Essentials+of+Business+Research+Method.pdf

Essentials of Business Research Methods Joseph F. Hair, Jr., Ph.D., author; Leyh Publishing Index by Melody Englund, Songbird Indexing Services

© 2002 Leyh Publishing

NOTE: Page numbers in italics refer to Exhibit and Research in Action insets. absolute zero points, 156 abstracts, 330–31 acceptable error, 216–17 accuracy. See validity Acme Rent-A-Car, 17 advantageousness (of ideas), 95 advertising

campaigns, 97, 135 content analysis, 126 measuring effect of, 176, 285, 288–90 sales, effect on, 51–52, 55, 288–91 sales predictions from, 294–95, 309 targeting identified groups, 368

agglomeration schedule tables, 375 AID (Automatic Interaction Detection), 15 aided questions, 196–97 alpha reliability coefficients, 170 alternative forms reliability, 168–69 alternative hypotheses, 251, 252–53, 258–59, 264, 291,

297 American Business Information, 75, 132 analytical phase (business research process), 50–51,

76 analytical techniques. See cluster analysis; factor

analysis ANOVA (Analysis of Variance). See also factor analysis;

statistical techniques assumptions, 268 cluster analysis, 374–76, 395–96 customer assessment project, 266–73 factor scores, 401 F-test, 267, 271, 292, 299 hypothesis testing, 260, 266–73 metric scales, 254, 257 null hypotheses, 266–68, 270–73 one-way, 266–69, 377 overview, 266–70, 274–75, 288–89 perception concept, 316 regression analysis, 292–93, 300, 302–3, 305–6,

314–17 Scheffé test, 268–70 in SPSS, 267–68, 272, 375 statistical significance, 268 tables, 267, 292–93, 302–3, 305–6, 314–16, 374–76 two-way, 270–73

Appendices, 330, 332, 351. See also research proposals and reports

applied business research, 6–7, 51, 125, 327–30. See also basic business research

arbitrary zero points, 154 assessments

of concepts, 167–74 demand effect, 111–12 proposal outlining, 29–30 reliability, 167–72 SWOT, 26 validity, 167–68, 172–74

associative analysis, 278. See also correlation analysis; regression analysis

assumptions. See also hypotheses for ANOVA follow-up tests, 268 in discriminant analysis, 382 for estimating the mean, 217 in factor analysis, 359, 360 in the FICO credit measures, 357 hypotheses as, 251 incorrect, 87–88, 98–99 of linear relationship as efficient, 279 for parametric and nonparametric procedures, 257 for Pearson correlation coefficients, 282 for regression analysis, 290, 294, 313, 322 for samples, 61 for self-completed questionnaires, 130 testing, 317–20 in t-tests, 265

ATOCS (criteria evaluation), 95–96 attitudes/opinions of respondents, 127, 146–47, 154,

156–64, 187 audience considerations, 324–25, 332–33 Automatic Interaction Detection (AID), 15 average summated scores, 230 balanced scales, 165–66. See also scales bar charts, 43, 84, 234–35, 286, 331, 344–45 Baruch College-Harris poll, 75 basic business research. See also research proposals

and reports designs, 57–65, 71–76, 77 experiments, 65–71 overview, 6–7 process, 50–51, 76–77 sampling, 206–18

behavioral learning theory, 52

© 2002 Leyh Publishing of 21

beta coefficients, 294, 295–96, 298–300, 303, 305, 310, 315, 346

between-subjects experimental design, 67, 68, 69–70, 268–69, 271

bisexual population, 127 bivariate statistical techniques. See also multivariate

statistical techniques; statistical techniques; univariate statistical techniques

hypothesis testing, 260–64 multiple regression compared to bivariate regression,

310 multivariate techniques compared to, 296, 299, 304 null hypotheses, 265–66, 291–92 overview, 254, 258, 274–75, 287–88 Pearson correlations, 282–83, 304 regression analysis, 288–94 residuals analysis, 317, 322 Spearman's rank order correlation, 154, 285–87,

288–89 in SPSS, 263 statistical significance, 258–64, 283 t-tests, 264–65

blocking variables, 69–70. See also variables box and whiskers plots, 243–45, 247 branching questions, 194 budgets (research), 27, 31, 328 build-up approach, 371. See also cluster analysis Burger King/McDonalds case study, 264, 356–58,

377–81. See also case studies Burke Marketing Research, 171 business ethics

causal research, 108–12 of decision maker, 112–13, 116 overview, 103–6, 110, 115–17 of participants, 113–15 of researcher, 71, 106–12, 123–24 unethical actions, 115–16

business research, applied, 6–7, 51, 125, 327–30 business research, basic. See basic business research business research designs. See also causal research;

descriptive research; exploratory research basic, 57–65, 71–76, 77 overview, 122 planning and implementing, 25–31, 150–51 science, 54–57, 65–67, 77 Secure Customer Index, 171 service quality model, 159

business research overview, 5–9, 9–17, 20, 50–57. See also ethics; researcher-client relationship; researcher-participant relationship

business-to-business survey proposal, 28 CAB process

creativity, 82–83, 92–95 evaluation and choice, 82–83, 90, 95–96, 98, 99 implementation, 83, 96–98 overview, 82, 98 research questions, 82, 85–91, 98–99

CAQADS (Computer Assisted Qualitative Data Analysis Software), 125

Carlson, Chester, 92 case studies. See also customer assessment project;

employee study; Gino's Ristorante; Samouel's Restaurant

Burger King/McDonalds, 264, 356–58, 377–81 Chrysler, 58, 136 Hito VCR, 149–50 Pontiac, 136 R.J. Reynolds, 106–7 soft drink consumption, 237–38, 241, 251, 252–53,

264, 285 Starbucks/Maxwell House, 254, 257

Casewise Diagnostics tables, 318, 320–21 Casino Rewards program, 16 casual Friday example, 56 categorical scales/variables, 162, 266, 273. See also

nominal scales; ordinal scales; variables categories, collapsing or combining, 229 category labels for scales, 166–67 causal research. See also questionnaire design

process; surveys covariance, 64, 279–81, 358–59 ethics, 108–12 experiments, 64–67 hypotheses in, 64–65 hypotheses testing, 64–65 literary reviews, 58 overview, 57, 64–65 selecting, 71, 77 variables, 64–65, 77, 278, 290

census, 14, 75, 206–7, 258 central tendency, 57, 237–40, 246–47, 254, 257 centroid, 371, 384–85 Chance (Web site), 255 characteristics. See variables charts and graphs. See also SPSS software; tables

bar charts, 43, 84, 234–35, 286, 331, 344–45 in Excel, 44–46 factorial experimental results, 68–69 overview, 229–34, 235–37, 240–46, 329–30, 331 pie charts, 235–37, 329–30


charts and graphs (continued) research level related to risk, 84 residuals analysis, 317–21 scatter diagrams, 280–81 seasonality trends, 63 sources of off-the-shelf, 75 in SPSS, 41–43, 236, 244

Children's Health Foundation Research Proposal, 26–27

Chi-square tests, 254, 257, 260–64, 274–75 Chrysler, 136 Chrysler case study, 58. See also case studies Circuit City, 96–97 classification

error, 380–81 questions, 33–34, 153, 185, 187, 189, 192, 194–95 results, 382, 396–97, 404 variables, 33–34, 36, 379–82

client-researcher relationship. See decision making; researcher-client relationship

close-ended question, 188–89 cluster analysis

ANOVA, 395–96 with discriminant analysis, 390–98, 404–5, 408 error in, 373, 391–92 group mean, 372–75, 377 null hypotheses, 373, 375, 391, 396 overview, 288–89, 317, 356, 368–77, 386 in SPSS, 375, 377, 396, 398 variance, 372, 374

cluster sampling, 209, 213–15, 219 Coca-Cola, 6 coding, 228 coefficient of determination (r2), 282, 291, 293, 295. See

also R2 coefficients. See also R2

beta, 294, 295–96, 298–300, 303, 305, 310, 315, 346 correlation, 279–85, 289, 290, 301–2, 309 Cronbach's alpha, 170–71 determination, 282, 295, 310 error, 373–75, 391–92, 396 Pearson correlations, 281–83 regression, 292–96, 299, 301, 303, 306, 308, 310 standardized, 293–94, 298, 300, 303, 346, 349, 383,

385, 401–2 tables, 208, 293–94, 298–99, 303, 315, 346 unstandardized, 293–94, 298, 300, 303

coercion vs. incentives, 108–9 collinearity, 303, 308, 318. See also multicollinearity common factor analysis, 358–60

common variance, 358–60 communality, 362–63 communication. See also Internet; researcher-client

relationship; research proposals and reports decision maker obligations, 112–13 group moderators, 58–59, 74, 77, 134 overview, 89–90, 324–25, 336 presentation techniques, 333–35 of research methods, 324–25, 343–44 technical writing guidelines, 326

comparative scales. See nonmetric scales competition theory, 51, 53 completion rates of surveys, 210 composite factors, 357–58 composite variables, 229. See also variables computer dialogue, 138–39 concepts/constructs. See also satisfaction, concept of;

scales; statistics; validity assessing, 167–74 causality, 64–65 conceptual background, 331, 341–43 customer churn/share, 12 evaluation and choice, 95–98, 99, 137–38, 140–41 goodness of fit, 261 levels of measurement, 149–56, 164–67 measurement of, 146–50, 156–64, 170 measures of constructs, 175 minivan, 58 multi-item, 171 overview, 145, 176, 187 proxy variables, 148, 149–50 questionnaire design, 183, 187, 199, 202 relationship marketing, 12, 21, 33, 36 relationships between variables, 278–79 research questions for measuring, 33–36 Samouel's Restaurant image, 174–76 scales development, 174–76 smokeless cigarette, 106–7 validity of, 172–73, 200 variables as components, 174, 183

Conclusions, Recommendations and, 330, 332, 349–51. See also research proposals and reports

concurrent validity, 173 confidence interval, 211, 268–69 confidence level, 217, 267–68 confirmatory research, 77. See also empirical tests conflicts of interest, 104, 105–6, 126–27. See also

ethics conjoint analysis, 288–89 consistency, 96. See also reliability


constant sum scales, 157, 163. See also nonmetric scales

constructs, 175. See also concepts/constructs consultants, 18–20, 327 content analysis, 124–26 content validity, 172–73 context effect, 197–98 contingency tables, 261–62 continuous scales. See metric scales continuous variables, 151, 177, 264–65, 266. See also

variables control (experiments), 64 convenience sampling, 209, 215 convergent validity, 172–73 correlation analysis

coefficients, 279–85, 289, 290, 301–2, 309 in Excel, 284 of factors, 360–61 null hypotheses, 281–83 overview, 278, 281–84, 288–89, 309 Pearson correlations, 281–84, 304, 307, 309, 346 scales and, 285–87 Spearman's rank order correlation, 154, 285–87,

288–89 statistical significance, 279–80, 284–85 structure matrix, 383–84 tables, 283, 285–86, 304

covariance, 64, 279–81, 280–81, 358–59 creativity

barriers to, 92 CAB process, 82–83, 92–95 enhancing, 93–95, 99 implementation alternatives, 97 learning, 94 oral reports, 333

credit scores, 357 crisis management, 86, 87 criterion validity, 173–74 criterion variables, 173–74. See also variables Cronbach's alpha reliability coefficients, 170–71 cross-classifications, 61 cross-sectional data/studies, 61–63, 210. See also

questionnaires; surveys cross-tabulation, 260–63, 330, 331 curvilinear relationship (variables), 278–79. See also

variables, about customer assessment project. See also Samouel's

Restaurant; satisfaction, concept of analysis of, in SPSS, 34–37 analysis with SPSS, 383, 392

ANOVA, 266–73 bivariate analysis, 260–64, 285–87, 291–94 customer database, 34–36 overview, 342–43, 343–44 questionnaire, 184–85 recommendations and conclusions, 350–51 results, 347–49 t-test of two means, 265–66 univariate hypothesis testing, 258–60

customer churn, 12 customer segments, 16 customer share, 12, 16 customer surveys

factor analysis, 34–36

DASL (Data and Store Library), 255 data. See also data analysis; data collection; data

preparation; data types Data and Store Library (DASL), 255 data mining, 14–15 data sets, 71 entry/editing, 38–39, 225, 228, 243 overview, 56, 71

data analysis. See also ANOVA (Analysis of Variance); SPSS software; statistical techniques; variables, about

central tendency, 237–39, 240, 246 content analysis, 124–26 correlation analysis, 281–84 data transformation, 38, 229 documentation of, 330 of interactions, 15, 68, 69, 77, 278–79 of nominal scales, 152 price sensitivity analysis, 97 proposal for, 30–31 sample statistics vs. population parameters, 252–53,

257–58, 274 statistical techniques overview, 287–88 XLSTAT, 250

data collection. See also data; interviews; questionnaire design process; questionnaires; surveys

data mining, 14–15, 15–16 GPS, 15, 17 literature reviews, 58 observation, 122–26, 139–40 overview, 122, 139–41, 250 perceptual data, 30, 34–35, 184–85, 197–98, 214–15 technology and, 122 technology for, 122


data preparation. See also data charts and graphs, 234–36 coding and data entry, 228 data editing, 225 dispersion, 239–41, 241–43, 246–47, 254 frequency distribution, 229–34 missing data, 41, 225, 227–28 normal distribution, 236–39, 240, 257 overview, 225, 245–47 in SPSS, 232, 234, 236, 240, 243, 244, 246 standardizing data, 370 stem and leaf displays, 245, 246, 247 transformation of data, 38, 229

data reduction techniques. See cluster analysis; factor analysis

data types. See also perceptual data; qualitative data; quantitative data

objective, 71–72 observational, 122–26 off-the-shelf, 14, 75, 129 primary, 72, 77–78 projective, 59–60 researcher dependent, 72 secondary, 72–74, 75, 77–78 subjective, 72

data warehouses, 14, 83, 107 debriefing, 111–12, 114–15 Decision Analyst, Inc., 129 decision maker-researcher relationship. See

researcher-client relationship decision making. See also CAB process; evaluations;

exploratory research ethical obligations, 112–13, 116, 117 expert systems, 6, 52, 83, 86, 98 normative decision rules, 52 overview, 50, 52–55, 82–85, 87, 90–91, 98 seasonality trends, 60 strategic/tactical decisions, 7–8, 115

Decision Support System (DSS), 85–86, 327 Delicato, Inc., 13 Delphi interview technique, 18, 59. See also exploratory

research; interviews demand effects/characteristics, 68, 95–98, 109, 111–12,

114–15, 193–94 demographic information

cluster analysis, 370, 372, 376 for panel surveys, 127 profiling, 343, 376 questions about, 194 RAGE matrix, 128

sources of, 10, 75, 255 dendogram, 371 Department of Commerce, 75 dependence statistical techniques. See also ANOVA;

discriminant analysis conjoint analysis, 288–89 logistic regression, 288–89 multiple regression analysis, 288–89, 294–302,

313–22, 345–46, 397–402 overview, 287–89 Spearman's rank order correlation, 154, 285–87,

288–89 dependent variables. See also regression analysis;

variables ANOVA/MANOVA, 270–71, 273 ANOVA testing, 266–73 criterion variables, 173–74 electric shock trial, 111 measuring, 287–88 multiple, 273 overview, 67 plotting, 280–81 treatment levels and, 69–70

depth interviews, 59, 74, 77, 137–38, 141. See also exploratory research; interviews

descriptive research. See also descriptive statistics; group mean; mean; mode; sampling, about; standard deviation; surveys

central tendency, 57, 237–40, 246–47, 254, 257 cross-sectional studies, 61–62 designs, 57, 60–64 ethics, 110 frequency distribution, 229–37, 260–61, 318–19, 329 hypotheses testing, 60, 62, 76, 250 longitudinal studies, 62–63, 128 overview, 57, 60–61, 77 panel surveys, 63, 126, 127, 129, 201, 208 quantitative data, 74, 76, 78, 122, 127, 140 research questions, 71 structured interviews, 60, 133, 141 tables, 268–69, 271–72, 291, 297–98, 346, 364, 366,

376 theory of competition, 51, 53 variable selection, 77

descriptive statistics. See also descriptive research; standard deviation

correlations, 61 factor analysis, 366 frequency counts, 57, 61 group means, 61


descriptive statistics (continued) overview, 57, 61, 235–36, 250, 329 rankings, 61 in research reports, 329, 330, 346, 351 in SPSS, 232, 236, 240, 243, 244, 246, 262, 263,

268, 272 table display of, 268–69, 271–72, 283, 291, 297–98,

307, 364–66, 400 diagnostics tables, 319, 321 diagnostic tools. See data preparation directional hypotheses, 252 direction (of variables), 279. See also variables disclosure, 110–11 discovery, 54–56, 76 discrete variables/scales, 151, 177. See also nominal

scales; ordinal scales; variables discriminant analysis

assumptions, 382 Burger King/McDonalds case study, 377–81 with cluster analysis, 390–98, 404–5, 408 discriminant function, 378–85, 386 group mean, 377–78, 382–85, 398 missing data options, 228 multiple discriminant analysis, 377–82 overview, 288–89, 356, 368, 382–85, 386 scores (Z scores), 378–80 in SPSS, 383, 392 stepwise discriminant analysis, 385 tables, 382–83 validity, 168, 172–73, 360 variance, 379, 382 weights, 379–82 Z-scores, 378–80

dispersion, 239–41, 242–43, 246–47, 254. See also variances

disproportionately stratified sampling, 212–13 divisive approach, 371 Documents Center at University of Michigan, 255 double-barreled question, 197 DoubleClick, 108 DSS (Decision Support System), 85–86, 327 dummy tables, 87, 89–90, 328, 336 dummy variables, 228, 290, 313–17, 322. See also

variables, about EBSCO, 75 Edison, Thomas, 92 EESEE (Electronic Encyclopedia of Statistical Exercises

and Examples), 255

EFA (Exploratory Factor Analysis), 358–62, 366, 367, 399, 400

eigenvalue, 361–63, 367 electric shock trial, 111, 111 Electronic Encyclopedia of Statistical Exercises and

Examples (EESEE), 255 electronic surveys, 131–32, 139, 194, 200–201 elements, 206 email surveys, 13, 123, 131–32, 194, 200–201, 208.

See also Internet; surveys emotional inhibitions, 92 empirical tests, 51, 56, 77, 83, 251 employee study. See also Samouel's Restaurant;

satisfaction, concept of analysis of, in SPSS, 32–34 cross-tabulation of, 260–63 data preparation, 230–34, 241, 242, 243, 244–46 dispersion example, 242–43 histogram of, 318–19 hypotheses, 341–42, 344–46 overview, 343 Pearson bivariate correlation example, 282–84 questionnaire, 152–53, 187 recommendations and conclusions, 349 research proposal, 32–34 results, 344–47

error (in research). See also standard error acceptable, 216–17 classification, 380–81 cluster analysis, 373, 391–92 data entry, 243 error coefficients, 373–75, 391–92, 396 error terms, 290, 318–19, 320 least squares method, 294 measurement error, 167–68 random, 251 residual analysis, 317–19, 321 sampling, 61, 251, 296 standard, in coefficient tables, 293, 300, 302, 303,

306, 308, 315 standard error of the mean, 241 Type I and Type II error, 253 variance, 317, 322, 359–60, 362, 372, 374

eSampling, 208 ethics. See business ethics ethics checklist, 116 ethnographic research, 10, 124 Euclidean distance, 370


evaluations. See also decision making of alternatives, 98 ATOCS, 96 CAB process, 82–83, 90, 95–96, 98, 99 evaluative criteria, 95–96, 344 of research questions, 90, 95–96, 99, 185–86, 344 with test markets, 65–66, 67, 96–97

Excel (Microsoft), 44–46, 239, 242, 250, 284 execution phase (business research process), 50, 60,

72, 76, 77–78 Executive Summary, 326, 327–28, 330, 340. See also

research proposals and reports expediency, 87, 92 experimental design. See also business research

designs; causal research; factor analysis between-subjects, 67, 68, 69–70, 268–69, 271 overview, 64–71, 77 participant protection, 108–12 variables, 69, 109 within-subjects, 67–71

expert systems, 6, 52, 83, 86, 98 explained variance, 292–93, 294, 317, 322, 346, 348 Exploratory Factor Analysis (EFA), 358–62, 366, 367,

399, 400 exploratory interview techniques. See also exploratory

research; focus groups; interview techniques Delphi, 18, 59 depth, 59, 74, 77, 137–38, 141 projective, 59–60

exploratory research. See also experimental design; exploratory interview techniques; qualitative data

Chrysler minivan, 36 empirical tests, 51, 56, 77, 83, 251 literature reviews, 58 non-probability sampling, 206, 215–16, 219 open-ended questions, 189 overview, 57–60, 77 research questions, 71 technology and, 58–59 variable selection, 77

face-to-face interviews, 128, 132–33, 138, 139, 140–41. See also interview techniques

face validity, 172 factor analysis. See also ANOVA; nominal scales;

ordinal scales assumptions, 359, 360 Burger King/McDonalds case study, 356–58 Chi-square tests, 254, 257, 260–64, 274–75 common factor, 358–60

communality, 362–63 composite factors, 357–58 customer surveys, 34–36, 153–54, 164 EFA, 358–62, 366, 367, 399, 400 experimental designs, 68–71 factorial experimental results, 68–69 factor loading, 362–68 factor rotation, 360–61 factor scores, 360, 398–402, 399–404, 400, 401, 408 factor solutions, 362–63, 364–67 FICO scores example, 357 finite population correction factor, 218 interpretation of, 363–64 latent root criterion, 361–62, 367 mean calculations, 398 null hypotheses, 364 overview, 77, 270, 288–89, 356–58 PCA, 358–62, 366, 367, 399, 400 percentage of variance, 267–68, 361–63 with regression analysis, 397–402, 408 rotation in, 360–61, 364, 366, 399 Samouel's Restaurant case study, 364–68 sample size determination, 218 selection factor rankings scales, 35–36 simple structure, 360–61 Spearman's rank order correlation, 154, 285–87 in SPSS, 362, 367, 375, 400, 401 sum of squared factor loadings, 362–63 tables, 401–2 unique variance, 359–60 variables, 68–69, 356, 368, 408 variance, 358–63, 366–68, 401 varimax procedure, 360–62, 366, 367, 399, 400 VIF (Variance Inflation Factor), 303–4, 308, 310 with XLSTAT, 250

failure as learning, 98 Fair, Isaac and Company, 357 fax surveys, 130–31, 139, 200–201 FDA (Food and Drug Administration), 115 Federal Express, 13, 88 FEDSTATS, 255 FICO scores, 357 field experiments, 65–67, 77, 96–97, 109. See also

experimental design; exploratory research finite population correction factor, 218 focus groups. See also exploratory research; non-

probability sampling content analysis of, 125 data collection, 122–23, 135 depth interviews compared to, 137–38


focus groups (continued) expense of, 136 probing, 58–59, 74, 77

follow-up test, 267–68 Food and Drug Administration (FDA), 115 forced choice scales, 166. See also scales formulation phase (business research process), 50–51,

57, 86 F-ratio/F-test, 267, 271, 292, 299 freedom from harm, 109–10 free markets, 9–10 frequency distribution, 229–37, 260–61, 318–19, 329 Frito Lay, 60, 67 front-end loading, 326 functional fixedness, 87, 88–89, 92–93, 98 funnel approach, 193, 194–95 Gallup Organization, 147 gay, lesbian, bisexual, transgender population (GLBT),

127 GCP (Good Clinical Practices), 115 gender variables, 148. See also variables General Social Survey, 75 geographic area sampling, 214 Geographic Information Systems (GIS), 14 GINLIST (Global Interact Network Mailing List), 75 Gino's Ristorante, 21, 32–36. See also case studies;

customer assessment project; Samouel's Restaurant

GIS (Geographic Information Systems), 14 GLBT (gay, lesbian, bisexual, transgender) population,

127 Global Interact Network Mailing List (GINLIST), 75 GolfBC, 86 Good Clinical Practices (GCP), 115 goodness of fit, 261 Google, 53, 73 GPS (Global Positioning Satellite), 15, 17, 124 graphic ratings scales, 157, 160–61. See also metric

scales graphs. See charts and graphs grocery industry, online, 7 group mean. See also descriptive research; mean

cluster analysis, 372–75, 377 discriminant analysis, 377–78, 382–85, 398 hypothesis testing, 260 null hypothesis, 251 overview, 275, 386 in SPSS, 317, 375, 377 testing differences in, 264–73

Group Statistics tables, 384–85

group think, 92 Harrah's Casinos, 16 heterogeneity, 213–14, 217–18, 368, 386 heuristic ideation technique (HIT), 94–95 hierarchical clustering, 371 histograms, 232–35, 239, 240, 245 history of business research, 4 HIT (heuristic ideation technique), 94–95 hit ratio, 381–82, 384, 396, 404 homogeneity

in cluster analysis, 356, 368–69, 386, 390 in sampling, 211, 212, 213–14, 216–18

Hoover's Business Press, 75 human error, 228 human resources review committee, 112 hypotheses. See also assumptions; hypothesis testing;

null hypotheses alternative, 251, 252–53, 258–59, 264, 291, 297 assumptions as, 251 casual Friday example, 56 data analysis of, 252–53 demand effects, 68, 95–98, 109, 111–12, 114–15,

193–94 descriptive research, 62 developing, 36, 76–77, 137–38, 251–52 directional, 252 employee study, 341–42, 344–46 field experiments, 65 nondirectional, 252 objectivity from quantitative data, 74, 78 overview, 50, 51, 54–55, 60, 64–65, 250–51 research proposals/reports, 327–29, 330, 331, 336,

340–45, 347–49 scientific method, 54–56, 77 statistical significance, 252–53

hypothesis testing. See also hypotheses; null hypotheses; specific scales

ANOVA, 260, 266–73 bivariate statistical techniques, 260–64 causal research, 64–65 cross-tabulation using Chi-square analysis, 260–64 customer assessment project, 258–60 descriptive research, 60, 62, 76, 250 group mean, 260 interval scales, 258–60, 264–65 level of significance, 258–60 MANOVA, 273 multiple regression analysis, 303 null hypotheses, 258–60, 291–92


hypothesis testing (continued) overview, 252–55, 257, 274–75 with qualitative data, 74, 76 with quantitative data, 74, 76 rejecting hypotheses, 281–82 research design, 56–57 satisfaction, concept of, 253, 258, 264, 265–73 scales utilized, 254, 257 secondary data, 72–73 statistical significance, 258–59 statistical techniques, 254 t-test of difference in two means, 264–65 Type I and Type II errors, 253 univariate/bivariate tests, 258–65

ideas, 54, 90, 94–96, 99, 145 implementation (CAB process), 83, 96–98 Implications and Conclusions, 330, 332, 349–51. See

also research proposals and reports importance-performance chart, 405–7 incentives vs. coercion, 108–9 incorrect assumptions, 87–88, 98–99. See also

assumptions independence techniques. See also discriminant

analysis multiple regression analysis, 294–302, 313–22,

345–46 stepwise multiple regression, 295, 305–8, 310

independent samples, 264, 348. See also sampling, about

independent variables, 266, 270–71, 280–81. See also variables

in-depth interviews, 59, 74, 77, 137–38, 141 index. See scales indicators. See proxy variables; variables inferential statistics, 250, 252–53, 274 information-only businesses/products, 8–9 information revolution. See technology information sources. See also software

business research reports, 332 content analysis, 125 online information and databases, 226–27 qualitative data, 122 questionnaire designs, 183 questionnaire development, 201 Researcher Sourcebook™, 129 scales, 175–76 secondary data, 73–74, 75, 77–78 SIRS Knowledge Source, 75 statistics, 14, 75, 255–56

survey sampling, 208 Tamtam.com, 18

in-house research, 18–20 ink blot tests, 59 intangible variables, 148, 150. See also variables intellectual property rights, 9 intensity of responses, 151, 155, 156–57. See also point

scales intentions of subjects, 157, 158. See also participants;

surveys interactions, 15, 68, 69, 77, 278–79. See also statistical

techniques interdependence statistical techniques. See also cluster

analysis; factor analysis perceptual mapping, 288–89, 390, 405–6, 409

internal consistency reliability, 169–70 Internal Revenue Service (IRS), 15 international research, 10–11 Internet

computer dialogue, 138–39 consumer information, 108 email, 13, 123, 131–32, 194, 200–201, 208 online grocery venture, 7 online panel surveys, 129, 201, 208 Web-hosted surveys, 13, 129, 131–32, 135–36,

138–39, 208 inter-rater reliability, 74, 76 interrogatories, 88–89. See also research questions interval scales. See also ANOVA; metric scales; scales

arbitrary zero points, 154 central tendency, 237–38 constant sum scales compared to, 163 continuous variables, 151, 177 hypothesis testing, 258–60, 264–65 independent samples t-test, 347 Likert scales, 155–57, 297–98, 343–44 overview, 150–51, 154–56, 166–67, 177, 285 Pearson correlation coefficients, 281–83 regression analysis, 290, 313, 322, 345, 348 statistical techniques, 155, 254, 257, 275, 288 univariate hypothesis testing, 258–60, 264–65 variables, 264–65, 266

interviews. See also data collection; interview techniques

convenience samples, 215 of decision makers, 105–6 overview, 140–41 passive vs. active interviewers, 76 price sensitivity analysis with, 97


interview techniques. See also focus groups; interviews; probing; unstructured interviews

Delphi technique, 18, 59 depth, 59, 74, 77, 137–38, 141 interviewer-assisted questionnaires, 132–39, 186 panel surveys, 63, 126, 127, 129, 201, 208 personal, 127–28, 132–33, 138, 139, 140–41 projective, 59–60 semi-structured, 133–36 structured, 60, 133, 141 telephone, 127, 138, 139, 140–41, 200–201 Web-hosted surveys, 13, 129, 131–32, 135–36,

138–39, 208 Introduction (research report), 328–29, 340–41. See

also research proposals and reports intuition-based decisions, 53–54 IRS (Internal Revenue Service), 15 issues, definition, 90–91 Job Mart, 129 Johns Hopkins University, 112 Journal of Statistics Education, 256 judgment sampling, 209, 215 KDD (Knowledge Discovery in Databases), 14–15 KISS, 326 knowledge, derivation of, 71 Knowledge Discovery in Databases (KDD), 14–15 knowledge management, 224 Kodak, 92 kurtosis, 242 labels for scale categories, 166–67 laboratory experiments, 65–67, 70, 77. See also

experimental design; exploratory research latent root criteria/variance, 361–63, 367 law-like generalizations, 51 leading questions, 197. See also research questions least common denominator principle, 325 least squares method, 290, 294 lesbian population, 127 level of confidence/precision, 216, 217 level of significance, 258–60. See also statistical

significance Lexis-Nexis database, 75 Likert scales, 155–57, 297–98, 343–44. See also

interval scales; scales limitations (research report), 351 linear regression formula, 290. See also regression

analysis

linear relationships (variables), 278–79, 281–83, 288, 320, 358. See also variables, about

literature reviews, 58, 331 logistic regression, 288–89. See also regression

analysis longitudinal studies, 62–63, 128. See also descriptive

research mahalanobis, 370 mail surveys, 130–31, 139, 200–201. See also

questionnaires; surveys main effects, 68–69, 77 Management Information Systems (MIS), 85–86, 175 management-researcher relationship. See researcher-

client relationship manipulation. See also variables, about

in causal experiments, 64–65 demand effects, 68, 95–98, 109, 111–12, 114–15,

193–94 on electronic surveys, 132 harmful, 109–10 manipulation checks, 225 overview, 70

MANOVA (Multivariate Analysis of Variance), 273, 288–89. See also ANOVA

manufacturing applications, 7 marketing applications, 7 mathematical theory, 53 Maxwell House/Starbucks case study, 254, 257. See

also case studies McDonalds/Burger King case study, 264, 356–58,

377–81. See also case studies mean. See also descriptive research; group mean

ANOVA, 266–67, 268–73 assumptions for estimating, 217 bivariate analysis, 268–73, 283, 291–93 box and whiskers plot, 244–45 central tendency, 57, 237–40, 246–47, 254, 257 centroids, 384–85 cluster analysis, 368, 374–76, 392–95, 408 correlation analysis, 307 dispersion, 239–40, 242, 246–47 display of, 330 in Excel, 46 factor analysis, 359, 366–67, 398 histograms, 232, 234 importance-performance charts, 407 interaction of two, 69 interval data, 155


mean (continued) mean square, 271, 292–93, 300, 302, 306, 315, 316,

401 missing data, 228, 232–33 null and alternative hypotheses, 251 performance ratings, 347–48 pie charts, 237 regression analysis, 298–304, 315–16, 319–20, 321,

322, 345–48, 400–402 scales utilizing, 237–38, 254, 257, 264 in SPSS, 228, 266 standard error of the, 241 substituting the, 228 summated scores, 402–3 t-tests, 264–66 univariate testing, 258–60 variance from the, 240–41

measurement. See also measures; scales of attitudes/opinions, 61, 127, 146–47, 154, 156–64 of behavioral intentions, 157, 158, 175 error, 167–68 of intensity, 151, 155, 156–57 levels of, 151–56 overview, 145, 146–51, 157, 177 of perceived product complexity, 161

measurement scales. See scales measures. See also questionnaires; research questions

of central tendency, 237–39, 240, 246–47 of constructs, 175 of customer interaction, 145 of dispersion, 239–43, 246–47 objective, 148, 150 of perceptions, 35 of a population, 252, 281–82 of relationship, 33, 36 of satisfaction, 391 of work environment, 33, 230

median box and whisker plots, 244 central tendency, 237–39, 246 cluster analysis, 368, 371, 408 dispersion, 239–40, 242 in Excel, 46 importance-performance charts, 406 scales utilizing, 177, 254, 257 Spearman's rank order correlation, 286–87 stem and leaf displays, 245

Merrill Lynch, 86 message framing check, 149

methods and strategies. See causal research; descriptive research; exploratory research; interview techniques; sampling methods; scales; statistical techniques

metric scales. See also nominal scales; ordinal scales; scales

graphic ratings, 157, 160–61 mean calculations, 237–38, 254, 257, 264 median calculations, 177, 254, 257 numerical, 157–58, 175 overview, 157, 161 Pearson correlation, 155, 156 semantic differential, 157, 158–60, 161 summated ratings, 156–57, 165, 169, 229, 230,

402–3 metric statistical techniques. See also ANOVA;

correlation analysis conjoint analysis, 288–89 MANOVA, 273, 288–89 multiple regression analysis, 288–89, 294–302,

313–22, 345–46, 397–402 metric variables, 148–50, 151, 177, 228, 270–71, 273,

281–83. See also metric scales; variables Michalko, Michael, 89 MIS (Management Information Systems), 85–86, 175 missing data, 41, 225, 227–28 mode. See also descriptive research

central tendency, 57, 237–40, 238–39, 246–47, 254, 257

dispersion, 239–41, 242–43, 246–47, 254 in Excel, 46 importance-performance charts, 406 scales utilizing, 152, 177, 254, 257 stem and leaf displays, 244

model of service quality, 159 model summary tables, 291, 298, 302–3, 305–6,

314–15, 345–46 moderators, group, 58–59, 74, 77, 134 Moen, Inc., 124 monitoring, 97 multicollinearity, 295, 299, 301–4, 310. See also

collinearity; multivariate statistical techniques multi-item scales, 165, 168, 169, 171, 173–74, 177. See

also concepts/constructs; scales multiple coefficient of determination. See R2 multiple comparisons tables, 273 multiple discriminant analysis, 377–82. See also

discriminant analysis


multiple regression analysis, 288–89, 294–303, 305–8, 310, 313–22, 345–46, 397–402. See also regression analysis

multiple solutions, 90 multi-stage sampling, 209, 214–15. See also sampling,

about Multivariate Data Analysis (Hair), 305 multivariate statistical techniques. See also bivariate

statistical techniques; cluster analysis; factor analysis; univariate statistical techniques

MANOVA, 273 multicollinearity, 295, 299, 301–4, 310 multicollinearity with multiple regression analysis,

301–4 multiple discriminant analysis, 377–82 multiple regression analysis, 288–89, 294–301,

313–22, 345–46, 397–402 overview, 254, 287–88 stepwise multiple regression analysis, 305–8

narrative data. See qualitative data National Decision Systems, 10 networking, 13 Nielsen ratings, 63 Nightingale, Florence, 235 nominal scales. See also discriminant analysis; factor

analysis; mode; nonmetric scales data analysis, 152 discrete variables, 151, 177 nominal data, 238, 260–61, 275 overview, 150–52, 177 statistical techniques, 254, 257, 285–87, 288–89, 378 variables, 330, 347

nondirectional hypotheses, 252 non-forced choice scales, 166 nonhierarchical clustering, 371–72 nonlinear relationships (variables), 278–79. See also

variables, about nonmetric scales. See also discriminant analysis;

nominal scales; ordinal scales; scales categorical scales/variables, 162, 266, 273 constant sum, 157, 163 data analysis, 177 displaying results, 330 dummy variables, 228, 290, 313–16, 322 median calculations, 286 missing data, 228 overview, 157, 161, 177 paired comparison, 157, 163–64 sorting, 157, 162–63

statistical techniques, 154, 285–87, 288–89 variables, 151, 177, 266, 270–71

nonparametric statistical procedures. See also Chi-square; nominal scales; ordinal scales

assumptions, 257 dispersion, 239–41, 241–43, 246–47, 254 hypothesis development, 253

non-probability sampling, 206, 209, 215–16, 219 non-spurious association

causality, 64 normal distribution, 236–39, 240, 257, 282, 317-19, 321.

See also parametric statistical procedures normative decision rules, 52 null hypotheses. See also hypotheses; hypothesis

testing ANOVA, 266–68 associative analysis, 278 bivariate hypotheses testing, 291–92 bivariate testing, 265–66 cluster analysis, 373, 375, 391, 396 correlation analysis, 281–83 factor analysis, 364 multiple discriminant analysis, 378, 380, 382 overview, 251–53 population parameters, 250 regression analysis, 297, 299–300, 303 rejecting, 281–82 Spearman's rank order correlation, 285 statistical power, 253 statistical significance, 279–80 two-way ANOVA, 270–73 Type I and Type II errors, 253 univariate hypothesis testing, 258–60

numerical scales, 157–68, 175. See also metric scales objective measures, 71–72, 148, 150 oblique rotation, 360–61 observability (of ideas), 95 observations, 54–55, 62–63, 71, 122–26, 139–40. See

also outliers Occupational Safety and Health Administration (OSHA),

103 off-the-shelf data, 14, 75, 129 Olestra Potato Chip test market, 67 one-sample t-test, 259–60, 264 one-shot research projects, 6, 61 open-ended questions, 189 opening questions, 193 opportunity, 84, 87


ordinal scales. See also mode; nonmetric scales; scales discrete variables, 151, 177 as interval scale, 154–55 ordinal data, 154, 238 overview, 150–51, 152–54, 161–64, 177 pie charts, 330 rank order, 154–55, 157, 162–63, 285–87, 288–89 statistical techniques, 154, 155, 254, 257, 260–61,

274–75, 286, 288, 290 organizational commitment, 12, 32–34, 146–47, 155,

160–61, 341 organizational learning/memory, 14–15 organizational variables, 126 orthogonal rotation, 360–61 OSHA (Occupational Safety and Health Administration),

103 outcome variables, 69, 77, 273 outliers, 232, 237–38, 243, 247, 317–21, 322 outside research, 18–20 overview sampling, 30, 209–16, 219 paired comparison scales, 163–64. See also nonmetric

scales panel surveys, 63, 126, 127, 129, 201, 208 parametric statistical procedures, 253, 257. See also

ANOVA; interval scales; normal distribution; ordinal scales; t-tests

parsimony, 57 participants

attitudes/opinions of, 127, 146–47, 154, 156–64, 187 demand effects, 68, 95–98, 109, 111–12, 114–15,

193–94 ethical obligations of, 113–15, 117 ethical treatment of, 107–12, 123–24 human resources review committee, 112 privacy issues, 107–8, 110, 114–15 treatment levels of, 67–71

PCA (principle components analysis), 358–62, 366, 367, 399, 400

Pearson bivariate correlations, 282–84, 304 Pearson Chi-square test, 261, 263 Pearson correlations, 281–82, 307, 309, 346 percentage of variance, 267–68, 361–63. See also

factor analysis perceptions. See satisfaction, concept of Percept Technology, 10 perceptual data. See also data

analyzing, 288–89, 290–91, 297–301, 316 collecting, 30, 34–35, 184–85, 197–98, 214–15 measuring with, 146–48, 152, 154–55, 160–61, 171

overview, 72 rationale for, 58, 61, 72 subjective nature of, 72 from surveys, 174–75 variables, 171, 234, 365–66, 368, 395–97

perceptual mapping, 288–89, 390, 405–6, 409. See also interdependence statistical techniques

personal interviews, 127–28, 132–33, 138, 139, 140–41. See also interviews

personal value systems. See business ethics phenomena. See concepts/constructs; hypotheses pie charts, 235–37, 329–30 pilot tests/training, 65–66, 96–97, 99, 217–18, 219. See

also exploratory research; test markets placebos, 111 point scales. See also descriptive research; scales

Casewise Diagnostics, 321 classification variables, 36 cluster analysis, 375–76, 391, 393, 408 collapsing or combining categories, 229 5-point agree/disagree, 145 graphic ratings, 160 intensity of, 149–50 numerical, 157–58 100-point ethical scale, 103 ordinal ranking, 154–55 perceived risk, 149 perceptions measure, 35, 61–62, 68 price check, 150 range, 240, 242 relationships measure, 36 respondents' capacity to understand, 165 reverse coding, 170, 172, 229 selection factor rankings, 35 semantic differential, 158–60, 161 7-point agree/disagree, 33–34, 35, 36, 41–42,

230–31, 230–32, 235, 238–39, 258 7-point Likert, 155–57, 297–98, 343–44 7-point ranking, 149–50, 241–42, 266 source credibility check, 149 standardizing data, 370 summated ratings, 156–57, 402–3 10-point ranking, 168–69 work environment survey, 152

Pontiac Aztec, 136 populations. See also sampling, about

comparing two or more, 266–73 finite population correction factor, 218 GLBT population, 127 measures of, 252, 281–82


populations (continued) overview, 206 parameters, 250, 252–53, 257–58, 274 parametric variables, 252, 274 population correction factor, 218 target, 186, 207–8, 218–19

population statistics for U.S., 127 position bias, 197–98 practical significance, 284 predictive validity, 173–74 presence (variable relationships), 278. See also

variables, about presentation recommendations, 333–35 pretests, 199–200, 206 primary data, 72, 77–78 principle components analysis (PCA), 358–62, 366,

367, 399, 400 principle components variables, 358–62, 366, 367, 399,

400. See also variables privacy issues, 107–8, 110, 114–15 probability levels/plots, 317–19, 322 probability sampling, 206, 209–15, 219 probing interview technique. See also interview

techniques depth interviews, 59, 74, 77, 137–38, 141 focus groups, 58–59, 74, 77 issue discovery with, 90–91 questionnaire pretesting, 200

problem identification. See research questions product line profitability, 13 profiling, 376 pro-forma tables, 87, 89–90, 328, 336 Progressive Insurance, 17 project deliverables, 105 projective interviews, 59–60. See also interview

techniques promotional trends, 62 proportionately stratified sampling, 212, 213 proposals. See research proposals proxy variables, 148, 149–50, 174. See also variables psychographics, 10 public speaking, 333–35 purposive sampling, 215 push polls, 110 QSR NUD*IST software, 125 qualitative data. See also nonmetric scales

analysis software, 125, 126 hypothesis testing, 74, 76 overview, 74, 76, 78, 140

sources of, 122–23, 124 qualitative research. See also focus groups;

unstructured interviews depth interviews, 59, 74, 77, 137–38, 141 variables, 151, 177

quality control tests, 206 quantitative data, 74, 76, 78, 122, 127, 140. See also

metric scales hypothesis testing, 74, 76

quantitative research, 189. See also metric scales variables, 151, 177

questionnaire design process. See also research questions

context effect, 197–98 instructions, 199 opening questions, 193 overview, 182, 183, 201–2 pretesting, 199–200 question preparation, 195–98 reliability assessment, 168–72 structure, 189–95

questionnaires. See also employee study; interviews; measures; surveys

administering, 186, 200–201 customer survey, 34–36, 154, 184–85 Gallup Q12, 147 interviewer-assisted, 132–39, 199 overview, 127–30, 140 self-completion, 130–32, 186, 199

Quirk's Research Review, 129 quota sampling, 209, 215–16 RAGE matrix, 127 rail gauge example, 89 Random Digital Dialing (RDD), 208, 210 random error, 251 random sample selection

in SPSS, 210–11 random sample tables, 211 random sampling, 210 range, 240. See also dispersion rank order scales, 154–55, 157, 162–63, 285–87,

288–89. See also ordinal scales ratings scales. See also metric scales

graphic, 160–61, 217 overview, 154–55 summated, 156–57, 165, 169, 229, 230, 402–3

ratio, sample to completion, 210


ratio scales. See also metric scales; standard deviation absolute zero points, 156 ANOVA, 266–67 continuous variables, 151, 177, 264–65, 266 group means, 264 mean, estimating sample size of, 217 measuring central tendency, 237–38 overview, 150–51, 156, 177, 274, 285 parametric statistics, 257–64 Pearson correlation coefficients, 281–83 regression analysis, 290, 313, 322, 345–46

ratio statistical techniques, 288–89, 294–302, 313–22, 345–46, 397–402. See also ANOVA; correlation analysis; statistical techniques

RDD (Random Digital Dialing), 208, 210 reality-based theory, 53–54 Recommendations and Conclusions, 330, 332, 349–51.

See also research proposals and reports reference list, 330 referral samples, 216 referral sampling, 209, 216 regression analysis

assumptions, 290, 294, 313, 322 bivariate regression, 288–94 coefficients, 292–96, 299, 301, 303, 306, 308, 310 discriminant analysis compared to, 379, 385 dummy variables, 313–16, 322 with factor analysis, 397–402, 408 linear regression formula, 290 multicollinearity, 295, 299, 301–4, 310 multiple regression, 294–302, 295, 305–8, 310,

313–22, 345–46 null hypotheses, 297, 299–300, 303 overview, 287–91, 309–10 residuals analysis, 292–93, 294, 305, 317–21, 322 satisfaction regression, 348–49 simple regression, 290 in SPSS, 292, 296, 299, 305–8, 310, 322 statistical significance, 292–93, 295–96, 298–99, 304 summated scores, 405, 408 sum of squares, 292–93, 294, 317, 322, 346, 348,

399 tables, 330, 348, 401–2 variances, 346

related samples, 264 related sampling, 264 relationship marketing, 12, 21, 33, 36 relationship outcome variables, 273. See also variables relationship presence, 278 relationships. See variables, about

reliability, 167–72, 177 replicable research, 6, 74, 76 representative sample, 127, 207, 209, 218 research design. See business research designs researcher bias, 225 researcher-client relationship. See also research

proposals and reports consensus, 112, 113 consultant vs. in-house researcher, 18–20 dummy tables, 87, 89–90, 328, 336 ethics, 106–7, 112–13 overview, 17, 116–17

researcher dependent data, 72, 76 researcher-participant relationship. See also interviews;

interview techniques ethical obligations of researcher, 107–12 moderator characteristics, 134 opening questions, 193 question design considerations, 186

Researcher Sourcebook™, 129 research proposals and reports

business-to-business survey, 28 Children's Health Foundation Research Proposal,

26–27 hypotheses in, 327–29, 330, 331, 336, 340–45,

347–49 oral presentations, 332–35 overview, 25, 89–90, 324–27, 336 research implications, 7, 328 research topic questions, 8, 193–94 results of research, 106–7, 329–30, 344–49 Samouel's Restaurant, 29–31, 339–51 schedule, 27, 31, 328 structure, 27–28, 327–32

research questions. See also hypotheses; measures; point scales; scales

actionable, 82, 86, 87, 89, 91, 98–99, 133 aided, 196–97 CAB process, 82, 85–91, 98–99 casual Friday example, 56 classification, 33–34, 153, 185, 187, 189, 192,

194–95 concept measuring, 33–36 descriptive, 60, 71 design selection, 71 developing, 57–60, 87–91, 105–6, 185–87 discovery or clarification, 71 double-barreled, 197 evaluation of, 90, 95–96, 99, 185–86, 344 hypotheses developed from, 55


research questions (continued) interrogatories, 88–89 issue discovery with, 90–91 leading, 197 open- and close-ended, 188–89 overview, 54 position bias, 197–98 recommendations, 330 screening, 114, 193 solution space, 89, 90, 93

research reports. See research proposals and reports residual analysis

variances, 305 residuals analysis, 292–93, 294, 305, 317–21, 322 respondents. See participants response variables, 69, 77, 273. See also variables risk. See statistical significance R.J. Reynolds, 106–7 Rogers, Everett, 95–96 rotated factor component matrix, 364, 366 rotation sums of squared loadings variance, 399 r2 (coefficient of determination), 282, 291, 293, 295 R2 (multiple coefficient of determination)

multiple regression analysis, 298, 301, 304, 305, 310, 314, 349

overview, 296 residuals analysis, 317, 322

sales/advertising relationship, 51–52, 55, 288–91, 294–95, 309

Sales and Marketing Index (SAMI), 14 SAMI (Sales and Marketing Index), 14 Samouel's Restaurant case study. See also case

studies; customer assessment project; employee study; satisfaction, concept of

cluster analysis example, 372–77 cluster sample, 214 cross-tabulation with Chi-square test, 260–64 customer survey, 184–85 dummy variables example, 314–16 factor analysis example, 364–68 hypotheses development, 251 introduction, 21, 32–36, 207 multicollinearity example, 302–4 normative decision rule example, 52 one-sample t-tests, 258–60 random sample selection example, 211 research proposal, 29–31 research report, 339–51 residuals and outliers example, 318–21

restaurant image concept, 174–76 stepwise multiple regression example, 305–8 t-test of difference in two means, 264–65 univariate statistical tests, 258–60

sampling, about. See also descriptive research; populations; sampling methods

assumptions, 61 census vs. sample, 206–7, 257–58 customer segments, 16 error, 61, 251, 296 finite population correction factor, 218 frame selection, 209, 218–19 implementation, 218 independent, 264, 348 independent and related, 348 mean, estimating sample size of, 217 measurements, 61 overview, 206–8, 218–19, 252 random sample tables, 211 sample statistics, 252–53, 257–58, 274 sample to completion ratio, 210

sampling methods. See also focus groups; sampling, about

cluster, 209, 209, 213–15, 219 convenience, 209, 215 geographic area, 214 judgment, 209, 215 multi-stage, 209, 214–15 non-probability, 206, 209, 215–16, 219 overview, 30, 209–16, 219 panels, 63, 126, 127, 129, 201, 208 probability, 206, 209–15, 219 purposive, 215 quota, 209, 215–16 referral, 209, 216 related, 264 representative sample, 127, 207, 209, 218 sample statistics, 252–53, 257–58, 274 sample to completion ratio, 210 sampling interval, 210–12 sampling unit, 208 simple random, 209, 210 size determination, 216–18, 219, 238, 262 snowball, 209, 216 in SPSS, 210–11, 259 stratified, 209, 212–13 systematic, 209, 210–12

Samuel Adams Beer, 96 satellite technology, 15, 17, 124 satisfaction, concept of


bivariate analysis, 290–96, 309–10 cluster analysis, 370, 390–97, 391–98, 408 concept development, 145–46, 188 data analysis, 233–34 data collection, 9, 72 dummy variables, 313–15, 322 evaluative criteria, 95 factor analysis and multiple regression, 397–402 factor scores in regression analysis, 399–404 field research measures, 36, 58, 190–92 hypotheses development, 36, 65 hypotheses testing, 253, 258, 264, 265–73 measuring, 146–47, 150, 166, 177 multivariate analysis, 297–301, 305–8, 310 overview, 7, 72 perceptual mapping, 405–8 research proposal, 29–30 research report, 340–43, 347–51 residuals analysis, 318–21, 322 sampling methods, 212 in SPSS, 234 summated scores, 402–5 variable relationships, 278

scales. See also metric scales; nonmetric scales; point scales; research questions

balanced, 165–66 categorical scales/variables, 162, 266, 273 category labels, 166–67 correlation analysis, 285–87 developing, 164–67, 174–76 discrete variables/scales, 151, 177 forced choice scales, 166 hypothesis testing, 254, 257 information sources, 175–76 Likert scales, 155–57, 297, 343–44 for mean calculations, 237–38, 254, 257, 264 for median calculations, 177, 254, 257 for mode calculations, 152, 177, 254, 257 multi-item, 165, 168, 169, 171, 173–74, 177 overview, 30, 33–36, 150–51, 177, 229 Pearson correlation, 155, 156 selecting, 164–67 semantic differential, 157, 158–60, 161 sources of, 175–76 summated ratings, 156–57, 165, 169, 229, 230,

402–3 unbalanced, 165–66 validity/reliability assessments, 167–75

scatter diagrams/plots, 280–81, 290, 319, 369–72, 379 Scheffé test, 268–70

science, 54–57, 65–67, 77. See also business research designs; experimental design

scope of process, 27, 328 screening questions, 114, 193 seasonal trends, 60, 62–63 secondary data, 72–74, 75, 77–78

hypothesis testing, 72–73 selection factor rankings, 35–36 selection factor rankings scales, 35–36. See also factor

analysis self-completion questionnaires, 130–32, 186, 199 semantic differential scales, 157, 158–60, 161. See also

metric scales semi-structured interviews, 133–36 service quality research model, 159 share-of-customers. See customer share Siemens, 58 Simmons Market Research Bureau, 127 simple random sampling, 209, 210 simple structure (factor analysis), 360–61 simplicity (of ideas), 96 SIRS Knowledge Source, 75 skewness, 241–42 Skoda, 11 snowball sampling, 209, 216 social science in business research, 5 soft drinks case study, 237–38, 252–53, 285. See also

case studies software. See also SPSS software

AID, 15 content analysis, 125, 126 DSS, 85–86 editing, 37, 45 Excel, 44–46, 239, 242 expert systems, 6, 52, 83, 86, 98 group system, 139 QSR NUD*IST, 125 questionnaire development, 201 TextSmart, 125 XLSTAT, 250

solution space, 89, 90, 93, 94 sorting scales, 162–63. See also nonmetric scales sources. See information sources Soviet Union, free market in, 9–10 Spearman's rank order correlation, 154, 285–87,

288–89 specificity, 186 split-half reliability, 170 spreadsheets, 44–46, 228


SPSS software ANOVA, 272, 375 bar charts and pie charts, 236 boxplot, 244 central tendency, 240 charts and graphs, 41–43 Chi-square, 262–63 cluster analysis, 375, 377, 396, 398 coding, 228 Cronbach's alpha, 170–71 customer database, 34–37 customer surveys, 383, 392 data preparation with, 232, 234, 236, 240, 243, 244,

246 difference in two means, 266 discriminant analysis, 383, 392 dispersion, 243 dummy variables, 316 employee database, 32–34 factor analysis, 362, 367, 375, 400, 401 frequency distribution table, 232 group means, 317, 375, 377 MANOVA, 273 overview, 37–43, 201, 224, 301 Pearson bivariate correlations, 283 random sample selection, 210–11 regression analysis, 292, 296, 299, 305–8, 310, 320,

322 satisfaction, concept of, 234 Spearman's rank order correlation, 286 statistical significance of correlation coefficient, 279–

80 stem and leaf plot, 246 substituting the mean, 228 summated scores, 230, 403 t-tests, 259, 264 variables in, 39–42 variance analysis, 267–68 XLSTAT compared to, 250

standard deviation. See also central tendency;

descriptive research; dispersion; variance, about ANOVA, 272 computing with software, 242, 244 descriptive research, 57 dispersion, 240, 241, 242, 246–47 estimating sample size of a mean, 217 in Excel, 46, 242 histogram, 233 overview, 234, 241

pie charts, 237 of population, 217–18 regression analysis, 319–21, 400 scales with, 155–56, 177, 254, 257 summated scores, 402, 408 t-test of two means, 265 univariate testing, 259

standard error. See also error (in research) in coefficient tables, 293, 300, 302, 303, 306, 308,

315 estimating sample size of mean, 217–18 of the mean, 241 in multiple regression analysis, 401 of skewness, 243

standardized canonical discriminant function coefficients, 383, 385

standardized coefficients, 293–94, 298, 300, 303, 346, 349, 383, 385, 401–2. See also regression analysis

standardized residuals, 317–21, 322 Standard & Poor's Corporate Records, 75 standards, research, 115 Starbucks/Maxwell House case study, 254, 257. See

also case studies statistical analysis. See data analysis; statistical

techniques statistical power, 253 statistical significance

ANOVA, 268 bivariate/univariate tests, 258–64, 283 of correlation coefficients, 279–80, 284–85 hypotheses, 252–53 hypothesis development, 252–53 null hypotheses, 279–80 overview, 274, 278, 279–80, 309, 310 regression analysis, 292–93, 295–96, 298–99, 304 Type I and Type II errors, 253 univariate hypothesis testing, 258–59

statistical techniques. See also ANOVA; bivariate statistical techniques; discriminant analysis; factor analysis; regression analysis

Chi-square tests, 254, 257, 260–64, 274–75 factor analysis, 356–68, 386 multivariate, 254, 273, 287–88, 356–68 one-sample t-test, 259–60 overview, 287–89 Pearson correlations, 281–84, 304, 307, 309, 346 residual analysis, 317–21, 322 Spearman's rank order correlation, 285–87, 288–89 univariate, 254, 258–64, 268–69, 287


statistics. See also descriptive statistics inferential, 250, 252–53, 274 Nightingale's contribution to, 235 parametric, 257 sources for, 14, 75, 255–56

Statistics.com, 256 StatWeb, 256 stem and leaf displays, 245, 246, 247 stepwise discriminant analysis, 385 stepwise multiple regression, 295, 305–8, 310 stratified sampling, 209, 212–13 strength of association, 279, 280 structured interviews, 60, 133, 141. See also descriptive

research; interviews structure matrix correlations, 383–84 structure matrix table, 396–97 subjective data. See perceptual data subjects, 67. See also participants summated ratings scales, 156–57, 165, 169, 229, 230,

402–3. See also metric scales summated scores, 229, 230, 402–5, 408 sum of absolute differences, 370 sum of squared deviation, 241, 290 sum of squared factor loadings variance, 362–63, 401 sum of squares, 267, 271, 292, 362, 363, 366, 401 sum of squares variance, 269, 271, 292–93, 300, 302,

306, 315, 316 surveys. See also data collection; questionnaire design

process; questionnaires completion rates of, 210 email, 13, 123, 131–32, 194, 200–201, 208 mail/fax, 130–31, 139, 200–201 overview, 61–62, 128, 139, 140 panel, 63, 126, 127, 129, 201, 208 primary data from, 72 quantitative data from, 74 self-completion vs. interviewer-administered, 128,

186 Web-hosted, 13, 129, 131–32, 135–36, 138–39, 208

Survey Sampling Inc., 208 Swaddling, Jeffrey D., 58 SWOT assessment, 26 symptoms, definition, 90–91 systematic sampling, 209, 210–12. See also sampling,

about tables. See also charts and graphs; SPSS software

Agglomeration Schedule, 375 ANOVA, 267, 292–93, 302–3, 305–6, 314–16,

374–76 beta, 346

between-subjects effects, 268–69, 271 central tendency, 238–39 Chi-square test, 262–63 classification matrix, 380–81 cluster solution, 397 coefficients, 208, 293–94, 298–99, 303, 315, 346 contingency, 261–62 correlations, 283, 285–86, 304 cross-sectional data, 62 descriptives, 268–69, 271–72, 291, 297–98, 346,

364, 366, 376 diagnostics, 319, 321 discriminant analysis, 382–83 dummy, 87, 89, 328, 336 editing (software), 37, 45 factor analysis, 356–68, 362, 401–2 frequency distribution, 230–37, 329 Group Statistics, 384–85 HIT matrix, 94 independent samples test, 348 model summary, 291, 298, 302–3, 305–6, 314–15,

345–46 multiple comparisons, 273 one-sample t-test, 259 Pearson correlation matrix, 346 pro-forma, 87, 89–90, 328, 336 random sample, 211 regression analysis, 348, 401–2 residual statistics, 319–20, 321 rotated factor component matrix, 366 stratified sample, 213 structure matrix, 396–97 total variance explained, 366–67 t-test, 265 Wilks' lambda (λ), 396–97

Tamtam.com, 18 tangible variables, 148–49 target populations, 186, 207–8, 218–19 Tauber, Ed, 94 t-distribution, 265 technical writing guidelines, 326 technology. See also Internet; software

data collection using, 122 data warehouses, 14, 83, 107 expert systems, 6, 52, 83, 86, 98 exploratory research and, 58–59 information age, 18 information-only businesses, 8–9 networking, 13 satellite technology, 15, 17, 124


Technology IQ test, 19 telephone interviews, 127, 138, 139, 140–41, 200–201 test markets, 65–66, 67, 96–97. See also exploratory

research; pilot tests/training test-retest reliability, 168 Texas A&M University, 159 TextSmart software, 125 thematic apperception, 59 theories

of competition, 51, 53 exploratory research solidifies, 57–60 implicit, 67 normative decision rules, 52 overview, 51–54, 55, 76–77 of relationship between advertising and sales, 51–52,

55, 288–91 validity established with, 172–74

theory-based decisions, 53–54 thinking, 87–88, 94, 95 3M (Company), 92, 124 time and motion management, 5 time series, 62 tolerance, 303–5, 308, 310. See also multicollinearity Total Rewards program (Harrah's), 16 total sum of squares, 292 total variance explained tables, 366–67 Toyota, 5–6 transgender population, 127 translational equivalence, 11 treatment levels, 67–71, 268–69, 271 trends, 9–17, 60, 62–63, 76 t-tests, 259, 264–66, 293, 347. See also ANOVA Type I and Type II error, 253 unbalanced scales, 165–66 unexplained variance, 292–93, 294, 305, 317, 322. See

also regression analysis unique variance, 359–60. See also factor analysis univariate statistical techniques, 254, 258–65, 268–69,

274, 287, 383–85. See also bivariate statistical techniques; multivariate statistical techniques; statistical techniques

universe. See populations unstandardized coefficients, 293–94, 298, 300, 303.

See also coefficients; regression analysis unstructured interviews. See also focus groups;

interviews; qualitative research depth interviews, 59, 74, 77, 137–38, 141 moderators, 58–59, 74, 77, 134

overview, 58–59, 74, 76, 77, 133–34, 136–37 personal, 127–28, 132–33, 138, 139, 140–41 probing technique, 58–59, 74, 77 qualitative data from, 74 selecting participants, 206 utilization of, 134–36

upside down thinking, 93, 95 U.S. Census, 14, 75, 258 U.S. Department of Commerce, 75 validity. See also concepts/constructs

of clusters, 391, 394–95 concurrent, 168, 173 construct, 168, 172 content, 168, 172 convergent, 168, 173, 360 criterion, 168, 173 discriminant, 168, 173, 360 internal vs. external, 65–66 overview, 167–68, 177 predictive, 168, 173–74

value systems. See business ethics variables. See also dependent variables; variables,

about blocking, 69–70 categorical, 266, 273 causal, 64–65, 77, 278, 290 classification, 33–34, 36, 379–82 composite, 229 continuous, 151, 177 discrete, 151, 177 dummy, 228, 290, 313–17, 322 experimental, 69, 109 factor analysis, 68–69, 356, 368, 408 gender, 148 independent, 266, 270–71, 280–81 intangible, 148, 150 metric, 148–50, 151, 177, 228, 270–71, 273, 281–83 nonmetric, 151, 177, 266, 270–71 outcome, 69, 77, 273 overview (of variables), 278–79 perceptions, 171, 234, 365–66, 368, 395–97 population parameters, 252, 274 principle components, 358–62, 366, 367, 399, 400 proxy, 148, 149–50, 174 qualitative, 151, 177 quantitative, 151, 177 relationship outcome, 273 response, 69, 77, 273 tangible, 148–49


variables, about. See also coefficients; correlation; data analysis; factor analysis; manipulation; scales; statistical techniques; variance

applicability considerations, 91 assumptions, 279 clarifying, 91 as components of concepts, 174, 183 covariance, 64, 279–81, 358–59 curvilinear relationships, 278, 279 directional hypotheses, 252 direction of, 279 in electric shock trial, 111 frequency distribution, 229–37, 260–62, 318–19, 329 interactions, 68–69, 77, 278–79 linear relationships, 278–79, 281–83, 288, 320 logical transformations, 229 main effects, 68–69, 77 nonlinear relationships, 278–79 organizational, 126 overview, 145–46, 150–51, 177, 278–79, 287–88,

295–96, 309 presence, 278 proxies, 148, 149–50 rank order correlation, 285–87, 288–89 relationship presence, 278 sample statistics, 252 scatter diagrams, 280–81 in SPSS, 39–42 statistical technique choices, 254, 258, 274 strength of association, 279 values assignment, 40 variability/homogeneity, 211, 212, 213–14,

216–18, 239–43 variance, about. See also ANOVA; coefficients;

dependent variables; variables, about; variances

cluster analysis, 372, 374 discriminant analysis, 379, 382 factor analysis, 358–63, 366–68 MANOVA, 273, 288–89 overview, 240–41, 278–79 percentage of variance, 267–68, 361–63 regression analysis, 399 in SPSS, 267–68 variability/homogeneity, 211, 212, 213–14, 216–18

Variance Inflation Factor (VIF), 303–4, 308, 310. See also multicollinearity

variances. See also variance, about common, 358–60

covariance, 64, 280–81, 358–59 dispersion, 239–41, 241–43, 246–47, 254 error, 317, 322, 359–60, 362, 372, 374 explained, 292–93, 294, 317, 322, 346, 348 factor analysis, 358, 361–63, 366, 367 F-ratio, 271, 292, 299 within or between groups, 267, 270–71 regression, 292–93, 294, 317, 322, 346, 348 residual, 292–93, 294, 305, 317, 322 rotation sums of squared loadings, 399 sum of squared factor loadings, 362–63, 401 sum of squares, 269, 271, 292–93, 300, 302, 306,

315, 316 t-test, 264–65 unexplained, 292–93, 294, 305, 317, 322 unique, 359–60 VB (variance between groups), 267, 270–71 VW (variance within groups), 267, 270–71

varimax procedure, 360–62, 366, 367, 399, 400. See also factor analysis

VB (variance between groups), 267, 270–71 VIF (Variance Inflation Factor), 303–4, 308, 310. See

also multicollinearity visual aids, 333 VW (variance within groups), 267, 270–71 Web-hosted surveys, 13, 129, 131–32, 135–36,

138–39, 208. See also Internet WebStat, 256 weighted sum of values, 379 Wilks' lambda (λ), 396–97 within or between groups variance, 267, 270–71 within-subjects experimental design, 67–71 word association, 59 XLSTAT, 250 zero points, 154–56 Zobel, Mark W., 58 Z-scores, 378–80. See also discriminant analysis

Essentials+of+Business+Research+Method.pdf

Documents

Transcript of Essentials+of+Business+Research+Method.pdf