Quantitative marketing research
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Quantitative marketing research is the application of quantitative research techniques to the field of marketing. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion.
As a social research method, it typically involves the construction of questionnaires and scales. People who respond (respondents) are asked to complete the survey. Marketers use the information to obtain and understand the needs of individuals in the marketplace, and to create strategies and marketing plans.
Contents
Typical general procedure
Simply put, there are five major and important steps involved in the research process:
 Defining the problem.
 Research design.
 Data collection.
 Data analysis.
 Report writing & presentation.
A brief discussion on these steps is:
 Problem audit and problem definition  What is the problem? What are the various aspects of the problem? What information is needed?
 Conceptualization and operationalization  How exactly do we define the concepts involved? How do we translate these concepts into observable and measurable behaviours?
 Hypothesis specification  What claim(s) do we want to test?
 Research design specification  What type of methodology to use?  examples: questionnaire, survey
 Question specification  What questions to ask? In what order?
 Scale specification  How will preferences be rated?
 Sampling design specification  What is the total population? What sample size is necessary for this population? What sampling method to use? examples: Probability Sampling: (cluster sampling, stratified sampling, simple random sampling, multistage sampling, systematic sampling) & Nonprobability sampling: (Convenience Sampling, Judgement Sampling, Purposive Sampling, Quota Sampling, Snowball Sampling, etc. )
 Data collection  Use mail, telephone, internet, mall intercepts
 Codification and respecification  Make adjustments to the raw data so it is compatible with statistical techniques and with the objectives of the research  examples: assigning numbers, consistency checks, substitutions, deletions, weighting, dummy variables, scale transformations, scale standardization
 Statistical analysis  Perform various descriptive and inferential techniques (see below) on the raw data. Make inferences from the sample to the whole population. Test the results for statistical significance.
 Interpret and integrate findings  What do the results mean? What conclusions can be drawn? How do these findings relate to similar research?
 Write the research report  Report usually has headings such as: 1) executive summary; 2) objectives; 3) methodology; 4) main findings; 5) detailed charts and diagrams. Present the report to the client in a 10minute presentation. Be prepared for questions.
The design step may involve a pilot study in order to discover any hidden issues. The codification and analysis steps are typically performed by computer, using statistical software. The data collection steps, can in some instances be automated, but often require significant manpower to undertake. Interpretation is a skill mastered only by experience.
Statistical analysis
The data acquired for quantitative marketing research can be analysed by almost any of the range of techniques of statistical analysis, which can be broadly divided into descriptive statistics and statistical inference. An important set of techniques is that related to statistical surveys. In any instance, an appropriate type of statistical analysis should take account of the various types of error that may arise, as outlined below.
Reliability and validity
Research should be tested for reliability, generalizability, and validity.
Generalizability is the ability to make inferences from a sample to the population.
Reliability is the extent to which a measure will produce consistent results.
 Testretest reliability checks how similar the results are if the research is repeated under similar circumstances. Stability over repeated measures is assessed with the Pearson coefficient.
 Alternative forms reliability checks how similar the results are if the research is repeated using different forms.
 Internal consistency reliability checks how well the individual measures included in the research are converted into a composite measure. Internal consistency may be assessed by correlating performance on two halves of a test (splithalf reliability). The value of the Pearson productmoment correlation coefficient is adjusted with the Spearman–Brown prediction formula to correspond to the correlation between two fulllength tests. A commonly used measure is Cronbach's α, which is equivalent to the mean of all possible splithalf coefficients. Reliability may be improved by increasing the sample size.
Validity asks whether the research measured what it intended to.
 Content validation (also called face validity) checks how well the content of the research are related to the variables to be studied; it seeks to answer whether the research questions are representative of the variables being researched. It is a demonstration that the items of a test are drawn from the domain being measured.
 Criterion validation checks how meaningful the research criteria are relative to other possible criteria. When the criterion is collected later the goal is to establish predictive validity.
 Construct validation checks what underlying construct is being measured. There are three variants of construct validity: convergent validity (how well the research relates to other measures of the same construct), discriminant validity (how poorly the research relates to measures of opposing constructs), and nomological validity (how well the research relates to other variables as required by theory).
 Internal validation, used primarily in experimental research designs, checks the relation between the dependent and independent variables (i.e. Did the experimental manipulation of the independent variable actually cause the observed results?)
 External validation checks whether the experimental results can be generalized.
Validity implies reliability: A valid measure must be reliable. Reliability does not necessarily imply validity, however: A reliable measure does not imply that it is valid.
Types of errors
Random sampling errors:
 sample too small
 sample not representative
 inappropriate sampling method used
 random errors
Research design errors:
 bias introduced
 measurement error
 data analysis error
 sampling frame error
 population definition error
 scaling error
 question construction error
Interviewer errors:
 recording errors
 cheating errors
 questioning errors
 respondent selection error
Respondent errors:
 nonresponse error
 inability error
 falsification error
Hypothesis errors:

type I error (also called alpha error)
 the study results lead to the rejection of the null hypothesis even though it is actually true

type II error (also called beta error)
 the study results lead to the acceptance (nonrejection) of the null hypothesis even though it is actually false
See also
 Qualitative marketing research
 Quantitative research
 Marketing research
 Master of Marketing Research
 Automated computer telephone interviewing
 Brand strength analysis
 Bureau of Labor Statistics
 Choice Modelling
 Computerassisted telephone interviewing
 Computerassisted personal interviewing
 Data Mining
 DIY research
 Enterprise Feedback Management
 Maximum Difference Preference Scaling
 NIPO Software
 Official statistics
 Online panel
 Paid survey
 Qualtrics
 Questionnaires
 Questionnaire construction
 Rating scale
 SPSS
 Urtak
References
 Bradburn, Norman M. and Seymour Sudman. Polls and Surveys: Understanding What They Tell Us (1988)
 Converse, Jean M. Survey Research in the United States: Roots and Emergence 18901960 (1987), the standard history
 Glynn, Carroll J., Susan Herbst, Garrett J. O'Keefe, and Robert Y. Shapiro. Public Opinion (1999) textbook
 Oskamp, Stuart and P. Wesley Schultz; Attitudes and Opinions (2004)
 James G. Webster, Patricia F. Phalen, Lawrence W. Lichty; Ratings Analysis: The Theory and Practice of Audience Research Lawrence Erlbaum Associates, 2000
 Young, Michael L. Dictionary of Polling: The Language of Contemporary Opinion Research (1992)