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2012 data analysis


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2012 data analysis

  1. 1. DATA ANALYSIS & INTERPRETATION Adapted from J&C Research Consultants Pte. Ltd.
  2. 2. REVIEWING THE RESEARCH PROCESS Topic Identification Choosing a Research Topic (Assignment 1) Methodology Social Survey (Assignment 2) Interview (Assignment 3) Data Analysis & Interpretation Report Writing (Assignment 4)
  3. 3. Key Objectives <ul><li>How to conduct an in-depth analysis on your collected data </li></ul><ul><li>How to report your results </li></ul>
  4. 4. On Data Collection and Analysis <ul><li>You are travelling to a country you have never been before for a two-week holiday! </li></ul><ul><li>Que: What data would you need to decide what to wear? </li></ul>Suggested Ans: Perhaps: The season of the year What the weather will look like Which clothes fit the weather condition What will be done during the day
  5. 5. Data Collection and Analysis Continued <ul><li>Que: How will you analyse your data to determine the best course of action? </li></ul><ul><li>Supposing it is summer/winter </li></ul><ul><li>Suggested Ans: </li></ul><ul><li>You decide to go only with t-shirts </li></ul><ul><li>You bring some sweater with you as you will be in an air-conditioned room </li></ul>
  6. 6. Summary <ul><li>Data analysis helps us to: </li></ul><ul><li>Know important facts about our object of study </li></ul><ul><li>Uncover trends that we might not otherwise have known </li></ul><ul><li>Make better decisions </li></ul>Note: the analogy above is a simple way of showing how scientists go about data collection, analysis and interpretation
  7. 7. What then is data analysis? <ul><li>The process of arranging, summarising, and transforming data into information. </li></ul>Note: Data collection and analysis is a systematic process
  8. 8. A Case Study of Previous RE Project <ul><li>Survey of students at a school exhibition on groundbreaker, John Nash </li></ul><ul><li>Objective: To showcase the life and work of John Nash </li></ul>Survey
  9. 9. Evaluating the Survey <ul><li>Evaluate the quality of this survey: </li></ul><ul><li>What objective(s) is the survey trying to accomplish? </li></ul><ul><li>Does the survey questions meet those objectives? </li></ul><ul><li>Did the survey operationalise the key concept in the project topic well? (WHAT IS THIS!) </li></ul><ul><li>Do you think the survey will facilitate the collection of the right data? </li></ul><ul><li>Will the students generate sufficient data to reach the right conclusion? </li></ul>
  10. 10. Operationalisation of the key concept(s) <ul><li>The process of turning abstract concepts into a concrete and measurable concept. </li></ul><ul><li>To operationalise a concept, ask </li></ul><ul><li>What do I mean by the concept? </li></ul><ul><li>Do my survey questions define the concept in a way that will facilitate the collection of a good (measurable) data? </li></ul>
  12. 12. Processing the Data <ul><li>Give each completed survey a serial number. </li></ul><ul><li>Code the responses (This can be skipped if you number all your response options in the questionnaire) </li></ul><ul><li>Set up an excel spreadsheet as follows: </li></ul><ul><li>Column – Question no. </li></ul><ul><li>Row – Survey serial no. </li></ul><ul><li>For each survey, key in the completed responses into the excel spreadsheet. </li></ul><ul><li>Check the data </li></ul><ul><li>Analyse the responses of each question (using the count function or sort and count). </li></ul>
  13. 13. Analysing Quantitative Survey Data (AQSD) <ul><li>The following is important: </li></ul><ul><li>a) Demographic variables </li></ul><ul><li>(e.g. profile of your respondents in terms of age, gender, income, education etc) </li></ul><ul><li>b) Answers to your research questions </li></ul>
  14. 14. AQSD Continued <ul><li>c) Relationships between two variables </li></ul><ul><li>You can do more in-depth data analysis by: </li></ul><ul><li>Analysing the data by subgroups (e.g. comparing males and females on Question 1) </li></ul><ul><li>Analysing two questions (e.g. for whose who responded “Yes” to Question 1, how do their responses differ for Question 2?) </li></ul>
  16. 16. Reporting your Results <ul><li>State: </li></ul><ul><li>(i) Research objective clearly </li></ul><ul><li>(ii) Describe your methodology </li></ul><ul><li>(iii) Report your results </li></ul>Problem: Some students’ reports leave out the method section. Implication: A sudden presentation of results without an explanation of where the data came from . This is a poor use of data to support an argument.
  17. 17. An Example <ul><li>Better way to report your results: </li></ul><ul><li>AN EXAMPLE: </li></ul><ul><li>Research objective: To explore the marketability of our new proposed product. </li></ul><ul><li>Methodology : A survey was conducted with 120 respondents from RI. The respondents were all males, aged … (Describe the survey sample) </li></ul><ul><li>Results: Results showed that … </li></ul>
  18. 18. The Order of a Good Report <ul><li>Present a finding by: </li></ul><ul><li>First, state the conclusion </li></ul><ul><li>Second, support your statement with relevant data or the results from your analysis. </li></ul><ul><li>Third, make inferences from and interpret your data and. </li></ul>
  19. 19. From Previous Example <ul><li>Results showed that males were willing to pay more for the new proposed product than females [ conclusion from the data ]. </li></ul><ul><li>On average, male respondents indicated that they are willing to spend $35 on the product, as opposed to females who are willing to pay only $25 [ relevant data ]. </li></ul><ul><li>Hence, it is recommended that marketing strategies should focus more on male consumers who are willing to pay more for the product [ interpretation/inference ]. </li></ul>
  20. 20. Note <ul><li>Difference between reporting the results and interpreting it </li></ul><ul><li>Reporting is describing what the data show (e.g. On average, males were willing to spend $35 on the new product while females will pay only $25) </li></ul><ul><li>Interpretation involves a discussion of the implications (i.e. should focus marketing efforts on male consumers). </li></ul>
  21. 21. Reporting Your Results <ul><li>Use figures purposefully </li></ul><ul><li>Data with only two categories (such as Yes/No and Aware/Not aware) do not require a figure. </li></ul>
  22. 22. Reporting Your Results <ul><li>Use figures purposefully </li></ul><ul><li>Use bar charts instead of pie charts to present information more clearly. </li></ul>Copyright (c) 2007 J&C Research Consultants Pte. Ltd.
  23. 23. Reporting Your Results Copyright (c) 2007 J&C Research Consultants Pte. Ltd. Figure 2. Reason that captured respondents' attention
  24. 24. Reporting Your Results <ul><li>Use figures purposefully (cont’) </li></ul><ul><li>Note that pie charts are seldom used in academic reports </li></ul><ul><li>Captions for figures are placed at the bottom , not at the top. </li></ul>Copyright (c) 2007 J&C Research Consultants Pte. Ltd.
  25. 25. REPORTING YOUR RESULTS Figure 2. Reason that captured respondents' attention
  26. 26. Common Mistakes to Avoid <ul><li>Do not change data </li></ul><ul><li>Do not alter data to compensate for bad survey design </li></ul><ul><li>Do not project your data to people that do not respond to your questionnaire </li></ul>
  27. 27. Reporting Your Results <ul><li>Honesty is the best policy: Integrity issues when reporting results </li></ul><ul><li>If the results did not turn out as expected, this is also a finding which means you might have to rethink your prior assumptions . </li></ul><ul><li>Do NOT falsify your results </li></ul><ul><li>A plausible reason for unexpected results is your methodology , e.g. convenience sampling (i.e. asking friends to respond to the survey), administering of survey, how questions were phrased, etc </li></ul>
  28. 28. Reporting Your Results <ul><li>Honesty is the best policy: Integrity issues when reporting results (cont’) </li></ul><ul><li>Discuss what was learnt from the research process and offer suggestions for how the project could be improved upon in future research </li></ul>
  29. 29. Further Instructions on Data Analysis <ul><li>On step-by-step instructions, see Project Works Vol 3, pages 25-33. </li></ul><ul><li>On presenting your results, see Project Works Vol 3, pages 34-41. </li></ul>
  30. 30. END OF LECTURE