- Problem Definition
- Exploratory Research
- Secondary Data
Facts and figures that have been recorded before the project at hand. [1]
- Focus Groups
An informal session of 6-10 customers - past, present, or prospective - in which a discussion leader, or moderator, asks their opinions about he firm's and its competitors' products. [1:1]
- Depth Interviews
Detailed, individual interviews with people relevant to a research project. [1:2]
- Formal Research Design
- Survey
A research technique used to generate data by asking people questions and recording their responses on a questionnaire. [1:3]
- Experiment
Obtaining data by manipulating factors under tightly controlled conditions to test cause and effect. [1:4]
- Observation
Watching, either mechanically or in person, how people behave. [1:5]
- Sampling
- Probability
Using precises rules to select the sample such that each element of the population has a specific known chance of being selected. [1:6]
- Nonprobability
Using arbitrary judgements to select the sample so that the chance of selecting a particular element may be unknown or zero. [1:7]
- Data Collection and Analysis
- Conclusions and report
*Steps in bold are critical decision points in the process which will be further explained below
Problem Definition
We need to identify a problem, clearly define the scope of the problem, and factors influencing our marketing problem to guide our research efforts.
Formal Research Design
At this stage we outline the methods and procedures we will use to analyze and sort our collected data.
Formal Research Design Plan Outline
-
Objectives of the Research
Definition of marketing problems, opportunities, areas of improvement, and goal metrics.
-
Information and Data Sources
Internal and external sources of information and data to be used in the research process.
-
Research Methods
Surveys, experiments, and observation of target audiences in varying channels. Surveys should follow the three stage theory ( Screening/Rapport, Product Specific Questions, Demographic Questions ). [2] Keep questions short readable questions with under 20 words. Avoid leading and/or loaded questions to improve reliability of data collected. [2:1]
-
Sampling Plan
Selective, and hopefully, representative sampling focused on reliability of data and methodical testing using statistically appropriate methods.
-
Schedule and Cost of Research
Time and cost based analysis of the research tasks, analysis, and marketing actions required to meaningfully interpret and implement impactful solutions.
Data Collection and Analysis
The keys to successful relevant data collection and analysis is to plan on ways to minimize errors and maintain focus on the core research problems and informational value to current and future decision making.
Generalizability
The ability to make inferences from the sample data collected from our target sample in relation to the general population sentiment. [3]
Reliability
The ability for the data to be measured to produce consistent results whether with the same sample or a similar sample. [3:1]
- Test-retest reliability
Pearson Correlation evaluates whether there is statistical evidence for a linear relationship among the same pairs of variables in the population, represented by a population correlation coefficient, ρ (“rho”). The Pearson Correlation is a parametric measure. Also known as Pearson Product-Moment Correlation (PPMCC)[4]
Stability of results collected in similar circumstances over repeated measurements compared to the Pearson Correlation coefficient of previous results. [3:2]
- Alternative forms reliability
Testing consistency across different forms of research methods to find similarities and/or outliers occurring in differing research methods.
- Internal consistency reliability checks
Spearman-Brown Prediction Formula is used to predict the reliability of a test after changing the test length and can be used to understand the relation between test reliability and test length. [5]
Validity
- Content Validation ( Face Validity )
Checks relevance between between collected research data and the variables to be studied. The main focus is to determine whether the research questions are relevant to the variables being researched. Ensures that the elements of the research are within the domain of the research study.
- Criterion Validation
Checks how meaningful the research criteria are relative to other possible criteria. When the data is collected later the goal is to establish predictive validity. [3:3]
- Construct Validation
Makes sure the underlying construct validity is relevant for measurement. Three variants Convergent (research relationship to other measures of the same construct), discriminant (quality of reseaearch relationship to opposing constructs measurements), and nomological (research relationship to other variables required by statistical theories).
- Internal Validation
Analyzing the relationship between the dependent and independent variables used in research methods to find sampling errors.
- External Validation
Check for potential for experimental results to be generalized.
Note: A reliable measure does not imply that it is valid [3:4]
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
Above errors list referenced from Quantitative Marketing Research Wiki [3:5]
Conclusions and Report
Review the analysis of the data to interpret the information and its relevancy to the core business and marketing goals driving the consumer action.
-
Referenced from "Crane, F. G., Kerin, R. A., Rudelius, W., & Hartley, S. W. (2014). Marketing (9th ed.). Whitby, Ontario: McGraw-Hill Ryerson". ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎↩︎ ↩︎
-
Referenced from "https://en.wikipedia.org/wiki/Questionnaire_construction" ↩︎ ↩︎
-
Referenced from "https://en.wikipedia.org/wiki/Quantitative_marketing_research" ↩︎ ↩︎ ↩︎ ↩︎ ↩︎ ↩︎
-
Referenced from "https://libguides.library.kent.edu/SPSS/PearsonCorr" ↩︎
-
Referenced from "https://en.wikipedia.org/wiki/Spearman–Brown_prediction_formula" ↩︎
