Quantitative marketing research
From Academic Kids

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.
Contents 
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.
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 sampling
 Data collection  Use mail, telephone, Internet, mall intercepts. May be a custom survey, or added to an omnibus survey
 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 10 minute presentation. Be prepared for questions.
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 tendency
 measures of statistical dispersion
 measures of the shape of the distribution
 estimate the values of certain parameters which summarize the data
Inferential techniques
Inferential techniques involve generalizing from a sample to the whole population. It also involves testing a hypothesis. A hypothesis must be stated in mathematical/statistical terms that make it possible to calculate the probability of possible samples assuming the hypothesis is correct. Then a test statistic must be chosen that will summarize the information in the sample that is relevant to the hypothesis. A null hypothesis is a hypothesis that is presumed true until a hypothesis test indicates otherwise. Typically it is a statement about a parameter that is a property of a population. The parameter is often a mean or a standard deviation.
Not unusually, such a hypothesis states that the parameters, or mathematical characteristics, of two or more populations are identical. For example, if we want to compare the test scores of two random samples of men and women, the null hypothesis would be that the mean score in the male population from which the first sample was drawn, was the same as the mean score in the female population from which the second sample was drawn:
 <math>H_0: \mu_1 = \mu_2<math>
where:
 H_{0} = the null hypothesis
 μ_{1} = the mean of population 1, and
 μ_{2} = the mean of population 2.
The equality operator makes this a twotailed test. The alternative hypothesis can be either greater than or less than the null hypothesis. In a onetailed test, the operator is an inequality, and the alternative hypothesis has directionality:
 <math>H_0: \mu_1 =or< \mu_2<math>
These are sometimes called a hypothesis of significant difference because you are testing the difference between two groups with respect to one variable.
Alternatively, the null hypothesis can postulate that the two samples are drawn from the same population:
 <math>H_0: \mu_1  \mu_2 = 0<math>
A hypothesis of association is where there is one population, but two traits being measured. It is a test of association of two traits within one group.
The distribution of the test statistic is used to calculate the probability sets of possible values (usually an interval or union of intervals). Among all the sets of possible values, we must choose one that we think represents the most extreme evidence against the hypothesis. That is called the critical region of the test statistic. The probability of the test statistic falling in the critical region when the hypothesis is correct is called the alpha value of the test. After the data is available, the test statistic is calculated and we determine whether it is inside the critical region. If the test statistic is inside the critical region, then our conclusion is either the hypothesis is incorrect, or an event of probability less than or equal to alpha has occurred. If the test statistic is outside the critical region, the conclusion is that there is not enough evidence to reject the hypothesis.
The significance level of a test is the maximum probability of accidentally rejecting a true null hypothesis (a decision known as a Type I error).For example, one may choose a significance level of, say, 5%, and calculate a critical value of a statistic (such as the mean) so that the probability of it exceeding that value, given the truth of the null hypothesis, would be 5%. If the actual, calculated statistic value exceeds the critical value, then it is significant "at the 5% level".
Types of hypothesis tests
 Parametric tests of a single sample:
 t test
 z test
 Parametric tests of two independent samples:
 twogroup t test
 z test
 Parametric tests of paired samples:
 paired t test
 Nominal/ordinal level test of a single sample:
 chisquare
 KolmogorovSmirnov one sample test
 runs test
 binomial test
 Nominal/ordinal level test of two independent samples:
 chisquare
 MannWhitney U
 Median
 KolmogorovSmirnov two sample test
 Nominal/ordinal level test for paired samples:
 Wilcoxon test
 McNemar test
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 SpearmanBrown 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. Are the research questions 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. They are 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. 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. But reliability does not necessarily imply validity :a reliable measure need not be 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