Quantitative marketing research

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 obtained and understand the needs of individuals in the marketplace, and to create strategies and marketing plans.

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 re-specification - 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 10 minute 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.

Test-retest 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 (split-half reliability). The value of the Pearson product-moment correlation coefficient is adjusted with the Spearman–Brown prediction formula to correspond to the correlation between two full-length tests. A commonly used measure is Cronbach's α, which is equivalent to the mean of all possible split-half 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:

non-response 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 (non-rejection) of the null hypothesis even though it is actually false

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 1890-1960 (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)