Quantitative Marketing Research

Quantitative market enquiry is the application of quantitative search 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 emptor and seller reach a satisfying agreement on the four Ps of marketing Product, Price, Place (location) and Promotion. As a social query method, it typically involves the construction of questionnaires and scales. People who respond (respondents) are asked to complete the survey.Marketers use the information so obtained to understand the needs of individuals in the marketplace, and to create strategies and marketing plans. Contents hide 1 Scope and requirements 2 Typical general procedure 3 Statistical analysis o3. 1 Reliability and validity o3. 2 Types of errors 4 See besides 5 List of associate topics 6 References edit Scope and requirements This section is empty. You can help by adding to it. (July 2010) edit Typical g eneral procedure Simply, there are basketball team major and most-valuable steps involved in the research process 1. Defining the Problem. 2.Research Design. 3. Data Collection. 4. psychoanalysis. 5. Report Writing & presentation. A brief discussion on these steps is 1. Problem audit and problem definition What is the problem? What are the various aspects of the problem? What information is needed? 2. Conceptualization and operationalization How exactly do we check the concepts involved? How do we translate these concepts into observable and measurable behaviours? 3. Hypothesis specification What claim(s) do we want to campaign? 4. Research design specification What type of methodology to use? examples questionnaire, survey 5.Question specification What questions to ask? In what order? 6. Scale specification How allow for preferences be rated? 7. Sampling design specification What is the total cosmos? What sample size is necessary for this population? What ingest me thod to use? examples Probability Sampling- (cluster sampling, stratified sampling, simple random sampling, multistage sampling, systematic sampling) & Nonprobability sampling- (Convenience Sampling,Judgement Sampling, goal-directed Sampling, Quota Sampling, Snowball Sampling, etc. ) 8. Data collection Use mail, telephone, internet, mall intercepts 9.Codification and re-specification Make adjustments to the raw data so it is compatible with statistical techniques and with the objectives of the research examples assigning numbers, dead body checks, substitutions, deletions, weighting, dummy variables, scale transformations, scale standardization 10. Statistical analysis Perform various descriptive and inferential techniques (see below) on the raw data. Make inferences from the sample to the livelong population. Test the results for statistical significance. 11. Interpret and integrate findings What do the results mean? What conclusions can be drawn?How do these findings relate to connatural research? 12. Write the research report Report usually has headings such as 1) executive summary 2) objectives 3) methodology 4) main findings 5) detailed charts and diagrams. Present the report to the client in a 10 minute presentation. Be prepared for questions. The design step may involve a pilot study to in order to discover each hidden issues. The codification and analysis steps are typically performed by computer, using statistical software. The data collection steps, can in some instances be automated, but often require significant manpower to undertake.Interpretation is a skill mastered only by experience. edit Statistical analysis The data acquired for quantitative marketing research can be analysed by almost any of the range of techniques of statistical analysis, which can be broadly divided into descriptive statistics and statistical inference. An important set of techniques is that related to statistical surveys. In any instance, an appropriate type of statistical analysis should take account of the various types of error that may arise, as outlined below. edit Reliability and validity Research should 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. interior consistency reliability checks how rise up the individual measures included in the research are reborn 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 chocolate-brown prediction formula to correspond to the correlation between two full-length tests. A commonly used measure is Cronbachs ? , which is equivalent to the mean of all doable 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 up 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(prenominal) 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 nomological 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 suffice the observed results? External validation checks whether the experimental results can be generalized. Validity implies reliability A valid measure must be reliable. Reliability does non necessarily imply validity, however A reliable measure does not imply that it is valid. edit Types of errors Random sampling errors sample too small sample not representative inappropriate sampling method used random errors Research design errors bias introduced measurement erro r data analysis error sampling frame error population definition error scaling error question construction error Interviewer errors recording errors cheating errors questioning errors respondent picking error Respondent errors non-response error inability error falsification error Hypothesis errors type I error (also called alpha error) othe study results lead to the rejection of the vain hypothesis regular(a) though it is actually true type II error (also called beta error) othe study results lead to the acceptance (non-rejection) of the null hypothesis even though it is actually false edit See also Choice Modelling Quantitative research Qualitative research Enterprise Feedback Management Marketing research mTAB QuestionPro Qualtrics Computer-assisted telephone interviewing Computer-assisted personal interviewing Automated computer telephone interviewing Official statistics Bureau of Labor Statistics Questionnaires Questionnaire construction Paid survey Data Mining Brand specia lism analysis NIPO Software DIY research SPSS Online panel Rating scale Master of Marketing Research Maximum Difference Preference Scaling Urtak edit List of related topics List of marketing topics List of management topics List of economics topics List of finance topics List of accounting topics edit References Bradburn, Norman M. nd Seymour Sudman. Polls and Surveys Understanding What They promise Us (1988) Converse, Jean M. Survey Research in the United States Roots and Emergence 1890-1960 (1987), the standard history Glynn, Carroll J. , Susan Herbst, Garrett J. OKeefe, and Robert Y. Shapiro. Public Opinion (1999) textbook Oskamp, Stuart and P. Wesley Schultz Attitudes and Opinions (2004) James G. Webster, Patricia F. Phalen, Lawrence W. Lichty Ratings Analysis The Theory and Practice of Audience Research Lawrence Erlbaum Associates, 2000 Young, Michael L. Dictionary of Polling The Language of Contemporary Opinion Research (1992)