Exploratory Research Design. Primary vs. Secondary data. Advantages and uses of SD

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1 Exploratory Research Design Secondary Data Qualitative Research Survey & Observation Experiments Företagsakademin, Henriksgatan 7 FIN Åbo Primary vs. Secondary data Primary data: originated by the researcher for the specific purpose addressing the research problem Secondary data: data collected for some other purpose than the problm at hand Collection Purpose Collection Process Collection Cost Collection Time Primary Data For the purpose at hand Very involved Long Secondary Data For other problems Rapid and easy Relatively low Short Advantages and uses of SD 1. Identifying the problem 2. Better define the problem 3. Develop and approach to the problem 4. Formulate an appropriate research design 5. Answering certain research Qs and test some hypotheses 6. Interpret primary data more insightfully Rule: Examination of available SD is a prerequisite to the collection of primary data. Start with SD; collect PD only after the SD source yield marginal returns 1

2 Criteria for evaluating SD Specifications/Methodolog y Data collection method Response rate Quality of data Sampling technique Sample size Questionnaire design Field work Data analysis Data should be systematic, valid, and generalizable to the problem at hand. Error/Accuracy Examine errors in: Approach, Research design, Sampling, Data collection, Data analysis and Reporting Assess accuracy by comparing data from different sources. Currency Time lag between collection and publication. Frequency of updates Census data is periodically updated by syndicated firms Criteria for evaluating SD Objective: Why were the data collected, i.e. determine the relevance of the data Nature: Definition of key variables, units of measurement, categories used, relationships examined, i.e. reconfigure the data to increase their usefulness if possible. Dependability: Expertise, credibility, reputation, and trustworthiness of the source, i.e. Data should be obtained from an original rather than an acquired source Classification of SD Internal Ready to use Requires further processing External Published materials guides directories indexes nongov t statistical data Census data Computerized databases Online/offline, internet DB, bibliograhic DB Syndicated services surveys panels scanner services audits industry services 2

3 A classification of Marketing research data Marketing Research Data Secondary Data Primary Data Qualitative Data Quantitative Data Descriptive Causal Survey Data Observational and other Data Experimental Data Primary Data: Qualitative versus Quantitative Research Qualitative research An unstructured, exploratory research methodology based on small samples that provide insights and understanding of the problem setting Quantitative research A research methodology that seeks to quantify the data and, typically applies some form of statistical analysis Qualitative research procedures: Direct, indirect Focus group: an interview conducted by a trained moderator, a small group, unstructured Association technique word associations Completion techniques sentence completion story completion Construction techniques picture response cartoon test Expressive techniques Focus Groups, Depth Interviews, projective techniques Criteria Structure Focus Groups Depth Interviews Projective techniques Probing of respondent Moderator bias Interpretation bias Subconscious Innovative info info Sensitive info Unusual behavior Usefulness No ly useful to high Some Useful to Yes Somewhat 3

4 Qualitative versus Quantitative Research Objective Sample Qualitative Research To gain a qualitative understanding of the underlying reasons and motivations Small number of nonrepresentative cases Quantitative Research To quantify the data and generalize the results from the sample to the population of interest Large number of representative cases Data collection Unstructured Structured Data analysis Non-statistical Statistical Outcome Develop an initial understanding Recommend a final course of action Descriptive Research Design: Survey & Observation Survey: A structured questionnaire given to respondents and designed to elicit specific information Structured data collection: Use of formal questionnaire that presents questions in a prearranged order Fixed-alternative questions: Respondents are required to choose from a set of predetermined answers Survey Methods: Telephone; traditional, computerassisted Personal interviewing; In-home, Mall Intercept, computerassisted Mail Interviewing; mail, mail panel Electronic Intrvieweing; , Internet Descriptive Research Design: Survey & Observation Observation: the recording of behavioral patterna of people, objects, and events in a systematic manner to obtain information about the phenomenon of interest Structured observation: observation techniques where the researcher defines the behavior to be observed and the methods by which they will be measured Unstructured observations: A researcher monitors all aspects of the phenomenon without specifying the details in advance Natural observations: observing behavior as it takes place in the environment Contrived observation: the behavior is observed in an artificial environment Disguised vs. undisguised observation 4

5 Causal Research Design: Experimentation Causality: Ordinary meaning and scientific meaning: Ordinary: X is the only cause of Y X must always lead to Y It is possible to prove that X is a cause of Y Scientific: X is only one of a number of possible causes of Y The occurence of X makes the occurence of Y more probable We can never prove the X is a cause of Y. At best we can infer that X is a cause of Y Causal Research Design: Experimentation Concomitant Variation: The extent to which a cause X and an effect Y occur together is predicted by the hypothesis under consideration Purchase of Fashion Clothing Y is influenced by education X The absence of initial evidence of concomitant variation does not imply there is no causation Can we prove high education cause high purchase? Education X Purchase of Fashion Clothing Y Total 363 (73%) 137 (27%) 500 (100%) 322 (64%) 178 (36%) 500 (100%) 5

6 What about Income? Income Purchase Income Purchase Educa -tion Total Educa -tion Total 122 (61%) 171 (57%) 78 (39%) 129 (43%) 200 (100 %) 300 (100 %) 241 (80%) 151 (76%) 59 (20%) 49 (24%) 300 (100 %) 200 (100 %) Time order of occurence of variables The causing event must occur either before or simultaneously with the effect. It cannot occur afterwards! An effect cannot be produced by an event that occur after the effect has taken place! But: A variable can be both the cause and the effect in a causal relationship! Customer who shop frequently in a store is likely to have a credit card for that store. Also customers who have the card is likely to shop there more frequently Definitions and Concepts Independent variables (treatments) (iv) are variables that are manipulated by the researcher and whose effects are measured and compared. These can be price levels, package designs, advertising themes, etc. Test unit are individuals, organizations, or other entities whose response to the independnt variables or treatments is being examined. Test units may include consumers, stores, geographic areas 6

7 Definitions and Concepts Dependent variables (dv) are the variables that measure the effect of the independent variable on the test unit. These variables may include, sales, profits, market share, brand, consumer choice, intentions to buy, etc. Extraneous or confounding variables are all variables other than the independent variables that affect the response of the test units. These variables can confound the dv. measures in a way that weakens or invalidates the results, e.g. store size, location, competitive effort. These variables have to be controlled Definitions and Concepts Experiment is formed when the researcher manipulates one or more iv. and measures their effect on one or more dv. while controlling for the effect of confounding variables Experimental design is a set of procedures specifying the test units and how they are to be divided into homogeneous subsamples what iv is to be manipulated what dv are to be measured how the confounding variabls are to be controlled Validity Internal validity is a measure of accuracy of an experiment. It measures whether an iv actually causes the effects on the dv External validity determines whether the cause-and-effect found in the experiment can be generalized 7

8 Confounding variables History: events that are external to the experiment but occur a the same time Maturation: changes in the test unit due to passage of time Testing effect: main testing effect interactive testing effect Main testing effect: when a prior observation affects the latter. Affects the internal validity interactive testing effect: prior measurement affects the test unit s response to the iv. Affects the external validity Confounding variables Statistical regression: when respondents with extreme scores move closer to the average, i.e. the change in attitude is attributable to statistical regression rather than the treatment Selection bias: Improper assignment of test units to treatment Mortality: loss of a test unit while the experiment is in progress Controlling confounding variables Randomization Matching Statistical control: measuring the confounding variable and adjusting for their effects Design control 8

9 Classification of experimental designs Experimetal designs Preexprimental True Experimental Quasi experimental Statistical One-shot case study One group pre-posttest Static group Pretest-posttest control group Posttest-only control group Solomon Four group Time Series Multiple Time Series Randomized Blocks Latin Square Factorial Classification of experimental designs Preexperimental design do not control for confounding factors by randomization True experimental designs the researcher can randomly assign test units to experimental groups and assign treatment randomly to experimental groups quasi-experimental design is like the true experimental design BUT without full experimental control Limitations of experimentation Time Cost Administration Application: Test marketing controlled test market simulated test market 9

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