Chapter Three Research Design

Research Design: Definition A research design is a framework or blueprint for conducting the marketing research project. It details the procedures necessary for obtaining the information needed to structure or solve marketing research problems.

Components of a Research Design Define the information needed (Chapter 2) Design the exploratory, descriptive, and/or causal phases of the research (Chapters 3 - 7) Specify the measurement and scaling procedures (Chapters 8 and 9) Construct and pretest a questionnaire (interviewing form) or an appropriate form for data collection (Chapter 10) Specify the sampling process and sample size (Chapters 11 and 12) Develop a plan of data analysis (Chapter 14)

A Classification of Marketing Research Designs 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

Exploratory research design Exploratory research is used in situations where the problem may have to be defined more precisely, relevant courses of action identified, hypotheses formulated, or additional insights gained before an approach can be developed.

Exploratory research design The owner of The Cupcake King has many, many ideas for improving the bakery's sales, but isn't sure which will work. They think increasing the flavors of cupcakes the bakery sells will bring in more customers, but know they need more information. They intend to conduct ER to investigate whether expanding their cupcake selection will lead to an increase in sales, or if there is a better idea.

Methods of Exploratory Research Survey of experts (discussed in Chapter 2) Pilot surveys (discussed in Chapter 2) Secondary data analyzed in a qualitative way (discussed in Chapter 4) Qualitative research (discussed in Chapter 5) Pilot survey tend to be less structured than large scales survey in that they generally contain more open-ended questions and the sample size is much smaller.

Uses of Exploratory Research Formulate a problem or define a problem more precisely Identify alternative courses of action Develop hypotheses Isolate key variables and relationships for further examination Gain insights for developing an approach to the problem Establish priorities for further research

Conclusive research designs Conclusive research would be used to test specific hypotheses, examine specific relationships, or make predictions. Conclusive research is typically more formal and structured than exploratory research. It is based on large and representative samples and the data obtained are subjected to quantitative analysis. Conclusive research may either describe or uncover causal relationships that may be generalised to large populations

Descriptive and Causal research Descriptive research is used to describe something, usually market characteristics or functions. For example, determining the average age of purchasers of your product. Causal research is used to obtain evidence regarding cause-and-effect relationships. For example, determining if increased advertising spending has led to an increase in sales.

Six W’s of descriptive research Descriptive research design requires a clear specification of the six W’s of the research: 1.Who: who should be considered? 2.Where: where should the respondents be contacted to obtain the required information? 3.When: when should the information be obtained from the respondents? 4.What: what information should be obtained from the respondents? 5.Why: why are we obtaining information from the respondents? 6.Way: the way in which we are going to obtain information from the respondents.

Use of Descriptive Research To describe the characteristics of relevant groups, such as consumers, salespeople, organizations, or market areas To estimate the percentage of units in a specified population exhibiting a certain behavior To determine the perceptions of product characteristics To determine the degree to which marketing variables are associated To make specific predictions

Methods of Descriptive Research Secondary data analysis (discussed in Chapter 4) Primary data: Surveys, Observations (Chapter 6)

Situations where causal research could be used Causal research is appropriate to use when the purposes are to understand which variables are the cause and which variables are the effect, and to determine the nature of the functional relationship between the causal variables and the effect to be predicted. understand which variables are the cause (independent variables) and which variables are the effect (dependent variables) of marketing phenomena; determine the nature of the relationship between the causal variables and the effect to be predicted; test hypotheses.

Exploratory & Conclusive Research Differences 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 Table 3.1

A Comparison of Basic Research Designs Objective: Characteristics: Methods: Discovery of ideas and insights Flexible, versatile Often the front end of total research design Expert surveys Pilot surveys Case studies Secondary data: qualitative analysis qualitative research Describe market characteristics or functions Marked by the prior formulation of specific hypotheses Preplanned and structured design quantitative analysis Surveys Panels Observation and other data Determine cause and effect relationships Manipulation of independent variables, effect on dependent variables Control mediating variables Experiments Exploratory Descriptive Causal Table 3.2

Cross-Sectional Designs In single cross-sectional designs, there is only one sample of respondents and information is obtained from this sample only once. In multiple cross-sectional designs, there are two or more samples of respondents, and information from each sample is obtained only once. Often, information from different samples is obtained at different times. Cohort analysis consists of a series of surveys conducted at appropriate time intervals, where the cohort serves as the basic unit of analysis. A cohort is a group of respondents who experience the same event within the same time interval.

Cohort analysis Cohort analysis is of special interest because it is used to predict changes in consumers' behaviour or attitudes over a period of time. It reveals the shared history and subtle effects of the ageing of consumers upon their behaviour and attitudes.

Longitudinal Designs A fixed sample (or samples) of population elements is measured repeatedly on the same variables A longitudinal design differs from a cross-sectional design in that the sample or samples remain the same over time

Relative Advantages and Disadvantages of Longitudinal and Cross-Sectional Designs 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

Potential sources of error can affect a research design 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.

Potential Sources of Error in Research Designs 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

Potential sources of error can affect a research design The potential sources of error can be broadly categorised into two classes: Random Sampling errors. Sampling error arises when the selected sample is not perfectly representative of the population it represents. In this case the mean value for the sample differs from the actual population mean, because particular types of participant have been over- or under-represented. Non-sampling errors. Non-sampling error can be classified as non-response error and response error. Non-response error occurs when some of the participants do not respond.

Potential sources of error can affect a research design Response errors are those that arise due to errors made by the researchers, interviewers and participants, such as the wrong formulation of the questionnaire, mis-recording of answers, hesitancy or unwillingness to provide answers.