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Essentials of Business Research Methods Joseph F. Hair, Jr., Ph.D., author; Leyh Publishing Index by Melody Englund, Songbird Indexing Services
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
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
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
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
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
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
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
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,
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;
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
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
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
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
also concepts/constructs; scales multiple coefficient of determination. See R2 multiple comparisons tables, 273 multiple discriminant analysis, 377–82. See also
narrative data. See qualitative data National Decision Systems, 10 networking, 13 Nielsen ratings, 63 Nightingale, Florence, 235 nominal scales. See also discriminant analysis; factor
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
population statistics for U.S., 127 position bias, 197–98 practical significance, 284 predictive validity, 173–74 presence (variable relationships), 278. See also
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
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
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
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
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
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,
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
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;
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