Module 6
Data Analysis & Report Writing
• After collection of data, the next step is to process and interpretation.
• There are two parts in processing data:
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- Data Analysis
- Interpretation of data
Steps in processing of data
- Preparing raw data
- Editing
- Coding
- Tabulation
- Summarising the data
- Usage of statistical data
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1 Preparing raw data
- The information collected may be illegible, irregular.
- Scattered information
- This can be organised through:
- Editing
- Coding
- Tabulation
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2.Editing
- The purpose of editing is to eliminate errors.
- It involves inspection & correction of each question.
- The main role of editing is to identify ambiguous response.
- The editing is the activity of inspecting, correcting correct data.
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2.Editing
- This can be done in two ways:
- Field editing
- Office editing
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2.Editing – Field Editing
- It should be done when the study is still under progress.
- This is to make sure that proper procedure is followed, respondents, interview them and record their response.
- Such editing is more effective when done on sample after the interview. The investigator must not just doing field editing.
- The reason to field editing is:
- Inappropriate respondents
- Incomplete interviews
- Improper understanding
- Lack of consistency
- Legibility – clear on same spot
- Fictitious interviews, cheating by interviewer
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2.Editing - Office Editing
- Such type of editing relates to the time when process has been completed.
- Here a single or common editor corrects the wrong place, entry in wrong unit e.t.c.
- As a rule all the wrong answers should be dropped results.
- It should be done after the collection of the data.
- This can be done by interviewer, or supervisor.
- Office editing need more experts who understand respondents.
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2.Editing – Office Editing
- Eg: I don't drink coffee,
- My favourite brand is BRU
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- It is inconsistent
- 1.was the respondent lying?
- 2. did the interviewer record wrongly?
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EDITING REQUIRES SOME CAREFUL CONSIDERATIONS:
- Editor must be familiar with the interviewer's mind set, objective of the study.
- Different colors should be used when editors make entry in data.
- They should initial all answers or changes they make to the data.
- The editors name and date of editing should be placed on the data.
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3. Coding
- It refers to transforming edited questionnaire ready for analysis.
- It involves assigning numbers or other symbols.
- Guidelines
- Establishment of appropriate category
- Internal -X external -Y
- Mutually exclusive
- Student, professional, sales, manager
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4. Tabulation
- It refers to counting the numbers of cases that fall into different categories.
- The results are summarized in the form of statistical tables, data is divided into groups and sub-groups.
- The counting and placing of data in a particular pattern are done.
- The tabulation involves:
- Sorting and counting
- Summarising the data
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4. Tabulation – summarising data
| Income | Tally marks |
|---|---|
| 1000 | |||| |
| 2000 | |||| |||| |
| 3000 | |||| |||| ||| |
4. Tabulation – Types
1.Simple or one way table :
the multiple choice questions which allow only one-way tabulation or univariate.
the questions are pre-determined and consist of responses falling into a particular category and percentage.
There may be two types of univariate tabulation:
- Questions with only one response
- Multiple response to question
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4. Tabulation – Types
1. Questions with only one response
Table No 1
Study of number of children in a family
| No of children | Family | % |
|---|---|---|
| 0 | 10 | 10 |
| 1 | 20 | 20 |
| 2 | 30 | 30 |
| 3 | 40 | 40 |
| 100 | 100 |
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4. Tabulation – Types
2. Questions with multiple response
Choice of an automobile
What you like about your Car
Table No 1
| Attributes | No of respondents |
|---|---|
| Body | 15 |
| Design | 20 |
| Colour | 25 |
| Mileage | 40 |
| Safety | 20 |
4. Tabulation – Types
2. Cross tabulation or two-way table
This is also known as Bivariate Tabulation.
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The data may include two or more variables.
Eg: popularity of health drink among families having different incomes.
Suppose 500 families are contracted and data collected is as follows.
| Income per month | 0 | 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|
| < 1000 | 5 | 0 | 8 | 9 | 11 |
| 1001-2000 | 10 | 5 | 8 | 10 | 13 |
| 2001-3000 |
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Data Validation
- Data validation is a process that ensures the supply of clean, correct, and relevant data to the programs, applications and systems.
- It checks for the integrity and validity of data by comparing it with the rules defined by different software and its components.
- Data validation ensures that the data complies with pre-defined requirements and quality benchmarks.
- Data validation is also known as input validation.
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Some of the types of data validation
- Code validation
- Data type validation
- Data range validation
- Constraint validation
- Structured validation
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Report Writing and Presentation
WHAT IS IT?
- An analysis of the data of the investigation, written in an objective, logical and factual way.
- Any matter on which definite information is required.
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“Research reports are detailed and accurate accounts of the disciplined studies accomplished to solve problems or to reveal new knowledge.” (Busha and Harter, 1988).
1. Meaning of Research Report
- Research report writing is the oral or written presentation of the evidence and the findings in such detail as readily understood and accessed by the reader, and enable him to verify the validity of the conclusions.
- According to American Marketing Society, Convey to interested persons the whole story in sufficient detail and to enable each reader to comprehend the data and to determine himself the validity of the interpretations. It is covers, Disseminations, Presents the information and knowledge to others, to test the generalizations, to encourage others to carry on the same or allied problem.
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Structure of Research Report
- Generally, a research report, whether it is case study.
- 1) The Priliminary i.e. preface pages
- 2) The text of the report / Main body of the report
- 3) The Reference material.
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PRELIMINARY SECTION
- Title page
- Certification
- Candidate Declaration
- Preface including Acknowledgment
- Table of Content
- List of Tables
- List of figures
- List of Abbreviation
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CHAPTER 1-INTRODUCTION
- 1.0 Introduction
- 1.1 Background of the study
- 1.2 Problem Statement
- 1.3 Purpose and objective of the study
- 1.4 Research Questions
- 1.5 Definition of Terms
- 1.6 Significant of the study
- 1.7 Conclusion
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CHAPTER 2-LITERATURE REVIEW
- 2.0 Introduction
- 2.1 Body of the literature
- 2.1.1 General area of research
- 2.1.2 Underlying theory
- 2.1.3 Variables used from previous
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- 2.2 Theoretical Framework
- 2.3 Hypotheses
- 2.4 Conclusion
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CHAPTER 3 - RESEARCH METHODOLOGY
- 3.0 Introduction
- 3.1 Research Design
- 3.2 Variable and Measurement
- 3.3 Questionnaire design
- 3.4 Population and Sample
- 3.5 Scope of the study
- 3.6 Data analysis method
- 3.6.1 Goodness of data
- 3.6.2 Inferential analysis
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- 3.7 Conclusion
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CHAPTER 4- DATA COLLECTION, DATA ANALYSIS
- 4.1 Introduction
- 4.2 Goodness of Measure
- 4.2.1 Representativeness of data
- 4.2.2 Validity test
- 4.2.3 Reliability test
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- 4.3 Inferential analysis
- 4.3.1 Descriptive analysis
- 4.3.2 Test of difference
- 4.3.3 Test of relationship
- Correlation analysis
- Hypothesis testing
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- 4.4 Conclusion
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CHAPTER 5-DISCUSSION AND CONCLUSION
- 5.1 Recapitulation of major findings
- 5.2 Discussion
- 5.3 Implication
- 5.3.1 Theoretical Implication
- 5.3.2 Practical Implication
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- 5.4 Limitation
- 5.5 Recommendation for future research
- 5.6 Conclusion
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REFERENCE MATERIAL
The reference material is generally divided as:
- 1. Bibliography (APA Style or ..)
- 2. Appendices (SPSS output & Data Stream)
- 3. Glossary of terms (if any)
- 4. Index (if any)
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Types Of reports
- Technical Report
- Popular Report
Technical Report
In the technical report the main emphasis is on:
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- the methods employed,
- assumptions made in the course of the study,
- the detailed presentation of the findings including their limitations and supporting data.
Popular Report
- The popular report is one which gives emphasis on simplicity and attractiveness.
- The simplification should be sought through clear writing, minimization of technical, particularly mathematical, detail and liberal use of charts and diagrams.
- Attractive layout along with large print, many subheadings, and occasional cartoon now and then is another characteristic feature of the popular report.
- Besides, in such a report emphasis is given on practical aspects and policy implications. We give below a general outline of a popular report.
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STRUCTURE OF A BUSINESS REPORT
- Title page
- Letter of Transmittal (covering letter)
- Table of contents
- Executive summary
- Introduction
- Main Body
- Conclusion
- Appendix
- References & Bibliography
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Characteristics of good report
- Attractive
- Clear Topic
- Balanced Language
- No repetition of facts
- Statement of scientific facts
- Practicability
- Description of the difficulties and the shortcomings
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Importance of Research Report
- Communicates the information
- Helps in evaluation
- Facilitates measuring performance
- Predicts future trends
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Presentation - Essential Characteristics
- Objectives :
- Preparation
- First impression
- Facial expression
- Visual aids
- Audience involvements
- Effective conclusion
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Elements of Presentation
- Presenter
- Specific content with a definite objective
- Why who where when what and how
- Audience
- Who
- Why
- Their background
- How many
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Factors affecting Presentation
- Audience analysis
- Personal appearance
- Opening and closing of presentation
- Language
- Body language
- Use of visuals
- Organization of presentation
- Voice
- Answering the questions
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Processing of data--Editing, Coding and tabulation
- After collecting data, the method of converting data into a meaningful statement; includes
- Data processing, Data analysis, and Data interpretation and presentation.
- Data reduction or processing mainly involves those activities that are necessary for preparing the data for analysis (editing, coding, classification and manipulation) could be manual or electronic.
- It involves editing, categorizing the open-ended questions, coding, computerization and preparation of tables and diagrams.
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Editing data:
- Information gathered during data collection.
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Example: Data collected through questionnaire may have answers which may not be ticked at all, or some questions may be left unanswered.
Sometimes information may be given in a form that needs reconstruction in a category designed for annual income may give daily/monthly income in annual income and so on.
The researcher has to take a decision as to how to deal with it.
- Editing also needs that data are relevant and consistent before they are modified.
- Occasionally, the investigator makes a mistake and records an impossible answer. "How much red chilies do you use in a month?" The answer is written as "4 kilos". Can a family consume four kilo chilies in a month? The correct answer may be 400 grams.
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Coding of data:
- Coding is translating answers into numerical codes. It involves assigning numbers to the various categories of a variable for the purpose of analysis.
- Coding is done by using a code book, code sheet and a computer card.
- Coding is done on the basis of the instructions of the investigator. The code book gives a numerical code for each answer.
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Data classification/distribution
- Sarantakos (1998: 343) defines distribution of data as the classification of scores obtained for the various categories of a particular variable.
There are four types of distributions:
- Frequency distribution
- Percentage distribution
- Cumulative distribution
- Statistical distributions
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Frequency distribution:
- In social science research, frequency distribution presents the frequency of occurrences of certain data. Frequency distribution appears in two forms:
- Ungrouped: Here, the scores are not collapsed into categories. The distribution of ages of the students of a BJ (MC) class (e.g., 18, 19, 20, and so on) will be presented as ungrouped frequency distribution.
- Grouped: Here, the scores are collapsed into categories and the scores are presented together as a group. For example, the age distribution groups like 18-20, 21-22 etc.
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Percentage distribution:
It is also possible to give frequencies not in absolute numbers but in percentages.
For instance instead of saying 200 respondents have a monthly income of less than Rs. 500, we can say that 40 percent of the respondents have a monthly income of less than Rs. 500.
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Cumulative distribution:
- It tells how often the value of the random variable is less than or equal to a particular reference value.
Statistical data distribution:
- In this type of data distribution, some measure of average is worked out of a sample of respondents.
- Several kind of averages are available (mean, median, mode) and the researcher must decide which is most suitable for his purpose.
- Once the average has been calculated, the question arises as to how representative a figure it is, i.e., how closely the other figures cluster around it.
- Are most of them very close to it or is there a great deal of variation?
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Tabulation of data:
After editing, which ensures that the information is consistent, accurate and categorized in a suitable form, the data are put together in some kinds of tables and may also undergo some forms of statistical analysis.
- Table can be prepared manually and/or by computer.
- For a small study of 100 to 200 persons, there is no point in tabulating by computer since this necessitates the use of punched cards.
- But for a survey analysis involving a large number of respondents requiring cross tabulation involving more than two variables, manual tabulation will be inappropriate and time consuming.
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Data Validation
- Data validation is a process that ensures the supply of clean, correct, and relevant data to the programs, applications and systems.
- It checks for the integrity and validity of data by comparing it with the rules defined by different software and its components.
- Data validation ensures that the data complies with pre-defined requirements and quality benchmarks.
- Data validation is also known as input validation.
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Some of the types of data validation
- Code validation
- Data type validation
- Data range validation
- Constraint validation
- Structured validation
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