Quantitative Marketing Research

Quantitative marketing inquiry is the application of quantitative research techniques to the field of marketing. It has roots in twain the positivist view of the world, and the upstart marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the four Ps of marketing Product, Price, Place (location) and Promotion. As a amicable research method, it typically involves the eddy of questionnaires and graduated tables. People who respond (respondents) ar asked to do the survey.Marketers use the information so obtained to belowstand the needs of individuals in the marketplace, and to wee strategies and marketing plans. Contents hide 1 Scope and requirements 2 natural ordinary procedure 3 Statistical psychoanalysis o3. 1 dependableness and rigour o3. 2 Types of fallacys 4 See also 5 joust of colligate topics 6 References edit Scope and requirements This section is empty. You bed help by adding to it. (July 2010) edit Typical general procedure Simply, there are five major and fundamental step involved in the research process 1. Defining the Problem. 2.Research Design. 3. selective information Collection. 4. Analysis. 5. Report Writing & presentation. A brief discussion on these steps is 1. Problem audit and problem definition What is the problem? What are the motley aspects of the problem? What information is needed? 2. Conceptualization and operationalization How exactly do we desexualise the concepts involved? How do we translate these concepts into observable and measurable behaviours? 3. meditation condition What claim(s) do we want to test? 4. Research design specification What suit of methodology to use? examples questionnaire, survey 5.Question specification What questions to ask? In what inn? 6. Scale specification How will preferences be rated? 7. Sampling design specification What is the total population? What take in size is necessary for this popula tion? What ingest method to use? examples Probability Sampling- (cluster take, stratified sampling, simple haphazard sampling, multistage sampling, systematic sampling) & Nonprobability sampling- (Convenience Sampling,Judgement Sampling, Purposive Sampling, Quota Sampling, Snowball Sampling, etc. ) 8. selective information assembly Use mail, shout, internet, mall intercepts 9.Codification and re-specification Make adjustments to the raw data so it is harmonious with statistical techniques and with the objectives of the research examples assigning numbers, accord checks, substitutions, deletions, weighting, dummy variable quantitys, scale transformations, scale idealization 10. Statistical analysis Perform various descriptive and inferential techniques (see below) on the raw data. Make inferences from the sample to the whole population. Test the results for statistical significance. 11. Interpret and integrate findings What do the results mean? What conclusions can be cadaverous?How do these findings relate to comparable research? 12. Write the research subject field 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 attain any 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 work force to under address.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 speaking 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 defect 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 call forth 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. Internal consistency reliability checks how easily the individual measures included in the research are converted into a composite measure.Internal consistency may be assessed by correlating performance on two halves of a test (split-half reliabilit y). The value of the Pearson product-moment cor apprisal coefficient coefficient is adjusted with the SpearmanBrown prediction formula to correspond to the correlation mingled with two full-length tests. A commonly used measure is Cronbachs ? , which is alike to the mean of all possible split-half coefficients. Reliability may be alter by increasing the sample size. Validity asks whether the research measured what it think to. Content validation (also called face validity) checks how well the content of the research are related to the variables to be studied it seeks to answer whether the research questions are example of the variables organism researched. It is a demonstration that the items of a test are haggard 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 the object is to establish predictive validity. Construct validation checks what underlyin g 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 cause the observed results? External validation checks whether the experimental results can be generalized. Validity implies reliability A valid measure must(prenominal) be reliable. Reliability does not 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 rand om errors Research design errors bias introduced beat 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) othe study results lead to the rejection of the cypher speculation even 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 extract Modelling Quantitative research Qualitative research Enterprise Feedback centering Marketing research mTAB QuestionPro Qualtrics Computer-assisted telephone interviewing Computer-assisted personal interviewing Automated computer telephone interviewing Official statistics Bureau of Labor Statistics Questionnaires Quest ionnaire construction Paid survey Data Mining Brand strength analysis NIPO Software DIY research SPSS Online beautify Rating scale Master of Marketing Research Maximum difference of opinion 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 invoice topics edit References Bradburn, Norman M. nd Seymour Sudman. Polls and Surveys Understanding What They Tell Us (1988) Converse, denim 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 picture (1999) textbook Oskamp, Stuart and P. Wesley Schultz Attitudes and Opinions (2004) James G. Webster, Patricia F. Phalen, Lawrence W. Lichty Ratings Analysis The possibleness and Practice of Audience Research Lawrence Erlbaum Associates, 2000 Young, Michael L. Dictionary of Polling The Language of Contemporary Opinion Research (1992)