A framework or blueprint for conducting the M.R project.
Specifies the details of the procedures necessary for obtaining information needed to structure &/ or solve the M.R problem.
A research design lays the foundation for conducting the research.
Design the exploratory, descriptive &/ or causal phases of the research.
Define the information needed.
Specify the measurement & scaling procedures.
Construct & pretest a questionnaire for data collection.
Specify the sampling process & sample size.
Develop a plan of data analysis.
Single Cross-Sectional Design Multiple Cross-Sectional Design Fig. 3.1 Research Design Conclusive Research Design Exploratory Research Design Descriptive Research Causal Research Cross-Sectional Design Longitudinal Design
Objective: Character-istics: Findings/ Results: Outcome: To provide insights and understanding Information needed is defined only loosely. Research process is flexible and unstructured. Sample is small and non-representative. Analysis of primary data is qualitative Tentative Generally followed by further exploratory or conclusive research To test specific hypotheses and examine relationships Information needed is clearly defined. Research process is formal and structured. Sample is large and representative. Data analysis is quantitative Conclusive Findings used as input into decision making Exploratory Conclusive Table 3.1
Provides insights into & comprehension of the problem situation confronting the researcher.
It is used when the problem is to be defined more precisely.
The information needed at this stage is loosely defined.
The research process is flexible & unstructured.
Findings are tentative or as input to further research.
E.g.: P.I with industry experts.
Formulate a problem or define a problem more precisely.
Identify alternative courses of action.
Gain insights for developing an approach to the problem.
Establish priorities for further research .
Survey of experts
Secondary data analyzed in a qualitative way.
It provides information that helps the executive make a rational decision.
It is the research designed to assist the decision maker in determining, evaluating & selecting the best course of action to take in a given situation.
Information needed is clearly defined.
Research process is formal & structured.
Findings are used as input to decision making.
Types: Descriptive & Causal/ experimental
A type of conclusive research that has as its major objective the description of something- usually market characteristics or functions.
To describe characteristics of relevant groups. e.g., profile of heavy users
To estimate a certain behavior in a specified population. E.g., percentage of heavy users
To determine the perception of product characteristics.
To make specific predictions.
To determine the degree to which marketing variables are associated. e.g., shopping and eating out.
-who, what, when, where, why, and way of the research.
Secondary data analyzed in a quantitative way.
Observational & other data
1.Cross-sectional design : Research Design involving the collection of information from any given sample of population elements only once.
Single cross- sectional design : Only one sample of respondents is drawn from the target population & information is obtained from this sample only once.
Multi cross- sectional design : A design in which there are two or more samples of respondents, & information from each sample is obtained only once.
Cohort analysis. E.g., consumption of soft drinks.
Figure 3.6 Cross Sectional vs. Longitudinal Designs Sample Surveyed at T 1 Sample Surveyed at T 1 Same Sample also Surveyed at T 2 T 1 T 2 Cross- Sectional Design Longitudinal Design Time
2. Longitudinal Designs: A research design involving a fixed sample of population elements that is measured repeatedly on the same variables.
The sample remains the same over time, thus providing a series of pictures which when viewed together portray an illustration of the situation & the changes that are taking place over time .
Evaluation Criteria Cross-Sectional Design Longitudinal Design Detecting Change Large amount of data collection Accuracy Representative Sampling Response bias - - - + + + + + - - Note: A “+” indicates a relative advantage over the other design, whereas a “-” indicates a relative disadvantage. Table 3.4
Brand Purchased Time Period Period 1 Period 2 Survey Survey Brand A 200 200 Brand B 300 300 Brand C 500 500 Total 1000 1000 Table 3.5
Brand Purchased in Period 1 Brand Purchased in Period 2 Brand A Brand B Brand C Total Brand A Brand B Brand C Total 100 25 75 200 50 100 150 300 50 175 275 500 200 300 500 1000 Table 3.6
A type of conclusive research where the major objective is to obtain evidence regarding cause-and-effect (causal) relationship.
It requires a planned & structured design.
To understand which variables are cause (independent variables) & which variables are the effect (dependent variables) of a phenomenon.
To determine the nature of the relationship between the causal variables & the effect to be predicted.
Objective To provide insights and To test specific hypothesis and understanding examine relationships
Characteristics Information needed is defined Information needed is clearly defined.
only loosely. Research process Research process is formal and is flexible and unstructured. structured. Sample is large and
Sample is small and non representative. Data analysis is representative. Analysis quantitative. of primary data is qualitative.
Findings / Results Tentative Conclusive
Outcome Generally followed by further Findings used as input into decision
exploratory or conclusive research. making.
Secondary Data Analysis
Secondary Data Analysis
(a) (b) (c)
Surrogate Information Error Measurement Error Population Definition Error Sampling Frame Error Data Analysis Error Respondent Selection Error Questioning Error Recording Error Cheating Error Inability Error Unwillingness Error Fig. 3.2 Total Error Non-sampling Error Random Sampling Error Non-response Error Response Error Interviewer Error Respondent Error Researcher Error
The total error is the variation between the true mean value in the population of the variable of interest and the observed mean value obtained in the marketing research project.
Random sampling error is the variation between the true mean value for the population and the true mean value for the original sample.
Non-sampling errors can be attributed to sources other than sampling, and they may be random or nonrandom: including errors in problem definition, approach, scales, questionnaire design, interviewing methods, and data preparation and analysis. Non-sampling errors consist of non-response errors and response errors.
Non-response error arises when some of the respondents included in the sample do not respond.
Response error arises when respondents give inaccurate answers or their answers are misrecorded or misanalyzed.