Quantitative research in social research is a systematic empirical observation via statistics, mathematics, and computational techniques. The objectives of quantitative research is to develop and employ mathematical models, theories and/or hypotheses pertaining phenomena.
MARKETING RESEARCH FLOW CHART
Regarding to my previous experiences in quantitative research as a Marketing Researcher, typically, there are 9 key activities in conducting marketing research as a routine job as follow:
- Obtaining Terms of Reference (TOR) or Request for Quotation (RFQ) which states research terms, objectives, methodologies, time period, target respondents, and budgets subject to research topic (project) from the customer.
- Meeting and briefing with the customer.
- Writing research proposal in accordance with TOR or RFQ
- Research proposal submission (if the research proposal is selected, the project will be commissioned and will be launched as issued date and time as on the research proposal.
- Developing the questionnaire and acquire the customer for approval.
- Data collection period or field - work period
- Quality controlling procedure : at least 30% per interviewer
- Data processing : data entry, data cleaning and analyzing on SPSS, Anova, SAS or other effective softwares.
- Writing report and making presentation.
QUANTITATIVE MECHANICS AND TECHNIQUES
There are several quantitative methodologies applying in the data - collection procedure. Each quantitative method and technique is slightly different in terms of research conditions, processes, conveniences, budgets, target groups, and time constraints.
- Face - to - Face Interview
- Computer Assistant Technique Interview (CATI)
- Telephone Interview
- Website and E-Mailing Interview
- Exit Interview
- Intercept Interview
- Central Location Interview (CLT)
STRENGTHS AND WEAKNESSES OF QUANTITATIVE METHODOLOGIES
- Testing and validating already constructed theories about how and why phenomena occur
- Testing hypotheses that are constructed before the data are collected
- Can generalize research findings when the data are based on random samples of sufficient size
- Can generalize a research finding when it has been replicated on many different populations and sub-populations
- Useful for obtaining data that allow quantitative predictions to be made
- The researcher may construct a situation that eliminates the confounding influence of many variables, allowing one to more credibly establish cause-and-effect relationships
- Data collection using some quantitative methods is relatively quick (e.g., telephone interviews)
- Provides precise, quantitative, numerical data
- Data analysis is relatively less time consuming (using statistical software such as SPSS, ASCII, SAS, and Excel)
- The research results are relatively independent of the researcher (e.g., statistical significance)
- It may have higher credibility with many people in power (e.g., administrators, politicians, people who fund programs)
- It is useful for studying large numbers of people
- The researcher’s categories that are used might not reflect local constituencies’ understandings
- The researcher’s theories that are used might not reflect local constituencies’ understandings
- The researcher might miss out on phenomena occurring because of the focus on theory or hypothesis testing rather than on theory or hypothesis generation (called the confirmation bias)
- Knowledge produced might be too abstract and general for direct application to specific local situations, contexts, and individuals
EXAMPLE OF 1 - 10 RATING SCALE
Example of Data Analysis
DATA ANALYSIS AND INTERPRETATION
SPSS, ANOVA, SAS will generate the contingency tables and tabulations.
- Mean score (μ) is an average score
Equation: ∑(a+b+c+,......,+n) ÷ total number of respondents (n)
Example : There are 10 respondents response questionnaire number 9 as below :
Respondent Response Mean score computation
- Mode = 7
- Median = 6
- Cross - Tabulation (Cross-Tab)
- Linear Regression Model (will be demonstrate to the customer if required)
- Benchmark or Comparative Analysis (Vertical & Horizontal benchmark)
- Weighted Score
- Other models