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DATA PRESENTATION:Bivariate Tables, Constructing Percentage Tables

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THE PARTS OF THE TABLE:Reading a percentage Table >>
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Research Methods ­STA630
VU
Lesson 31
DATA PRESENTATION
Tables and graphs (pictorial presentation of data) may simplify and clarify the research data. Tabular
and graphic representation of data may take a number of forms, ranging from computer printouts to
elaborate pictographs. The purpose of each table or graph, however, is to facilitate the summarization
and communication of the meaning of the data.
Although there are a number of standardized forms for presenting data in table or graphs, the creative
researcher can increase the effectiveness of particular presentation.  Bar charts, pie charts, curve
diagrams, pictograms, and other graphic forms of presentation create a strong visual impression.
The proliferation of computer technology in business and universities has greatly facilitated tabulation
and statistical analysis. Commercial packages eliminate the need to write a new program every time
you want to tabulate and analyze data with a computer. SAS, Statistical Package for the Social Sciences
(SPSS), SYSTAT, Epi. Info. And MINITAB is commonly used statistical packages.  These user
friendly packages emphasize statistical calculations and hypothesis testing for varied types of data. They
also provide programs for entering and editing data. Most of these packages contain sizeable arrays of
programs for descriptive analysis and univariate, bivariate, and multivariate statistical analysis.
Results with one variable
Frequency Distribution
Several useful techniques for displaying data are in use. The easiest way to describe the numerical data
of one variable is with a frequency distribution. It can be used with nominal-, ordinal-, interval-, or
ratio-level data and takes many forms. For example we have data of 400 students. We can summarize
the data on the gender of the students at a glance with raw count or a frequency distribution
Table 1: Frequency distribution of students
Gender
Frequency
Percent
Male
300
75
Female
100
25
Total
400
100
We can present the same information in a graphic form. Some common types of graphic presentations
are the histograms, bar chart, and pie chart. Bar charts or graphs are used for discrete variables. They
can have vertical or horizontal orientation with small space between the bars. The terminology is not
exact, but histograms are usually upright bar graphs for interval or ratio data.
Presentation of data in these forms lays emphasis on visual representation and graphical techniques over
summary statistics. Summary statistics may obscure, conceal, or even misrepresent the underlying
structure of the data. Therefore it is suggested that data analysis should begin with visual inspection.
The presented data has to be interpreted. The purpose of interpretation is to explain the meanings of the
data so that we can make inferences and formulate conclusions. Therefore, interpretation refers to
making inferences pertinent to the meaning and implications of the research investigation and drawing
conclusions. In order for interpretation, the data have to be meaningfully analyzed. For purposes of
analysis the researchers use statistics.
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Research Methods ­STA630
VU
The word statistics has several meanings. It can mean a set of collected numbers (e.g. numbers telling
how many people living in a city) as well as a branch of applied mathematics used to manipulate and
summarize the features of numbers. Social researchers use both types of statistics. Here, we focus on
the second type ­ ways to manipulate and summarize numbers that represent data from research project.
Descriptive statistics describe numerical data. They can be categorized by the number of variables
involved: univariate, bivariate, or multivariate (for one, two, and three or more variables). Univariate
statistics describe one variable.
Researchers often want to summarize the information about one variable into a single number. They
use three measures of central tendency, or measures of the center of the frequency distribution: mean,
median and mode, which are often called averages (a less precise and less clear way to say the same
thing). The mode is simply the most common or frequently occurring number. The median is the
middle point. The mean also called the arithmetic average, is the most widely used measure of central
tendency. A particular central tendency is used depending upon the nature of the data.
Bivariate Tables
The bivariate contingency table is widely used.  The table is based on cross-tabulation (cross-
classification); that is the cases are organized in the table on the basis of two variables at the same time.
A contingency table is formed by cross-tabulating the two or more variables. It is contingent because
the cases in each category of a variable get distributed into each category of a second variable. The
table distributes cases into categories of multiple variables at the same time and shows how the cases, by
the category of one variable, are "contingent upon" the categories of the other variables.
Constructing Percentage Tables
It is to construct a percentage table, but there are ways to make it look professional. Let us take two
variables like the age of the respondents and their attitude towards "women empowerment." Assuming
that age affects the attitude towards women empowerment let us hypothesize: the lower the age, the
higher the favorable attitude towards "women empowerment." The age range of the respondents is 25 to
70, and the attitude index has three categories of "highly favorable," "medium favorable," and "low
favorable." The age variable has so many categories that making a table with that number becomes
unwieldy and meaningless. Therefore, we regroup (recode) the age categories into three i.e. under 40
years, 40 ­ 60 years, and 61 + years.
Univariate table for age
· Table 2: Age of the respondents
.
·
Age (Yrs.)
Frequency
Percent
.
·
Under 40
1000
33.3
·
40 ­ 60
1000
33.3
·
61 +
1000
33.3
.
·
Total
3000
100
.
105
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Research Methods ­STA630
VU
Univariate table for attitude
· Table 3: Attitude towards women
.
empowerment
.
Attitude
Frequency
Percent
Hi Favorable
1100
37
Med Favorable
1050
35
Lo Favorable
850
28
Total
3000
100
Bivariate table
· Table 4: Age by attitude towards women
.
empowerment
.
Age (in years)
.
Level of
under 40
40 ­60
61 +
Total
attitude
F.
%
F.
%
F
%
F
%
Hi Favorable
600
60
300
30
200
20
1100
37
Med. Favorable 300
30
500
50
250
25
1050
35
Lo Favorable
100
10
200
20
500
50
850
28
Total
1000
100
1000 100
1000
100
3000
100
106
Table of Contents:
  1. INTRODUCTION, DEFINITION & VALUE OF RESEARCH
  2. SCIENTIFIC METHOD OF RESEARCH & ITS SPECIAL FEATURES
  3. CLASSIFICATION OF RESEARCH:Goals of Exploratory Research
  4. THEORY AND RESEARCH:Concepts, Propositions, Role of Theory
  5. CONCEPTS:Concepts are an Abstraction of Reality, Sources of Concepts
  6. VARIABLES AND TYPES OF VARIABLES:Moderating Variables
  7. HYPOTHESIS TESTING & CHARACTERISTICS:Correlational hypotheses
  8. REVIEW OF LITERATURE:Where to find the Research Literature
  9. CONDUCTING A SYSTEMATIC LITERATURE REVIEW:Write the Review
  10. THEORETICAL FRAMEWORK:Make an inventory of variables
  11. PROBLEM DEFINITION AND RESEARCH PROPOSAL:Problem Definition
  12. THE RESEARCH PROCESS:Broad Problem Area, Theoretical Framework
  13. ETHICAL ISSUES IN RESEARCH:Ethical Treatment of Participants
  14. ETHICAL ISSUES IN RESEARCH (Cont):Debriefing, Rights to Privacy
  15. MEASUREMENT OF CONCEPTS:Conceptualization
  16. MEASUREMENT OF CONCEPTS (CONTINUED):Operationalization
  17. MEASUREMENT OF CONCEPTS (CONTINUED):Scales and Indexes
  18. CRITERIA FOR GOOD MEASUREMENT:Convergent Validity
  19. RESEARCH DESIGN:Purpose of the Study, Steps in Conducting a Survey
  20. SURVEY RESEARCH:CHOOSING A COMMUNICATION MEDIA
  21. INTERCEPT INTERVIEWS IN MALLS AND OTHER HIGH-TRAFFIC AREAS
  22. SELF ADMINISTERED QUESTIONNAIRES (CONTINUED):Interesting Questions
  23. TOOLS FOR DATA COLLECTION:Guidelines for Questionnaire Design
  24. PILOT TESTING OF THE QUESTIONNAIRE:Discovering errors in the instrument
  25. INTERVIEWING:The Role of the Interviewer, Terminating the Interview
  26. SAMPLE AND SAMPLING TERMINOLOGY:Saves Cost, Labor, and Time
  27. PROBABILITY AND NON-PROBABILITY SAMPLING:Convenience Sampling
  28. TYPES OF PROBABILITY SAMPLING:Systematic Random Sample
  29. DATA ANALYSIS:Information, Editing, Editing for Consistency
  30. DATA TRANSFROMATION:Indexes and Scales, Scoring and Score Index
  31. DATA PRESENTATION:Bivariate Tables, Constructing Percentage Tables
  32. THE PARTS OF THE TABLE:Reading a percentage Table
  33. EXPERIMENTAL RESEARCH:The Language of Experiments
  34. EXPERIMENTAL RESEARCH (Cont.):True Experimental Designs
  35. EXPERIMENTAL RESEARCH (Cont.):Validity in Experiments
  36. NON-REACTIVE RESEARCH:Recording and Documentation
  37. USE OF SECONDARY DATA:Advantages, Disadvantages, Secondary Survey Data
  38. OBSERVATION STUDIES/FIELD RESEARCH:Logic of Field Research
  39. OBSERVATION STUDIES (Contd.):Ethical Dilemmas of Field research
  40. HISTORICAL COMPARATIVE RESEARCH:Similarities to Field Research
  41. HISTORICAL-COMPARATIVE RESEARCH (Contd.):Locating Evidence
  42. FOCUS GROUP DISCUSSION:The Purpose of FGD, Formal Focus Groups
  43. FOCUS GROUP DISCUSSION (Contd.):Uses of Focus Group Discussions
  44. REPORT WRITING:Conclusions and recommendations, Appended Parts
  45. REFERENCING:Book by a single author, Edited book, Doctoral Dissertation