Home > Quantitative marketing research
Quantitative marketing research is a social research method that utilizes statistical techniques. It typically involves the construction of questionnaires and scales. Large numbers of people are contacted, usually in a survey. Marketers use the information so obtained to craft strategies and marketing plans.1 Scope and requirements
If quantitative marketing research is carried out correctly, both descriptive and inferential statistical techniques can be used to analyse data and draw conclusions. It involves a large number of respondents, tests of a specific hypothesis, and the use of random sampling techniques to enable inference from the sample to the population.
2 General procedure
- 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: cluster sampling, stratified sampling, simple random sampling, multistage sampling, systematic sampling, nonprobability samplingSampling is the use of a subset of the population to represent the whole population. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. Nonprobability sampling doe
- 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.
3 Descriptive techniques
The descriptive techniques that are commonly used include:
- Graphical description
- use graphs to summarize data
- examples: histograms, scattergrams, bar charts, pie charts
- Tabular description
- use tables to summarize data
- examples: frequency distribution schedule, cross tabs
- Parametric description
- estimate the values of certain parameters which summarize the data
- measures of location or central tendencyCentral tendency is a term used in some fields of empirical research to refer to what statisticians sometimes call "location". A "measure of central tendency" is either a location parameter or a statistic used to estimate a location parameter. Examples in
- arithmetic meanIn mathematics and statistics, the arithmetic mean of a set of numbers is the sum of all the members of the set divided by the number of items in the set. The word set is used perhaps somewhat loosely; for example, the number 3. 8 could occur more than on
- medianThis article is about the statistical method, for alternative meanings see Median. In statistics, the median is that value that separates the highest half of the sample from the lowest half. More precisely 1/2 of the population will have values less than
- modeMode has several meanings: In statistics, the mode is the value that has the largest number of observations, namely the most frequent value or values. The mode is not necessarily unique, unlike the arithmetic mean and the median. It is most useful when th
- interquartile meanThe interquartile mean (IQM is a statistical measure of central tendency, much like the mean (in more popular terms called the average), the median, and the mode. The IQM is a truncated mean and so is very similar to the scoring method used in sports that
- measures of statistical dispersionIn descriptive statistics, statistical dispersion (also called statistical variability is quantifiable variation of measurements of differing members of a population within the scale on which they are measured. Measures of statistical dispersion A measure
- measures of the shape of the distribution
