individual. Time sampling In systematic observation, the act of Variable Any attribute that can assume different val- observing individuals for certain amounts of time. ues—for instance, among different people or across different times or places. Time-series designs Longitudinal research designs in which the dependent measure is assessed for Variance (s2) A measure of dispersion equal to the one or more groups more than twice, at regular sum of squares divided by the sample size (N). intervals, both before and after the experience of interest occurs. Within-groups variance A measure of the vari- ability of the dependent variable across the par- Traits Personality variables that are not expected to ticipants within the experimental conditions in vary (or at most to vary only slowly) within people ANOVA. over time. Within-participants (within-subjects) design Trimming A method of deleting outliers from the See Repeated-measures designs. distribution of a variable in which the most ex- treme scores on each end of the distribution are Z score See Standard score. simultaneously deleted. True score The part of a scale score that is not ran- dom error.

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Index A priori means comparisons, 380–383 Banaji, M. R., 259 Blackman, D.,57 A-B-A design, 285–285 Banks, C., 54 Blehar, M. D., 136 Abstracts, 32 Bar charts, 114, 114–115 fig. 6.1, 145 fig. Blind experimenters, 246, 247 Ackerman, P. L., 270 Blocked random assignment, 248, and Acknowledged participant, 131, 132 8.2, 212 Acquiescent responding, 76 Barbaree, H. E., 243 table 12.2, 247, 248 Adair, J. G., 53 Barlow, T. A., 84 Bodenhausen, G. V., 81 Aiken, L. S., 168, 378 Baron, K. S., 59 Boland, F. J., 243 Ainsworth, M. D. S., 136 Baron, R. A., 270 Borgida, E., 3 Alpha: definition of, 147; setting of, 151, Baron, R. M., 262 Bower, G. H., 46 Bartholow, B. D., 83 Bramel, D., 44 152; Type 1 error, 149, 150, 382 Baseline measure, 233 Breitholtz, E., 84 Alternative explanations, 231 Basic research, 11, 12 Brendl, C.M. 20 American Psychological Association Basso, D., 103 Bresnahan, J., 103 Baumrind, D., 53 Brierley, B., 265 (APA): ethical principles, 43, 44 Baxter, P. M., 30 Brown, S. L., 265 guidelines for publication, 293; Beaman, A. L., 81 Brown, T. A., 84 research guidelines, 49; research Beattie, A. E., 73 Brown vs. Board of Education, 3 report format, 11; website, 31, 43; Beattie, M., 103 Buchanan, T. W., 286 table 3.1., table 3.4 Beaubien, J. M., 265 Analysis of Covariance, 379 Before-after research designs, 232, 233 Cacioppo, J. T., 82, 237 Analysis of Variance, See ANOVA Cahill, L., 268 Anderson, C. A., 136, 271 and fig. 12.1 Calculating r: computation of, 360; on Anderson, D. B., 270 Behavioral measures: criterion variable, Animal research: guidelines on, 58, table the mean deviation, 359–360 3.4; reasons for use of, 56, 57 99; nonreactive measures of, 82; Campbell, D. T., 82, 229, 241, 273, 274 Annual Review of Psychology, 30 psychophysiological measures of, Canonical correlation, 387–388 ANOVA summary table: degrees of 82, 83; reliability for, 91, 134; used Carlsmith, J. M., 129, 258, 271 freedom, 191, 192 and figure 10.3; in conceptual variables, 81; table Carr, C., 241, 244 explanation of, 191–193, 193, 194; F 4.5 variables based on, 80 Carvallo, M., 20 statistic, 371 Behavioral research: animals in, 57; Carryover, 195 ANOVA: between-groups/within- archival research in, 135, 136; Cartoon type, 186 groups,171, 192; in experimental everyday science, 4, 5, 6; Case studies, 132, 133 designs, 190, 191; SPSS output, 371, explanation of, 3; goals of, 13–15; Caulkins, R. S., 270 table C.5; one-way between- organizing principles of, 35–38; Cells, 207 participants, 369–370, 371 requirements of, 10; uses of, Census, 110 Applebaum, M. I., 283 3–5, 7, 8 Central limit theorem, 354 Applied research, 11, 12 Bem, D. J., 295 Central tendency: definition of, 118; Archival research, 135 Berkowitz, L., 259, 283 Arithmetic mean, 118; Bernstein, I., 284 calculation of, 349; measures of, Arnold, W. J., 270 Berntson, G. G., 82 118–121, 120 fig. 6.5, 121 Aronson, E., 58, 81, 129, 259, 270 Berscheid, E., 59 Chaiken, S., 81 Artifacts, 242 Bersoff, D. N., 3 Chattopadhyay, A., 20 Ashby, F. G., 245 Beta weights, 168, 169, See Regression Chi-square statistic, 164–167 and table Association, 184, 185 coefficients Beta, 151 9.2 Associative lists, 73 Between-groups variance, 191 Chi-square test for independence, See also Variables Attrition, 278 Between-participants designs, 186 and 362–364 Austin, E. W., 109 fig. 10.1 Chravala, C., 29 Automated experiments, 236 Binomial distribution, 145 fig. 8.2, Clark, D. M., 84 145–147 Clubb, J. M., 109 Baddeley, A. D., 257 Bissonnette, V., 284 Cluster sampling, 112 Bakeman, R., 134 Bivariate regression, 366–368 Coefficient alpha: evaluating reliability, Bivariate statistics, 359 94; multivariate statistics, 383–384 436

INDEX 437 Coefficient of determination, 164 Convenience samples, 113, 114 software packages, 417 table F.1; Cohen, J., 151, 155, 378, 379, 414 Convergent validity, 97–99 define, 4; deleting and retaining, Cohen, P., 378, 379 Converging operations, 20, 68, 69 344–347; entering process, 340–342; Cohen, S., 81 Cook, S. W., 72 event frequencies/duration, 134, College students: advantages/disadvan- Cook, T. D., 241, 274 135; free-format, 73, 74, 75; inter- Coombs, C. H., 67 pretation of, 6–8; 10, 11; transform- tages in using, 256–258 Cooper, J., 82 ing, 346; uses of, 4 Column marginal frequencies, 362 Correlation matrix, 166, 167 table 9.3, Databases, 32–35, 33 fig. 2.1, 34 fig. 2.2 Common sense, 6 David, A. S., 265 Common-causal variables, 170, 171, 172, 167 table 9.4, 361 table C.2 Davis, J. A., 109 Correlational data: path analysis, Deaux, K., 3 175, 176, 185–186, 187 Debriefing, 45 table 3.1, 54–56 Communication of scientific knowledge, 174–175, 176; structural equation Deception, 45 table 3.1, 52–54 analysis, 176, 178 and fig. 9.6 Deductive method, 35 291–292 Correlational research designs: Degrees of freedom (df), 167, 191, 192 Comparison-group before-after designs, advantages/disadvantages of, 177, Demand characteristics, 243–245 178; based on research hypothesis, Demographic variables, 283 278–279 37; and causality, 170–176, 177, 178; Denzin, N. K., 15 Comparison-group designs, 276–277 explanation of, 19, 20; patterns of Department of Health and Human Ser- Complex comparisons, 221, 222, 380 association sample, 160 table 9.1; vices: ethical standards guidelines Computer software packages, 417 strengths and limitations of, 19, 20; website, 44; informed consent variables in, 67 fig. 4.1, 69, See also guidelines, 48; regulations on statistical table F.1 Research designs proposed research, 59, 60, 61 Computers: for data collection, 416–417; Correlations: coefficient, 165 fig. 9.3; Dependent variables, 37, 238, 69, 184, interpreting, 170–172, 173; SPSS 186, 187, 188, 242 data preparation and, 339–345; for output, 361 table c.2; use of, 16, Dermer, M., 59 descriptive statistics, 351–352 17; between variables, 184, 185. Descriptive research, 14–16 See also Conceptual replications, 260, 261 See also Pearson correlation Research designs Conceptual variables: correlational coefficient Descriptive statistics: calculation of, relationship design of, 69, 70; in Counterbalancing, 195, 196 330 table B.1; checking accuracy correlational research design, 69 fig. Cover stories, 243, 244 of, 342–343; computing, 348–353; 4.1; explanation of, 67, 68; as latent Cramer’s statistic, 364 definition of, 118; explanation of, variables, 168; in a Likert scale, 89 Crandall, R., 44 346; measures of, 118–121, 122; and 90 fig. 5.1; operation defini- Criterion validity, 99–100 SPSS output, 353 table B.2; SPSS tions of, 67, 68, table 4.1; reliability Criterion variable, 96, 97, 99 printout of, 119 fig. 6.4 analysis, 386 types of, 81 table 4.5 Critical values of chi-square, 405 Deutsch, M., 72 See also Variables statistical table E Dickerson, C. A., 81 Concurrent validity, 99 Critical values of F, 406–407 statistical Diener, E., 44, 81 Condition means, 197, 198 fig. 10.5, 210 table F Dillman, D. A., 109 fig. 11.3, fig. 11.6, 217, table 11.1, Critical values of r, 403–404 statistical Dillworth, D., 270 219, 221 table D Discriminant validity, 97–99, 99 Conditions, 186, 207 Critical values of t, 402 statistical table C Dispersion: definition of, 118; measures Confidence intervals, 122, 355 Cronbach’s coefficient alpha, 94 of, 121, 350, 353 Confirmatory factor analysis, 388–389 Cross, T., 3 Disproportionate stratified sample, 112 Confound checks, 241, 242, 246–247, 248 Cross-cultural replications, 263 Distracter items, 101 Confounding variables, 230, 231, 239, 240 Crossover interaction, 215 Distributions: binomial, 145 fig. 8.2, Construct validity: assessing, 97, 98; Cross-sectional research designs, 175, 283 146; normal, 119, 120 fig. 6.5; content validity, 96 and table 5.2; Crowder, R. G., 259 patterns and, 118; positive/negative explanation of, 95, 96 table 5.2; face Crowne, D. P., 80 skew, 119, 120 fig. 6.5; sampling, validity, 96; in naturalistic research, Current Contents, 31 146 and fig. 8.3; shapes of, 128, 129; threats to, 228, 229 and Curvilinear relationships, 163, 189 and 120 fig. 6.5; types of, 119 table 12.1 fig. 10.2 Donnerstein, E., 259 Constructive replications, 261, 262 Dougherty, D., 3 Content analysis: archival records coded Data: plural usage of 4, 340–341, 342 Dowden, C., 265 by, 136; coding free-response data Data analysis: by computer software Downing, L. L., 270 as, 74 Durkheim, E., 136 Content validity, 97 programs, 340–341 and fig. B.1, Dushenko, T. W., 53 Contingency tables: calculating 342; deletions from, 344–347; Dyer, M. A., 270 chi-square (χ2), 165 and table 9.2; different approaches to, 391–392; chi-square test for independence, missing data from, 343–344; prepar- Eagly, A. H., 29 362–364; coding of raters, 365 table ing for, 339–346 Ecological validity, 128, 129, 258, 259 C.4; SPSS output, 363 table C.3 Data: analyzing the, 339–340; anonymity Contrast analysis, 380 and confidentiality in, 51, 52; col- Contrast tests, 222 lecting, 339; collecting by computer Contrast weights, 381–382 Contrasts, 380–382 Control conditions, 188

438 INDEX Effect size, 191, 266, 364; explanation of, Experimental script, 236 6.2; shapes of distributions, 120 151, 152; one-way ANOVA and, 371; Experimenter bias, 245, 246 fig. 6.5 statistical Experiments: elements of, 18, 19 Frequency distributions: bar chart and, Experimentwise alpha, 221 116 fig 6.1; definition of, 114; significance and the, 153–155 External validity: explanation of, 229, grouped, 115; raw data sample, 115 Effrein, E. A., 81 table 6.1; types of, 350–351; vari- Eisenberger, N. I., 199 256; generalization, 256, 258; ables in, 116 fig. 6.1 Ekman, P., 252 threats to, 229 table 12.1, 255–267; Freud, S., 6, 26, 135 Eliott, A., 248 understanding, 255, 256 Friesen, W. V., 252 Ellsworth, P. C., 251 Extraneous variables: examples of, 187; Frey, K. S., 79 Empirical statements, 4, 5, 6 explanation of, 172; limited popula- Equivalence, 18, 186, 187 tion designs, 232; matched-group Geda, C. L., 109 Equivalent-forms reliability, 92 design and, 234 and fig. 12.2, 235 Gelfand, H., 293 ERIC, 32 General linear model (GLM), 379–380 Ericsson, K. A., 73 F test, 146 General Social Survey, 109 Eron, L. D., 174 F, 191, 192, 212 General theories, 36 Eta, 374, See also F Face validity, 96 and table 5.2, 97 Generalization: across participants, Ethical research: abuse of power in, 51; Factor analysis: exploratory, 387; rotated 256–258, 262–263; across settings, animals in, 57, 58; APA guidelines, factor matrix, 385 table D.3 258–259; explanation of, 256 45, 45 table 3.1; basic goals of, 43, Factor loading matrix, 384–386 and table Gerard, H. B., 270 44; characteristics of, 56 table 3.3; Gilbert, D. T., 301 cost-benefit analysis of, 59, 60, 61; D.3, 387 Glass, G. V., 265, 266 deception in, 52–53, 54; explanation Factor loadings, 396, 389 Goodness of fit statistic, 389–390 of, 41–43, 43; informed consent, Factor scores, 387 Gosling, S., 81 48–52; participants’ privacy, 51, 52; Factor, 207 Gosman, E. J., 17 protecting participants, 44–47 Factorial experimental designs: ANOVA Gottman, J. M., 134 Event sampling, 135–136 Grand mean, 369–370 Everyday behavior: measured variables summary table, 211–212 and fig. Greenberg, B. S., 136 of, 128, 129 11.4, 213; Greenberg, J., 81 Everyday science, 5, 6 interactions, 213–215, 216; line Greenwald, A. G., 65 Exact replications, 260 chart, 213, 214 and fig. 11.5; main Grouped frequency distribution: Existing research, 28, 29 effects, 209–211, explanation of, 115, 116; histogram, Expected frequencies, 363 214–216 and fig. 11.5; schematic 116, 117 fig.6.2; quantitative Experimental condition, 188 diagram, 209, 210 and fig. 11.2; variable resulting in, 116 Experimental control: before-after re- simple effects, 211; Grove, J. B., 82 search designs, 232, 233 and fig. three-way, 216–217 and table 11.1, Gully, S. M., 265 12.1; confounding variables, 230, 217; two-way design, 208–210 and Gursley, D.M., 84 231; explanation of, 229; extrane- fig. 11.2, 210 fig. 11.3, 214 fig 11.5; Guttman scale, 78–79 and table 4.4 ous variables, 230, 233 fig. 12.1; 2 × 2 factorial design, 240. See also Guttman, L., 78 matched-group designs, 234, 235 Constructive replications Experimental manipulations, See Factors, 386 H, 147 Manipulations Facts, 8–10, 11 Haney, C., 54 Experimental realism, 236, 237 Falender, V. J., 81 Hangnel, S., 15 Experimental research designs: advan- Falsifiable theories, 36 Harackiewicz, J. M., 81 tages/disadvantages of, 198, 199; Fazio, R. H., 81 Hardy, D. J., 265 choosing an, 281–282; comparison Festinger, L., 130 Harmon-Jones, E., 83 of the condition means, 219–222; Fidell, L. S., 383 Harris, R. J., 383 creating equivalence in, 186–187, Field experiments, 259 Hastie, R., 6 193; demonstrating causality, Fisher Least Significant Difference (LSD) Hays, W. L., 380 183–184; factorial designs, 207–219; Test, 382 Heider, F., 6 factors of, 173, 174; hypothesis test- Fisher, R. A., 401, 402, 404, 405 Heilman, M. E., 3 ing in, 190–191; levels in, 188–189, Fiske, S. T., 3, 6, 73 Herrnstein, R. J., 10 190; one-way, 185–192, 186 fig. 10.1, Fixed-format self-report measures, 74–77 Hertwig, R., 54 192–193, and fig. 10.4, 194; repeat- Focus group, 108 Hewstone, M., 281 ed-measures, 193–196, 193 fig. 10.4, Fode, K. L., 245 Hierarchical multiple regression, 378 197; three-level, 189 and fig. 10.2; Ford, T. E., 73 Hillyer, J. E., 103 two-level, 188–189, 192; two-way Forsyth, D. R., 59 Hindsight bias, 7 factorial design, 208 and fig. 11.1 Fowler, K. P., 137 Hinkin, C. H., 265 Experimental research: based on research Fraser, S. C., 81 Histogram, 116, 117 fig. 6.2, 348 hypothesis, 37; explanation of, 18; Free-format self-report measures, 72, 73, History threats, 378 goals of, 256–257; strengths and limi- 74. See also Variables Holmes, D. S., 252 tations of, 19, 194–196; use of vari- Freeman, H. E., 155, 273 Hsee, C. K., 6 ables, 184. See also Research designs Frequencies, 362 Frequency curve: definition of, 116; quantitative variables in, 117 fig.

INDEX 439 Hubbard, M., 55 Keppel, G., 380, 383 Markus, H., 81, 225 Huesman, L. R., 174 Keyword search, 32–35 Marlow, D., 80 Hull, J. G., 46 Kiechel, K. L., 226 Marsden, P., 109 Human Area Relations Files, 109 Kim, H., 81 Matched-group designs, 234, 235 Humphreys, L. 130 Kimmel, A., 54 Mathews, A.M., 84 Kirschbaum, C., 286 Matthewson, G. C., 270 Ickes, W., 258, 284 Knight, L. J., 243 Maturation threats, 298 Ideas: Internet sites, 30, 31; sources of, Knowles, E., 284 McCall, R. B., 283 Ko, S., 81 McCann, I. L., 252 26–29; strategies for developing, Koehler, K. A., 84 McGhee, D. E., 85 28, 29, 30 Kohlberg, L., 36 McGregor, H., 81 Impact, 236, 237 McGuire, W., 27 Incalcaterra, K. A., 265 Langer, E. J., 251 McNally, R. J., 84 Inclusion criteria, 264, 266 Latent variables, 177, 389. See also Con- Mean deviation scores, 219–221, Independent variables, 37, 38, 162–163, 199, 242–243 ceptual variables 380–381, 382 Individual sampling, 135 Latin square designs, 196 Mean deviation, 121 Inductive method, 27, 28 Laws, 35 Means comparisons, 219–221, 380–381, 382 Inferences of causality, See Experimental Lefkowitz, M. M., 174 Means, 118, 388–389 research Leippe, M. R., 6 Measured variables: correlational re- Inferential statistics, 143–145, 354–356 Lepper, M. R., 55 Informed consent, 45 table 3.1, 46–51; Levels, 186, 188, 189, 207 search design of, 69 and fig. 4.1, 70; research goals vs., 50, 51; sample Levin, P. F., 245 creating valid, 100–102; descriptive form, 49 table 3.2 Levine, D., 270 statistics of, 118; distribution, 119; Institutional Review Board (IRB), 59 Lewin, K., 12 effectiveness of, 89–99, 100; every- Interaction, 211 Libman, M., 59 day behavior, 128, 129; explanation Intercept, 375–376 Lieberman, J., 81 of, 67, 69 fig. 4.1; with regression to Internal analysis, 28, 239 Lieberman, M. D., 199 the mean, 279–280, 281; systematic Internal consistency: explanation of, Likert scale: explanation of, 75, 77; observation, 184; types of, 70, 71, 94; interrater reliability in, 95 82, 83; types of behavioral variables Internal validity: confirmation of, 231; ex- reverse-scored on, 346; Rosenberg of, 81, 81. See also Variables planation of, 229; threats to, 229 table self-esteem scale, 75 and table 4.2 Measurement, 67 12.1, 241–249, 276 table 14.1, 277–279, Likert, R., 75 Measures: choosing, 83, 84. See also 284–286. See also Valid research Lilenfeld, S. O., 137 Variables Internet: research literature sources and Limiting conditions, 28, 29 Median, 119, 349–350 the, 30–31 Lincoln, Y. S., 15 Mediating variables, 172, 173 Interrater reliability, 95, 136, 137 Lindsay, R. C. L., 53 Mediator, 172 Interval scale, 71, 72 Linear relationships, 162 Medline, 32 Interviews, 107, 108 Linton, P., 293 Merles, I. M., 84 Intuition: limitations of, 6–8; with obser- Literature search, 30–35 Meta-analysis, 264–265 table 13.1, 266 vational research, 27, 28; without Litowitz, D. L., 270 Milberg, S. J., 73 testing, 6 Loglinear analysis, 379–380 Milgram, S., 44, 46, 237 Isen, A. M., 245 Longitudinal research designs: conduct- Mill, J. S., 184 Items, 74 ing, 173–174, 175; limitation of, 175; Miller, D. T., 245 Item-to-Total Correlations, 94, 95 time-series designs, 281–282 and Miller, D., 81 fig. 14.2, 283 Miller, N. E., 57 Jahoda, M., 72 Lord, C. G., 301 Miller, R. L., 265, 266 Jetten, J., 19, 81 Lytle, B., 179 Mills, J., 55, 58, 237, 270 Johnson, R. D., 270 Milne, A. B., 18, 51 Johnson, S., 293 Macaulay, J., 283 Mitchell, G., 248 Jonas, K., 281 Macrae, C. N., 19, 81, 244 Mixed factorial designs, 218–219 fig. Joshi, A., 265 Madey, S. F., 270 11.6, 220 Joynes, R. L., 103 Madigan, R., 293 Mode, 118, 119, 120, 349–350 Main effects, 209–210, 239 Moderator variables, 262 Kahle, L. R., 46 Manderlink, G., 81 Modin, B., 136 Kappa: explanation of, 95; reliability Manipulated, 186 Money, R., 84 Manipulation checks, 237–238, 239 Morgan, C. D., 73 test, 364–365 Manipulations, 18, 37, 60, 186–189, 190, Mori, D., 81 Kassin, S. M., 225 Morris, K. J., 237 Kavannagh, D. J., 268 236–241, 241, 242 Morris, M., 81 Kelem, R. T., 81 Mann, C., 266 Mortality, 278 Kelly, H. H., 6 Mannarelli, T., 81 Multi-modal, 349–350 Kemper, D., 270 MANOVA, 387–388 Multiple correlation coefficient (R), 168, Kenny, D. A., 262 Margin of error, 122 378 Marginal means, 210

440 INDEX Multiple regression: analysis output Operational definitions, 67, 68 table 4.1; Predictions: as main effects, 215; as from a, 377 table D.1; ANOVA conceptual variables and, 68 table research hypothesis, 37, 38; use of and, 379, 380; explanation of, 4.1, 69. See also Variables correlations, 16, 17 168; goal of, 375–376; independent variables measured, 169 Ordinal scale, 71, 72 Predictive validity, 99 fig. 9.4; testing mediators, 172 Ortmann, C. E., 54 Predictor variables: common-causal Osgood, C. E., 77 Multivariate Analysis of Variance, See Osk, I. G., 84 variables, 171; independent vari- MANOVA Ostrom, T. M., 6 ables, 37–38; multiple regression, Outcome variables, 37, 38, 169 and fig. 163; in regression analysis, 168–169 Multivariate F, 387–388 and fig. 9.4; in relation to outcome Multivariate statistics, 383–384 9.4, 170–174, 175 variables, 172–173, 174; in scatter- Mundane realism, 269 Outliers, 119, 120 fig. 6.5, 345 plots, 163 Murray, C., 10 Oversampling, 112 Prentice, D. A., 245 Murray, H. A., 73 Presser, S., 109 Pairwise comparison, 219–221 Price, L., 242 Naive experimenters, 246 Panel study, See Longitudinal research Primary sources: computer databases, Narrative research reviews, 266–267 32, 44; Internet sites and, 30, 31; Naturalistic observation: descriptive designs research reports, 30, 31 Parameter, 346–347, 354; Parsimonious Priori comparisons, 221 research as, 14 Probability analysis, 145, 146 Naturalistic research, 128, 129, 136 table theories, 36; Participant replications, Probability sampling, 110, 112, 113 262–263 Probability statistics, 143–144, 145 7.1 Parr, J. M., 268 Probability values, See p-values Nettles, M. T., 17 Participant variable, 283; Participants: Process debriefing, 54 Neuberg, S. L., 73 confidentiality of, 42, 52; education- Program evaluation research, 11, Niesta, D., 248 al value for, 55; freedom of choice, 273–274 Nisbett, R. E., 5, 6 47–50, 51; informed consent, 48–50, Projective measure, 73 Nolen-Hoeksema, S., 265 51; psychological impact on, 46, 47 Proportion of agreement, 365 Nominal variables: chi-square statistic, Participant-variable design, 283–284 Proportion of explained variability, 155 Path analysis, 174–175 Proportionate stratified sample, 111, 112 165, 166; explanation of, 70; kappa, Path diagram, 174 and fig. 9.5 Protocol, 236 95. See also Variables Patrick, C. J., 137 Provisional idea theories, 35, 36 Nonlinear relationships, 162, 163, 189, 190 Pearson correlation coefficient: statistical Psychological Bulletin, 30 Nonmological net, 99 assessment by, 163, 164 Psychophysiological measures, 82, 83 Nonprobability samples, 112, 113, 114 Pearson product-moment correlation co- PsycINFO, 32 Nonreactive measures, 82, 244–245 efficient: calculating r, 359–361 and p-values: calculating, 346; comparing Normal distribution, 352–353 and fig. table C.1; explanation of, 16; influ- to Alpha, 144 fig. 8,1, 147, 148; B.2. See also Distributions ences of errors, 90, 91; predictor obtaining, 361, 362; two-sided, Null hypothesis: nonrejection of, 146 fig. variable, 17, 18 table 1.2; samplings 148, 149 8.4; rejecting the, 147, 150 and fig. distributions, 146 8.4; statistically significant/nonsig- Pearson’s r, See Calculating r Qualitative data, 128, 133, 134 nificant, 148; testing by, 146–147; Peer review, 294–295 Qualitative research, 14, 15, 132 Type 1 and Type 2 errors, 149–151, Pennebaker, J. W., 290 Quantitative data, 128 150 fig. 8.4, 151 Percentile rank, 353 Quantitative research, 14, 15 Nygren, T. D., 245 Personality variables, 284 Quantitative variables: correlation coef- Nunnally, J. C. , 70 Peterson, R. A., 84 Petty, R. E., 237 ficient, 164; explanation of, 70, 71; Objective procedures, 8 Pham, M. T., 222 patterns of association, 162 and fig. Observational research: coding sheet Phi, 364 9.2, 169. See also Variables Piaget, J., 26, 36, 132 Quasi-experimental research designs: sample, 131 fig. 7.1; explanation of, Piliavin, I. M., 47 comparison-group, 276–277; com- 129; participant/ observer in, 129, Piliavin, J. A., 47 parison-group-before-after design, 130; participation and acknowl- Pilot testing, 100, 240, 241 278–279; explanation of, 274; par- edgment, 131 table 7.1; systematic Placebo effects, 242, 243 ticipant-variable design, 283, 284; coding methods, 148; types of, Planned comparisons, 221 single-group before-after design, 128–129, 132; use of inductive Pliner, P., 81 277, 278; single-group design, 274, method, 27, 28 Plous, S., 57 275 and fig. 14.1, 258; single- Observed frequencies, 362 Pomerantz, E. M., 131, 134–135 participant designs, 284–286, and Observed means patterns, 213–214, 215 Popper, K. R., 36 fig. 14.3; summary of, fig. 14.1; Observers: acknowledge/unacknowl- Population, 110, 112, 113 threats to validity, 276 table 14.1 edged, 130–132 Post hoc comparisons, 221 Questionnaires: advantages and disad- O’Connor, P. J., 136 Post hoc means comparisons, 382–383 vantages of, 109–109; mailed, 113. One-shot case studies, 274 Postexperimental interviews, 55, See also Fixed-format self-report One-way experimental designs, 185–196, 226, 227 measures 192–193 and fig. 10.4. See also Pratfall, 7 Experimental research designs

INDEX 441 Random assignment to conditions, 186 308–309 fig. A.2; conducting, 12, distribution of the mean, 334, 335; fig. 10.1, 187, 208 and fig. 11.1, 209, 13; critical thinking and, 12; discus- probability, 122; proportionate and 247–248 and table 12.2, 396 sion of, 306–307, 309; evaluating, disproportionate, 111–112; types of, 12; explanation of, 10–11; footnotes 110–113; types of strategies, 135 Random error: explanation of, 89, and author notes of, 309; honesty Sansone, C., 81 measured variable by, 90 and fig. in, 59–61; “hourglass shape,” 298 Saxe, L., 3 5.1; measurement with, 143–144; fig A.1; introduction of, 302–303, Scales: advantage of fixed-format, 74; self-canceling by, 89, 90 304; method, 303–304; presentation explanation of, 71, 74; fixed-format of results in, 197–198, 199, and fig. self-report measure, 74; Likert, Random numbers, 396 10.5; references of, 304; results of, 75–77; of measurement, 72–73 Random sample, 396 304–305; sample of, 314–337; sam- Scaling, 69 Random sampling, 111 plings procedures in, 114; sections Scatterplot, 161, 367 fig. C.1 Range, 121, 350 of, 295–296; tables and figures of, Schachter, S., 130 Ransberger, V. M., 270 309; tips on writing, 310–311; title Scheffé Means Comparison Test, 382 Ratio scales, 71, 72 page of the, 299 Scherer, K. R., 252 Raw data, 114, 115 Research results: reporting, 299–309, Schlenker, B. R., 59 Reactivity, 79, 80 310; in scientific journals, 292–294, Schmitt, D. P., 265 Reciprocal causation, 170 295 Schuman, H., 109 Reed, J. G., 30 Research: choosing a measure, 93, 94; Schwartz, J. L. K., 85 Regression coefficients: explanation of, communicating scientific knowl- Schwartz, R. D., 82 edge, 291–309, 310; informed con- Schwarz, N., 109 257; independent variables, 356; sent vs. goals, 50–51; professional Scientific fraud, 60–63 relationship of predictor variables, collaboration, 201; publication pro- Scientific knowledge: accumulation of, 168, 169 fig. 9.4, 170 cess, 292–294, 295; understanding 6, 37; communication of, 291–292 Regression equation, 366, 367 fig. C.1 methods of, 12, 13 Scientific method, 8 Regression line, 161, 367, 369 Researchers: communication between, Scientific notation, 351 Regression to the mean, 279–281 291–292 Scientific research reports, 10–12 Reiss, S., 84 Response rate, 109, 113 Scientific research: ethics in, 42–44; Reliability analysis, 383, 384 table D.2 Restriction of range, 164, 165 fig. 9.3 values vs. facts, 8–10, 9 table 1.1, 11 Reliability: approaches to assessing, Retesting effects, 92, 278 Sears, D. O., 232, 258 81 table 5.1; basis of, 93, 94; code Reversal design, 285 Sechrest, L., 92 analysis, 134–135; comparing valid- Reverse causation, 170–171. See also Secondary sources, 30–31 ity and, 101–102; explanation of, Longitudinal research designs Selection threats, 277 91; internal consistency of, 94; test- Review papers, 264 Self-monitoring scale, 97–99 retest procedures in, 92 Richardson, D., 55 Self-promotion, 79–80 Repeated-measures designs: advantages/ Riecken, H. W., 130 Self-report measures: fixed-format, disadvantages of, 194–195, 196–197; Robinson, J. P., 102 74–78, 79; free-format, 72–74. See explanation of, 186; in factorial re- Rodin, J., 47 also Variables search, 218–219 fig 11.6, 220; one- Rokeach, M., 133 Selltiz, C., 72 way, 195 fig.10.4; reversal design, Rosenberg self-esteem scale, 75 and Semantic differential, 79 and table 4.3 285; when to use, 196–187 table 4.2, 76–77, 93, 94, 96, 284 Shaver, P. R., 102 Replications: explanation of, 8, 260, 261; table D.2 Shaw, P., 265 types of, 261–263 Rosenberg, M., 75 Shear, D. M., 84 Representative sample, 119 Rosenhan, D. L., 130 Sherer, K. R., 252 Research designs: approaches to, 13, Rosenthal, R., 43, 153, 245, 266, 380 Sholomskas, D. E., 84 14; characteristics of, 20 table 1.3; Rosnow, R. L., 153, 380 Shrout, P. E., 147 recognizing and controlling vari- Ross, L., 5, 55 Sigall, H., 237 ables in, 231–235, 236; selection of, Rossi, P. H., 155, 273 Sigelman, J., 83 19–20; types of, 19–20 Rounding, 348 Significance level, See Alpha Research hypothesis: explanation of, 37; Row marginal frequencies, 362 Simo, M., 270 explanatory measurement of, 37; Roy, D. F., 129, 131 Simon, H. A., 73 formalizing ideas, 35–36; 37–38; Rubin, Z., 53 Simonton, D. K., 137 hypothesis-testing flow chart, 144 Ruble, D. N., 36 Simple effect, 211 fig. 8.1; hypothesis testing proce- Running head, 299 Simple random sampling, 111 dure, 149, 150 and fig. 8.4; testing Simulation studies, 53–54 the, 70; use of variables and, 70 Sample size, 155–156, 167 Simultaneous multiple regression, 378 Research participants: types of threats Sample, 110 Single-group before-after design, to, 44–46 Sampling distribution of the mean, 277–278 Research programs: interpreting re- Single-group design, 274–276, 275 search literature, 267; need for, 354–355 fig. 14.1, 276 table 14.1 263–264; results of, 264–267 Sampling frame, 111, 113 Single-participant designs, 284–295 and Research reports: abstract of, 299–301; Sampling: bias, 112–113; definition of, fig 14.3, 285 APA format, 294, 298–299, 310; APA format checklist, 297 table A.1, 308– 110; distribution, 146 and fig. 8.3; 309 fig. A.1; APA reference format,

442 INDEX Skewed, 119 Statistics: explanation of, 346–347 Type 2 errors, 150–152, 153. See also Skika, L., 179 Stem and leaf plot, 116, 118 and fig. 6.3 Null hypothesis Slaby, R. G., 78, 79 Stephen, A. T., 222 Smith, A. P., 81 Stepwise multiple regression, 378 Tyrrell, D. A. J., 81 Smith, M. L., 265. 266 Sternberg, R. J., 295 Smith, S. S., 54, 55 Stevens, J., 383 U.S. Census Bureau, 109 Smith, T. W., 109 Strack, F., 109 Unacknowledged participant, 130–131 Snowball sampling, 113 Strata, 111 Unbiased estimator, 354 Snyder, M., 98 Stratified sampling, 111, 112 Univariate statistics, 383 Social desirability, 79 Stroebe, W., 281 Unrelated-experiments technique, 244 Social Science Citation Index (SSCI), 32 Structural equation analysis, 176–177, Unstructured interviews, 108 Society for Research in Child Develop- 178 fig. 9.6; 388–389, 390 fig. D.1 Valid research: definition of, 228; threats ment (SRCD), 49 Structured interviews, 108 to, 228, 229 and table 12.1. See also Solomon, S., 81 Student’s t, 355 Construct validity; External validity; Sperry, R. W., 133 Suci, G. J., 77 Internal validity; Null hypothesis Split-half reliability, 94 Sullivan, G. L., 136 SPSS, 166, 167 table 9.3, 340, 341 fig. B.1 Sullivan, L. A., 73 Validity: comparing reliability and, 101– Spurious relationship, 171–172 Sum of squares, 121, 351, 367–368 102; types of, 95–99, 100 table 5.2 Standard deviation, 118, 121, 350, 354 Summation notation, 347–348 Standard error of the mean, 354–355 Surveys: “current concerns,”15 fig. 1.1; Values, 8–10, 11 Standard normal distribution, 353 fig. Variables: analysis of, 190; in correla- explanation of, 14; goal of, 107; B.2, 298–399 statistical table B procedures for, 107, 108; sample tional research, 173–177; deleting, Standard score, 352 data, 115 table 6.2; use of existing, 344–346; distribution of the, 118; Standardization of conditions, 235–236 109. See also Self-report measures explanation of, 16; graph of, 161 Standardized regression coefficients, Suspicion check, 55 fig. 9.1; histogram, 116, 117 fig. 6.2; Systematic error, 89–90 and fig. 5.1 relationship between, 161; relation- 376. See also Regression coefficients Systematic observation, 134 ships of, 71; types of, 37–38; valida- Stangor, C., 36, 73, 241, 244, 281 Systematic random sampling, 111 tions of, 89–100 Stanley, J. C., 229, 241, 274 Variance, 118, 121, 190–191, 352 States, 93. See also Conceptual variables T test, 192 Vaughan, T. R., 43 Statistical analysis: choosing the, Tabachnick, B. G., 383 Tannenbaum, P. H., 77 Walder, L. O., 174 391–392 and fig. D.2; conducting, Tassinary, L. G., 82 Walker, C., 203 346–347 Tautological theories, 37 Wall, S., 136 Statistical conclusion validity: threats to, Teachman, B., 84 Waters, E., 136 228, 229 table 12.1 Temporal priority, 185 Websites: American Psychological Statistical notation, 347–348 Test-retest reliability, 91–92 Statistical Package for the Social Sci- Thematic Apperception Test (TAT), 73 Association (APA), 31; computer ences, See SPSS Theories, 35–37 software packages, 417 statistical Statistical power, 151–152, 414 statistical Thesaurus, 32 table F.1 table G Thibodeau, R., 81 Webb, E. J., 79 Statistical procedures: analysis of cova- Think-Aloud Protocols, 73. See also Weick, K. E., 134 riance, 379; Analysis of Variance Wells, G. L., 82, 135 (ANOVA), 190–192, 193; canonical Variables West, S. G., 6 correlation, 387–388; guide to the Thoeny, A. R., 17 Westling, B. E., 84 use of, 339–356; inferential statistics, Thomas, G., 57 Whyte, W. F., 131, 132 145; meta-analysis, 264–267 and Time sampling, 135 Williams, K. D., 199 table 13.1; multiple regression, 375– Time-series designs, 281–282 and fig. Wilson, T. D., 6 376; structural equation analysis, Wisneski, D., 179 175, 177 and fig. 9.6, 178; univariate 14.2, 283 Within-groups variance, 191 statistics, 383 Traits, 92. See also Conceptual variables Within-participants design, 193, 194 Statistical significance: effect size and Tranel, D., 286 Woods, S. W., 84 the, 153–155 Traugott, M. W., 109 Word, C. O., 82 Statistical tables: computer software pro- Trimming, 345 Wrightsman, L. S., 102 grams, 417 table F.1; critical values True score, 93 of chi-square, 405 table E; critical Tukey Honestly Significant Difference χ2, See Chi–square values of F, 405–413, critical values of r, 403–404 table D; critical values (HSD) Test, 382–383 Yates, F., 401, 402, 404, 405 of t, 402; distribution of z, 398–401 Tukey, J. W., 345 Young, R. D., 46 table B; random numbers, 396–397 Twenge, J. M., 265 Young, R. M., 268 table A; table A; statistical power, Two-sided p-values, 148–149 2 × 2 414 table G Z score, 352–353 and fig. B.2 Statistically (non)significant, See Null experimental design, See Factorial Zajonc, R. B., 29, 252 hypothesis experimental designs Zanna, M. P., 82 Type 1 errors, 150–151, 152–153, 219, Zedeck, S., 380, 383 271. See also Null hypothesis Zimbardo, P. G., 34, 270

FIGURE D.2 Choosing a Statistical Analysis IV = independent variable BEGIN No Descriptive DV = dependent variable Is there more than statistics one variable? No Descriptive statistics Yes No Are there an IV and a DV? Yes Yes Is there more than one IV? Are all of the IVs nominal? Is the IV nominal? Yes No No Yes Is the DV nominal? Multiple Pearson Is the DV nominal? Yes No regression correlation No Yes Loglinear Factorial One-way Chi analysis ANOVA ANOVA square