2012 data analysis
Upcoming SlideShare
Loading in...5

2012 data analysis






Total Views
Views on SlideShare
Embed Views



2 Embeds 74

http://ri.sg.libguides.com 73
http://libraryschool.campusguides.com 1


Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

2012 data analysis 2012 data analysis Presentation Transcript

  • DATA ANALYSIS & INTERPRETATION Adapted from J&C Research Consultants Pte. Ltd.
  • 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)
  • Key Objectives
    • How to conduct an in-depth analysis on your collected data
    • How to report your results
  • On Data Collection and Analysis
    • You are travelling to a country you have never been before for a two-week holiday!
    • Que: What data would you need to decide what to wear?
    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
  • Data Collection and Analysis Continued
    • Que: How will you analyse your data to determine the best course of action?
    • Supposing it is summer/winter
    • Suggested Ans:
    • You decide to go only with t-shirts
    • You bring some sweater with you as you will be in an air-conditioned room
  • Summary
    • Data analysis helps us to:
    • Know important facts about our object of study
    • Uncover trends that we might not otherwise have known
    • Make better decisions
    Note: the analogy above is a simple way of showing how scientists go about data collection, analysis and interpretation
  • What then is data analysis?
    • The process of arranging, summarising, and transforming data into information.
    Note: Data collection and analysis is a systematic process
  • A Case Study of Previous RE Project
    • Survey of students at a school exhibition on groundbreaker, John Nash
    • Objective: To showcase the life and work of John Nash
  • Evaluating the Survey
    • Evaluate the quality of this survey:
    • What objective(s) is the survey trying to accomplish?
    • Does the survey questions meet those objectives?
    • Did the survey operationalise the key concept in the project topic well? (WHAT IS THIS!)
    • Do you think the survey will facilitate the collection of the right data?
    • Will the students generate sufficient data to reach the right conclusion?
  • Operationalisation of the key concept(s)
    • The process of turning abstract concepts into a concrete and measurable concept.
    • To operationalise a concept, ask
    • What do I mean by the concept?
    • Do my survey questions define the concept in a way that will facilitate the collection of a good (measurable) data?
  • Processing the Data
    • Give each completed survey a serial number.
    • Code the responses (This can be skipped if you number all your response options in the questionnaire)
    • Set up an excel spreadsheet as follows:
    • Column – Question no.
    • Row – Survey serial no.
    • For each survey, key in the completed responses into the excel spreadsheet.
    • Check the data
    • Analyse the responses of each question (using the count function or sort and count).
  • Analysing Quantitative Survey Data (AQSD)
    • The following is important:
    • a) Demographic variables
    • (e.g. profile of your respondents in terms of age, gender, income, education etc)
    • b) Answers to your research questions
  • AQSD Continued
    • c) Relationships between two variables
    • You can do more in-depth data analysis by:
    • Analysing the data by subgroups (e.g. comparing males and females on Question 1)
    • Analysing two questions (e.g. for whose who responded “Yes” to Question 1, how do their responses differ for Question 2?)
  • Reporting your Results
    • State:
    • (i) Research objective clearly
    • (ii) Describe your methodology
    • (iii) Report your results
    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.
  • An Example
    • Better way to report your results:
    • Research objective: To explore the marketability of our new proposed product.
    • Methodology : A survey was conducted with 120 respondents from RI. The respondents were all males, aged … (Describe the survey sample)
    • Results: Results showed that …
  • The Order of a Good Report
    • Present a finding by:
    • First, state the conclusion
    • Second, support your statement with relevant data or the results from your analysis.
    • Third, make inferences from and interpret your data and.
  • From Previous Example
    • Results showed that males were willing to pay more for the new proposed product than females [ conclusion from the data ].
    • 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 ].
    • Hence, it is recommended that marketing strategies should focus more on male consumers who are willing to pay more for the product [ interpretation/inference ].
  • Note
    • Difference between reporting the results and interpreting it
    • 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)
    • Interpretation involves a discussion of the implications (i.e. should focus marketing efforts on male consumers).
  • Reporting Your Results
    • Use figures purposefully
    • Data with only two categories (such as Yes/No and Aware/Not aware) do not require a figure.
  • Reporting Your Results
    • Use figures purposefully
    • Use bar charts instead of pie charts to present information more clearly.
    Copyright (c) 2007 J&C Research Consultants Pte. Ltd.
  • Reporting Your Results Copyright (c) 2007 J&C Research Consultants Pte. Ltd. Figure 2. Reason that captured respondents' attention
  • Reporting Your Results
    • Use figures purposefully (cont’)
    • Note that pie charts are seldom used in academic reports
    • Captions for figures are placed at the bottom , not at the top.
    Copyright (c) 2007 J&C Research Consultants Pte. Ltd.
  • REPORTING YOUR RESULTS Figure 2. Reason that captured respondents' attention
  • Common Mistakes to Avoid
    • Do not change data
    • Do not alter data to compensate for bad survey design
    • Do not project your data to people that do not respond to your questionnaire
  • Reporting Your Results
    • Honesty is the best policy: Integrity issues when reporting results
    • If the results did not turn out as expected, this is also a finding which means you might have to rethink your prior assumptions .
    • Do NOT falsify your results
    • 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
  • Reporting Your Results
    • Honesty is the best policy: Integrity issues when reporting results (cont’)
    • Discuss what was learnt from the research process and offer suggestions for how the project could be improved upon in future research
  • Further Instructions on Data Analysis
    • On step-by-step instructions, see Project Works Vol 3, pages 25-33.
    • On presenting your results, see Project Works Vol 3, pages 34-41.