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.
The foundation of good market research is good data. And good market research helps you understand and manage issues, determine and pinpoint shifts in attitude and opinion, and enhance your communications efforts. To get reliable statistical results, it’s important to survey people in fairly large numbers and to make sure they are a representative sample of your target market.
IDEALWEEN takes great care with data collection. We offer a complete suite of data collection methods in-house to ensure unparalleled quality control. Our interviewers are thoroughly trained in consumer and business-to-business interviewing. And because we control the cost, quality, and the speed of our data collection, you get results you can count on.
Research will 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 homological 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.