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Quantitative Research

Updated on September 25, 2018
 Statistics is the most important tools of data analysis for quantitative research.
Statistics is the most important tools of data analysis for quantitative research.

QUANTITATIVE RESEARCH


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:

  1. 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.
  2. Meeting and briefing with the customer.
  3. Writing research proposal in accordance with TOR or RFQ
  4. 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.
  5. Developing the questionnaire and acquire the customer for approval.
  6. Data collection period or field - work period
  7. Quality controlling procedure : at least 30% per interviewer
  8. Data processing : data entry, data cleaning and analyzing on SPSS, Anova, SAS or other effective softwares.
  9. Writing report and making presentation.

Flow Chart

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

  • Strengths
  1. Testing and validating already constructed theories about how and why phenomena occur
  2. Testing hypotheses that are constructed before the data are collected
  3. Can generalize research findings when the data are based on random samples of sufficient size
  4. Can generalize a research finding when it has been replicated on many different populations and sub-populations
  5. Useful for obtaining data that allow quantitative predictions to be made
  6. The researcher may construct a situation that eliminates the confounding influence of many variables, allowing one to more credibly establish cause-and-effect relationships
  7. Data collection using some quantitative methods is relatively quick (e.g., telephone interviews)
  8. Provides precise, quantitative, numerical data
  9. Data analysis is relatively less time consuming (using statistical software such as SPSS, ASCII, SAS, and Excel)
  10. The research results are relatively independent of the researcher (e.g., statistical significance)
  11. It may have higher credibility with many people in power (e.g., administrators, politicians, people who fund programs)
  12. It is useful for studying large numbers of people


  • Weaknesses
  1. The researcher’s categories that are used might not reflect local constituencies’ understandings
  2. The researcher’s theories that are used might not reflect local constituencies’ understandings
  3. 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)
  4. Knowledge produced might be too abstract and general for direct application to specific local situations, contexts, and individuals

Source : http://www.southalabama.edu/coe/bset/johnson/oh_master/Ch14/Tab14-01.pdf




EXAMPLE OF 1 - 10 RATING SCALE

1 - 10 rating - scaled emocons (10 moods)
1 - 10 rating - scaled emocons (10 moods)
An Interpretation of rating scale in general.
An Interpretation of rating scale in general.

Example of Data Analysis

Respondent No.
Raw Score
1
5
2
6
3
8
4
6
5
7
6
4
7
3
8
2
9
7
10
7
Total
55
Mean
5.5
Mode
7.0
Median
6.0

DATA ANALYSIS AND INTERPRETATION

SPSS, ANOVA, SAS will generate the contingency tables and tabulations.

  • Frequency
  • 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)
  • Correlation
  • Benchmark or Comparative Analysis (Vertical & Horizontal benchmark)
  • Weighted Score
  • Correlation
  • Correspondent
  • Other models


EXAMPLE OF RESEARCH FINDING IN GRAPHICAL DEMONSTRATION

Research Finding of Satisfaction survey in 5 key touch points of brand / product XXY.
Research Finding of Satisfaction survey in 5 key touch points of brand / product XXY.
Example of comparative analysis of 2 variables, Time period in comparison to SPY.
Example of comparative analysis of 2 variables, Time period in comparison to SPY.
An example of Pie Chart.
An example of Pie Chart.

Comments

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    • kesinee profile imageAUTHOR

      Sranunta Lamduan 

      2 years ago from Bangkok, Thailand

      thank you for your comments Bob. I will write additional information about Quantitative Research and hope you will give me some feedback later on.

      Best wishes,

      Jeab

    • diogenes profile image

      diogenes 

      2 years ago from UK and Mexico

      Dear author: You could have just as easily cut and pasted much of this from Wikipedia. We want to see you writing about your subject, not just facts and figures which could come from anywhere. You could have used all this information, but contained in an article about YOUR take on the benefits and pitfalls,etc., of this complex and innovative subject.

      Bob

      ps I don't have the right to judge what you do; just speaking from a long career as a writer and 12 years on here as well.

    • kesinee profile imageAUTHOR

      Sranunta Lamduan 

      2 years ago from Bangkok, Thailand

      Hi Bob,

      I wrote this hub from my career as a marketing researcher. Just want to share knowledge with others.

      Jeab

    • diogenes profile image

      diogenes 

      2 years ago from UK and Mexico

      Sorry, but this is not a hub article...

      Bob

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