SlideShare a Scribd company logo
1 of 11
Download to read offline
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 1
Quantitative Research
In quantitative research, the investigator identifies a research problem based on trends in the
field or on the need to explain why something occurs. Describing a trend means that the research
problem can be answered best by a study in which the researcher seeks to establish the overall
tendency of responses from individuals and to note how this tendency varies among people. For
example, you might seek to learn how voters describe their attitudes toward a bond issue. Results
from this study can inform how a large population views an issue and the diversity of these
views (Cresswell, 2001, p-13)
Characteristics of Quantitative Research
In quantitative research the major characteristics are (Cresswell, 2001, p-13):
 Describing a research problem through a description of trends or a need for an
explanation of the relationship among variables.
 Providing a major role for the literature through suggesting the research questions to be
asked and justifying the research problem and creating a need for the direction (purpose
statement and research questions or hypotheses) of the study.
 Creating purpose statements, research questions, and hypotheses that are specific, narrow,
measurable, and observable.
 Collecting numeric data from a large number of people using instruments with preset
questions and responses.
 Analyzing trends, comparing groups, or relating variables using statistical analysis, and
interpreting results by comparing them with prior predictions and past research.
 Writing the research report using standard, fixed structures and evaluation criteria, and
taking an objective, unbiased approach.
Quantitative Analysis
Quantitative analysis involves the techniques by which researchers convert data to numerical
forms and subject them to statistical analyses (Babbie, 2001). It involves the numerical
representation and manipulation of observations for the purpose of describing and explaining the
phenomena that those observations reflect.
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 2
• Involves techniques
• Involve task of converting data into knowledge.
1. Quantification of data
It is the process of converting data to a numerical format. This involves converting social
science data into a machine-readable form—a form that can be read and manipulated by
computers and similar machines used in quantitative analysis. Today, quantitative analysis is
almost always handled by computer programs such as SPSS and Micro Case (Babbie, 2011,
page, 422).
1.1 Coding
To conduct a quantitative analysis, researcher often must engage in a coding process after the
data have been collected. For example, open-ended questionnaire items result in non-numerical
responses which need to be coded before analysis. Suppose, for example, that a survey
researcher asks respondents, ―What is your occupation?‖ The responses to such a question will
vary considerably. Although he or she can assign a separate numerical code to each reported
occupation, this procedure will not facilitate analysis. The variable occupation has many pre-
established coding schemes which differentiate and combines both to compare research results
with other studies.
One coding scheme distinguishes
Professional and managerial occupations Clerical occupation Semi-skilled occupations
Another coding scheme distinguishes sectors of economy
Manufacturing health education commerce
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 3
1.2 Developing code categories
It means the categorization of data into distinct field. For example, if we are willing to conduct a
survey in a self-administered campus about the existing problems facing by students. We can
categorize the responses from students as financial concern, academic concern and non academic
concern.
Financial concern Academic concern Non-academic concern
1.3 Codebook construction
The end product of the coding process is the conversion of data items into numerical codes.
These, codes represent attributes composing variables. A codebook is a document that describes
the locations of variables and lists the assignments of codes to the attributes composing those
variables. For example, if we conduct a survey on then it will be the following:
1.4 Data entry
Transforming data into quantitative form, researchers interested in quantitative analysis need to
convert data into a machine-readable format, so that computers can read and manipulate the data.
Tuition is too high
Books cost too much
Not enough financial aid
Not enough classes offered
Advisors are not available
Too many requirements
Cafeteria food is infected
Not enough parking
spaces
Cockroaches in the dorm
How often do you attend religious
services?
0. Never
1. Less than once a year
2. About once or twice a year
3. Several times a year
4. About once a month
5.Every week
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 4
Data-entry specialists (including yourself) could enter the data into, say, an SPSS data matrix or
into an Excel spreadsheet to be imported later into SPSS.
Types of Variables Analysis
2.0 Univariate analysis
Univariate analysis is the analysis of a single variable for purposes of description. Frequency
distribution, averages, and measures of dispersion would be examples of univariate analysis, as
distinguished from bivariate and multivariate analysis.
Univariate analysis covers the following points:
2.1 Frequency Distribution
A description of the number of times that the various attributes of a variable are observed in a
sample is called a frequency distribution. The report that 53 percent of a sample were men and
47 percent were women would be a simple example of a frequency distribution. It gives
researcher some general picture about the dispersion, as well as maximum and minimum
response.
2.2 Central Tendency
A measure of central tendency is a single value that attempts to describe a set of data by
identifying the central position within that set of data. As such, measures of central tendency are
sometimes called measures of central location.
Univariate analysis
One variable
E.g. Age, gender, income etc.
Bivariate analysis
Two variables
E.g. gender & CGPA
Multivariate analysis
Several variables
E.g. Age, education, and
prejudice
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 5
2.2.1 Mean
An average computed by summing the values of several observations and dividing by the
number of observations. If you now have a grade number of observations point average of 4.0
based on 10 courses, and you get an F in this course, your new grade point (mean) average will
be 3.6
2.2.2 Mode
An average representing the most frequently observed value or attribute. If a sample contains
1,000 Protestants, 275 Catholics, and 33 Jews, Protestant is the modal category.
2.2.3 Median
An average representing the value of the ―middle‖ case in a rank-ordered set of observations. If
the ages of five men are 16, 17, 20, 54, and 88, the median would be 20. (The mean would be 39)
Figure: Three averages (Babbie, 2001, page, 430)
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 6
2.3 Dispersion
Dispersion refers to the way values are distributed around some central value, such as an
average. The simplest measure of dispersion is the range: the distance separating the highest
from the lowest value.
Range
The simplest measure of dispersion is the range: the distance separating the highest
from the lowest value. The range is a simple example of a measure of dispersion. Thus, we may
report that the mean age of a group is 37.9, and the range is from 12 to 89 (Babbie, 2001, page,
431)
Variance
To describe the variability of the distribution.
Standard deviation
A measure of dispersion around the mean. It is an index of the amount of variability in a set of
data. Higher SD means data are more dispersed. Lower SD means that they are more bunched
together. Figure 14-4 illustrates the basic idea. Notice that the professional golfer not only has a
lower mean score but is also more consistent represented by the smaller standard deviation. The
duffer, on the other hand, has a higher average and is also less consistent: sometimes doing much
better, sometimes much worse (Babbie, 2001, page, 432)
Dispersion
Range Variance Standard Deviation
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 7
2.4 Continuous & Discrete Variables
Continuous variable: A variable whose attributes form a steady progression, such as age or
income. Thus, the ages of a group of people might include 21, 22, 23, 24, and so forth and could
even be broken down into fractions of years.
E.g. Income & age
Scale: Interval & Ratio
Discrete Variables: A variable whose attributes are separate from one another, or discontinuous,
as in the case of gender or religious affiliation. Thus, in age (a continuous variable), the attributes
progress steadily from 21 to 22 to 23, and so forth, whereas there is no progression from male to
female in the case of gender.
E.g. Marital status, gender & nationality.
Scale: Nominal & Ordinal
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 8
Modes should be calculated for nominal data, medians for interval data, and means for ratio data,
not for nominal data.
2.6 Sub group comparison
Univariate analyses describe the units of analysis of a study and, if they are a sample drawn from
some larger population, allow us to make descriptive inferences about the larger population. The
subgroup comparisons tell us how different groups in the population response to questions and
see a pattern in the result (Babbie, 2011, page: 433).
For example table represents whether marijuana should be legalized or not by age of
respondents:
Marijuana Legalization by Age of Respondents
Source: General Social Survey, 2004, National Opinion Research Center.
In response, 33.4 percent said it should and 66.6 percent said t.it shouldn‘t.
2.7 Collapsing” Response Categories
It means combining the two appropriate range of variation to get better picture or meaningful
analyses. Consider an example: Attitudes toward the United Nations: How is the UN doing in
solving the problems it has had to face?
Source: ―5-Nation Survey Finds Hope for U.N.,‖New York Times, June 26, 1985, p. 6
Under 21 21-35 36-54 55 & older
Should be legalized 27% 40% 37% 24%
Should not be legalized 73 60 63 76
100%= (34) (238) (338) (265)
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 9
Part of the problem with Table lies in the table relatively small percentages of respondents
selecting the two extreme response categories: the UN is doing a very good or a very poor job.
This procedure is inappropriate in that it ignores all those respondents who gave the most
positive answer of all: ―very good job.‖ In a situation like this, you should combine or ―collapse‖
the two ends of the range of variation combine ―very good‖ with ―good‖ and ―very poor‖ with
―poor.‖ If you were to do this in the analysis of your own data, it would be wise to add the raw
frequencies together and recompute percentages for the combined categories (Babbie, 2011,
page, 434)
After collapsing extreme categories
Source: ―5-Nation Survey Finds Hope for U.N.,‖New York Times, June 26, 1985, p. 6
2.8 Handling “Don’t Knows” option
Whether to include or exclude the ‗don‘t knows‘ is harder to decide. It‘s usually a good idea to
give people the option of saying ―don‘t know‖ or ―no opinion‖ when asking for their opinions on
issues. In any event, the truth contained within your data is that a certain percentage said they
didn‘t know and the remainder divided their opinions in whatever manner they did (Babbie,
2011, page, 436).
3.0 Bivariate Analysis
The analysis of two variables simultaneously, for the purpose of determining the empirical
relationship between them. The construction of a simple percentage table or the computation of a
simple correlation coefficient are examples of bivariate analyses. However, as with univariate
analysis the purpose of subgroup comparisons is largely descriptive. Most bivariate analysis in
social research adds on another element: determining relationships between the variables
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 10
themselves (Babbie, 2011, page, 436-37). For example: Religious Attendance Reported by Men
and Women in 2004. Table describes the church attendance of men & women as reported in
1990 General Social Survey. It shows: comparatively & descriptively – that women in the study
attended church more often as compared to men.
Source: Babbie, 2011, page, 437
3.1 Constructing and Reading Bivariate Tables
Steps involved in constructing of explanatory bivariate tables:
1. The cases are divided into groups according to attributes of the independent variable.
2. Each of these subgroups is then described in terms of attributes of the independent
variable.
3. Finally, the table is read by comparing the independent variable subgroups with one
another in terms of a given attribute of the dependent variable.
Table: Gender and attitudes toward equality for men and women. Source: (Babbie, 2011, 439)
Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 11
4.0 Multivariate analysis
The analysis of the simultaneous relationships among several variables. Examining
simultaneously the effects of Religious Attendance, Gender, and Age would be an example of
multivariate analysis (Babbie, 2011, page, 441).
.
Source: General Social Survey, 1972 – 2006, National Opinion Research Center
5.0 Sociological diagnostics
Sociological diagnostics is a quantitative analysis technique for determining the nature of social
problems such as ethnic or gender discrimination (Babbie, 2011, page, 442)
It can be used to replace opinions with facts and to settle debates with data analysis. For example
Issues of gender and income. Because family pattern, women as group have participated less in
in the labor force and many only begin outside the home after completing certain child-rearing
tasks.
Reference
Babbie E. (2011). The Practice of Social Research, (Twelfth ed.). California: Wadsworth
Cengage Learning.
http://www.slideshare.net/asmasemma/quantitative-data-analysis
Religious
attendance
Gender
Age

More Related Content

What's hot

Data Analysis, Presentation and Interpretation of Data
Data Analysis, Presentation and Interpretation of DataData Analysis, Presentation and Interpretation of Data
Data Analysis, Presentation and Interpretation of Data
Roqui Malijan
 
Univariate & bivariate analysis
Univariate & bivariate analysisUnivariate & bivariate analysis
Univariate & bivariate analysis
sristi1992
 
Research question presentation
Research question presentationResearch question presentation
Research question presentation
Basharat Mirza
 
Data analysis powerpoint
Data analysis powerpointData analysis powerpoint
Data analysis powerpoint
jamiebrandon
 

What's hot (20)

Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Quali...
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Quali...Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Quali...
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Quali...
 
Ppt data collection
Ppt data collectionPpt data collection
Ppt data collection
 
Qualitative research
Qualitative researchQualitative research
Qualitative research
 
Qualitative research design
Qualitative research designQualitative research design
Qualitative research design
 
Data Analysis, Presentation and Interpretation of Data
Data Analysis, Presentation and Interpretation of DataData Analysis, Presentation and Interpretation of Data
Data Analysis, Presentation and Interpretation of Data
 
Validity in Research
Validity in ResearchValidity in Research
Validity in Research
 
Quantitative research design
Quantitative research design Quantitative research design
Quantitative research design
 
Analysis of data
Analysis of dataAnalysis of data
Analysis of data
 
Problem statement of research
Problem statement of researchProblem statement of research
Problem statement of research
 
Data collection,tabulation,processing and analysis
Data collection,tabulation,processing and analysisData collection,tabulation,processing and analysis
Data collection,tabulation,processing and analysis
 
Chapter 7-THE RESEARCH DESIGN
Chapter 7-THE RESEARCH DESIGNChapter 7-THE RESEARCH DESIGN
Chapter 7-THE RESEARCH DESIGN
 
Analysis and Interpretation of Data
Analysis and Interpretation of DataAnalysis and Interpretation of Data
Analysis and Interpretation of Data
 
Univariate & bivariate analysis
Univariate & bivariate analysisUnivariate & bivariate analysis
Univariate & bivariate analysis
 
Data Collection in Quantitative Research
Data Collection in Quantitative ResearchData Collection in Quantitative Research
Data Collection in Quantitative Research
 
Research question presentation
Research question presentationResearch question presentation
Research question presentation
 
Quantitative Research Process
Quantitative Research ProcessQuantitative Research Process
Quantitative Research Process
 
Quantitative research
Quantitative researchQuantitative research
Quantitative research
 
Thematic analysis
Thematic analysisThematic analysis
Thematic analysis
 
Quantitative research design
Quantitative research designQuantitative research design
Quantitative research design
 
Data analysis powerpoint
Data analysis powerpointData analysis powerpoint
Data analysis powerpoint
 

Similar to Quantitative data analysis

The World Testifies Of Data And Our Understanding Of It Essay
The World Testifies Of Data And Our Understanding Of It EssayThe World Testifies Of Data And Our Understanding Of It Essay
The World Testifies Of Data And Our Understanding Of It Essay
Sandy Harwell
 
Question 1The Uniform Commercial Code incorporates some of the s.docx
Question 1The Uniform Commercial Code incorporates some of the s.docxQuestion 1The Uniform Commercial Code incorporates some of the s.docx
Question 1The Uniform Commercial Code incorporates some of the s.docx
makdul
 
Chapter 15 Social Research
Chapter 15 Social ResearchChapter 15 Social Research
Chapter 15 Social Research
arpsychology
 
Sfl level of measurement
Sfl level of measurementSfl level of measurement
Sfl level of measurement
lyasuyi
 
Kinds Of Variables Kato Begum
Kinds Of Variables Kato BegumKinds Of Variables Kato Begum
Kinds Of Variables Kato Begum
Dr. Cupid Lucid
 

Similar to Quantitative data analysis (20)

The World Testifies Of Data And Our Understanding Of It Essay
The World Testifies Of Data And Our Understanding Of It EssayThe World Testifies Of Data And Our Understanding Of It Essay
The World Testifies Of Data And Our Understanding Of It Essay
 
Data Analysis
Data Analysis Data Analysis
Data Analysis
 
Question 1The Uniform Commercial Code incorporates some of the s.docx
Question 1The Uniform Commercial Code incorporates some of the s.docxQuestion 1The Uniform Commercial Code incorporates some of the s.docx
Question 1The Uniform Commercial Code incorporates some of the s.docx
 
Myths And Misperceptions About Online Learning2
Myths And Misperceptions About Online Learning2Myths And Misperceptions About Online Learning2
Myths And Misperceptions About Online Learning2
 
Interpretation of Data.pptx
Interpretation of Data.pptxInterpretation of Data.pptx
Interpretation of Data.pptx
 
B0740410
B0740410B0740410
B0740410
 
Chapter 15 Social Research
Chapter 15 Social ResearchChapter 15 Social Research
Chapter 15 Social Research
 
WEEK 2 LECTURE 1 SLIDES.pphvhvyvjyfyfyfytx
WEEK 2 LECTURE 1 SLIDES.pphvhvyvjyfyfyfytxWEEK 2 LECTURE 1 SLIDES.pphvhvyvjyfyfyfytx
WEEK 2 LECTURE 1 SLIDES.pphvhvyvjyfyfyfytx
 
Lesson 1 05 measuring central tendency
Lesson 1 05 measuring central tendencyLesson 1 05 measuring central tendency
Lesson 1 05 measuring central tendency
 
Data analysis presentation by Jameel Ahmed Qureshi
Data analysis presentation by Jameel Ahmed QureshiData analysis presentation by Jameel Ahmed Qureshi
Data analysis presentation by Jameel Ahmed Qureshi
 
From data to endgame for dissertation and theses: what does it take
From data to endgame for dissertation and theses: what does it takeFrom data to endgame for dissertation and theses: what does it take
From data to endgame for dissertation and theses: what does it take
 
Sfl level of measurement
Sfl level of measurementSfl level of measurement
Sfl level of measurement
 
Exploratory Essay Example
Exploratory Essay ExampleExploratory Essay Example
Exploratory Essay Example
 
Specifying a purpose, Purpose statement, Hypostheses and research questions
Specifying a purpose, Purpose statement, Hypostheses and research questionsSpecifying a purpose, Purpose statement, Hypostheses and research questions
Specifying a purpose, Purpose statement, Hypostheses and research questions
 
Kinds Of Variables Kato Begum
Kinds Of Variables Kato BegumKinds Of Variables Kato Begum
Kinds Of Variables Kato Begum
 
Elements of research
Elements of researchElements of research
Elements of research
 
Descriptive research
Descriptive researchDescriptive research
Descriptive research
 
April Heyward Research Methods Class Session - 7-29-2021
April Heyward Research Methods Class Session - 7-29-2021April Heyward Research Methods Class Session - 7-29-2021
April Heyward Research Methods Class Session - 7-29-2021
 
Medieval Universities Essay
Medieval Universities EssayMedieval Universities Essay
Medieval Universities Essay
 
Machine learning pre requisite
Machine learning pre requisiteMachine learning pre requisite
Machine learning pre requisite
 

More from Tahmina Ferdous Tanny

More from Tahmina Ferdous Tanny (15)

Approaches of Philosophy of Science in Social Research
Approaches of Philosophy of Science in Social ResearchApproaches of Philosophy of Science in Social Research
Approaches of Philosophy of Science in Social Research
 
Impact of colonialism on british india and east pakistan
Impact of colonialism on british india and east pakistanImpact of colonialism on british india and east pakistan
Impact of colonialism on british india and east pakistan
 
Approcahes of developement
Approcahes of developementApprocahes of developement
Approcahes of developement
 
Land degradation
Land degradationLand degradation
Land degradation
 
Role of Elite in Social Development
Role of Elite in Social DevelopmentRole of Elite in Social Development
Role of Elite in Social Development
 
Reliability and validity
Reliability and validityReliability and validity
Reliability and validity
 
Survey Research Design
Survey Research DesignSurvey Research Design
Survey Research Design
 
Suo Motu Rule
Suo Motu RuleSuo Motu Rule
Suo Motu Rule
 
Scientific management
Scientific managementScientific management
Scientific management
 
Approcahes of developement
Approcahes of developementApprocahes of developement
Approcahes of developement
 
Assignment on development and undevelopment theory
Assignment on development and undevelopment theoryAssignment on development and undevelopment theory
Assignment on development and undevelopment theory
 
UNDP
UNDPUNDP
UNDP
 
Techniques of data collection in qualitative method
Techniques of data collection in qualitative methodTechniques of data collection in qualitative method
Techniques of data collection in qualitative method
 
Presentation on refugee crisis
Presentation on refugee crisisPresentation on refugee crisis
Presentation on refugee crisis
 
World war 1
World war 1World war 1
World war 1
 

Recently uploaded

SPLICE Working Group: Reusable Code Examples
SPLICE Working Group:Reusable Code ExamplesSPLICE Working Group:Reusable Code Examples
SPLICE Working Group: Reusable Code Examples
Peter Brusilovsky
 
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPSSpellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
AnaAcapella
 
Personalisation of Education by AI and Big Data - Lourdes Guàrdia
Personalisation of Education by AI and Big Data - Lourdes GuàrdiaPersonalisation of Education by AI and Big Data - Lourdes Guàrdia
Personalisation of Education by AI and Big Data - Lourdes Guàrdia
EADTU
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
CaitlinCummins3
 
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
httgc7rh9c
 

Recently uploaded (20)

SPLICE Working Group: Reusable Code Examples
SPLICE Working Group:Reusable Code ExamplesSPLICE Working Group:Reusable Code Examples
SPLICE Working Group: Reusable Code Examples
 
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPSSpellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
 
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptxCOMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdfUGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
 
Personalisation of Education by AI and Big Data - Lourdes Guàrdia
Personalisation of Education by AI and Big Data - Lourdes GuàrdiaPersonalisation of Education by AI and Big Data - Lourdes Guàrdia
Personalisation of Education by AI and Big Data - Lourdes Guàrdia
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptx
 
How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
 
Play hard learn harder: The Serious Business of Play
Play hard learn harder:  The Serious Business of PlayPlay hard learn harder:  The Serious Business of Play
Play hard learn harder: The Serious Business of Play
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024
 
e-Sealing at EADTU by Kamakshi Rajagopal
e-Sealing at EADTU by Kamakshi Rajagopale-Sealing at EADTU by Kamakshi Rajagopal
e-Sealing at EADTU by Kamakshi Rajagopal
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024
 
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
 
FICTIONAL SALESMAN/SALESMAN SNSW 2024.pdf
FICTIONAL SALESMAN/SALESMAN SNSW 2024.pdfFICTIONAL SALESMAN/SALESMAN SNSW 2024.pdf
FICTIONAL SALESMAN/SALESMAN SNSW 2024.pdf
 
21st_Century_Skills_Framework_Final_Presentation_2.pptx
21st_Century_Skills_Framework_Final_Presentation_2.pptx21st_Century_Skills_Framework_Final_Presentation_2.pptx
21st_Century_Skills_Framework_Final_Presentation_2.pptx
 
AIM of Education-Teachers Training-2024.ppt
AIM of Education-Teachers Training-2024.pptAIM of Education-Teachers Training-2024.ppt
AIM of Education-Teachers Training-2024.ppt
 
OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...
 
How to Manage Website in Odoo 17 Studio App.pptx
How to Manage Website in Odoo 17 Studio App.pptxHow to Manage Website in Odoo 17 Studio App.pptx
How to Manage Website in Odoo 17 Studio App.pptx
 
diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....
 

Quantitative data analysis

  • 1. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 1 Quantitative Research In quantitative research, the investigator identifies a research problem based on trends in the field or on the need to explain why something occurs. Describing a trend means that the research problem can be answered best by a study in which the researcher seeks to establish the overall tendency of responses from individuals and to note how this tendency varies among people. For example, you might seek to learn how voters describe their attitudes toward a bond issue. Results from this study can inform how a large population views an issue and the diversity of these views (Cresswell, 2001, p-13) Characteristics of Quantitative Research In quantitative research the major characteristics are (Cresswell, 2001, p-13):  Describing a research problem through a description of trends or a need for an explanation of the relationship among variables.  Providing a major role for the literature through suggesting the research questions to be asked and justifying the research problem and creating a need for the direction (purpose statement and research questions or hypotheses) of the study.  Creating purpose statements, research questions, and hypotheses that are specific, narrow, measurable, and observable.  Collecting numeric data from a large number of people using instruments with preset questions and responses.  Analyzing trends, comparing groups, or relating variables using statistical analysis, and interpreting results by comparing them with prior predictions and past research.  Writing the research report using standard, fixed structures and evaluation criteria, and taking an objective, unbiased approach. Quantitative Analysis Quantitative analysis involves the techniques by which researchers convert data to numerical forms and subject them to statistical analyses (Babbie, 2001). It involves the numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect.
  • 2. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 2 • Involves techniques • Involve task of converting data into knowledge. 1. Quantification of data It is the process of converting data to a numerical format. This involves converting social science data into a machine-readable form—a form that can be read and manipulated by computers and similar machines used in quantitative analysis. Today, quantitative analysis is almost always handled by computer programs such as SPSS and Micro Case (Babbie, 2011, page, 422). 1.1 Coding To conduct a quantitative analysis, researcher often must engage in a coding process after the data have been collected. For example, open-ended questionnaire items result in non-numerical responses which need to be coded before analysis. Suppose, for example, that a survey researcher asks respondents, ―What is your occupation?‖ The responses to such a question will vary considerably. Although he or she can assign a separate numerical code to each reported occupation, this procedure will not facilitate analysis. The variable occupation has many pre- established coding schemes which differentiate and combines both to compare research results with other studies. One coding scheme distinguishes Professional and managerial occupations Clerical occupation Semi-skilled occupations Another coding scheme distinguishes sectors of economy Manufacturing health education commerce
  • 3. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 3 1.2 Developing code categories It means the categorization of data into distinct field. For example, if we are willing to conduct a survey in a self-administered campus about the existing problems facing by students. We can categorize the responses from students as financial concern, academic concern and non academic concern. Financial concern Academic concern Non-academic concern 1.3 Codebook construction The end product of the coding process is the conversion of data items into numerical codes. These, codes represent attributes composing variables. A codebook is a document that describes the locations of variables and lists the assignments of codes to the attributes composing those variables. For example, if we conduct a survey on then it will be the following: 1.4 Data entry Transforming data into quantitative form, researchers interested in quantitative analysis need to convert data into a machine-readable format, so that computers can read and manipulate the data. Tuition is too high Books cost too much Not enough financial aid Not enough classes offered Advisors are not available Too many requirements Cafeteria food is infected Not enough parking spaces Cockroaches in the dorm How often do you attend religious services? 0. Never 1. Less than once a year 2. About once or twice a year 3. Several times a year 4. About once a month 5.Every week
  • 4. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 4 Data-entry specialists (including yourself) could enter the data into, say, an SPSS data matrix or into an Excel spreadsheet to be imported later into SPSS. Types of Variables Analysis 2.0 Univariate analysis Univariate analysis is the analysis of a single variable for purposes of description. Frequency distribution, averages, and measures of dispersion would be examples of univariate analysis, as distinguished from bivariate and multivariate analysis. Univariate analysis covers the following points: 2.1 Frequency Distribution A description of the number of times that the various attributes of a variable are observed in a sample is called a frequency distribution. The report that 53 percent of a sample were men and 47 percent were women would be a simple example of a frequency distribution. It gives researcher some general picture about the dispersion, as well as maximum and minimum response. 2.2 Central Tendency A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. Univariate analysis One variable E.g. Age, gender, income etc. Bivariate analysis Two variables E.g. gender & CGPA Multivariate analysis Several variables E.g. Age, education, and prejudice
  • 5. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 5 2.2.1 Mean An average computed by summing the values of several observations and dividing by the number of observations. If you now have a grade number of observations point average of 4.0 based on 10 courses, and you get an F in this course, your new grade point (mean) average will be 3.6 2.2.2 Mode An average representing the most frequently observed value or attribute. If a sample contains 1,000 Protestants, 275 Catholics, and 33 Jews, Protestant is the modal category. 2.2.3 Median An average representing the value of the ―middle‖ case in a rank-ordered set of observations. If the ages of five men are 16, 17, 20, 54, and 88, the median would be 20. (The mean would be 39) Figure: Three averages (Babbie, 2001, page, 430)
  • 6. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 6 2.3 Dispersion Dispersion refers to the way values are distributed around some central value, such as an average. The simplest measure of dispersion is the range: the distance separating the highest from the lowest value. Range The simplest measure of dispersion is the range: the distance separating the highest from the lowest value. The range is a simple example of a measure of dispersion. Thus, we may report that the mean age of a group is 37.9, and the range is from 12 to 89 (Babbie, 2001, page, 431) Variance To describe the variability of the distribution. Standard deviation A measure of dispersion around the mean. It is an index of the amount of variability in a set of data. Higher SD means data are more dispersed. Lower SD means that they are more bunched together. Figure 14-4 illustrates the basic idea. Notice that the professional golfer not only has a lower mean score but is also more consistent represented by the smaller standard deviation. The duffer, on the other hand, has a higher average and is also less consistent: sometimes doing much better, sometimes much worse (Babbie, 2001, page, 432) Dispersion Range Variance Standard Deviation
  • 7. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 7 2.4 Continuous & Discrete Variables Continuous variable: A variable whose attributes form a steady progression, such as age or income. Thus, the ages of a group of people might include 21, 22, 23, 24, and so forth and could even be broken down into fractions of years. E.g. Income & age Scale: Interval & Ratio Discrete Variables: A variable whose attributes are separate from one another, or discontinuous, as in the case of gender or religious affiliation. Thus, in age (a continuous variable), the attributes progress steadily from 21 to 22 to 23, and so forth, whereas there is no progression from male to female in the case of gender. E.g. Marital status, gender & nationality. Scale: Nominal & Ordinal
  • 8. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 8 Modes should be calculated for nominal data, medians for interval data, and means for ratio data, not for nominal data. 2.6 Sub group comparison Univariate analyses describe the units of analysis of a study and, if they are a sample drawn from some larger population, allow us to make descriptive inferences about the larger population. The subgroup comparisons tell us how different groups in the population response to questions and see a pattern in the result (Babbie, 2011, page: 433). For example table represents whether marijuana should be legalized or not by age of respondents: Marijuana Legalization by Age of Respondents Source: General Social Survey, 2004, National Opinion Research Center. In response, 33.4 percent said it should and 66.6 percent said t.it shouldn‘t. 2.7 Collapsing” Response Categories It means combining the two appropriate range of variation to get better picture or meaningful analyses. Consider an example: Attitudes toward the United Nations: How is the UN doing in solving the problems it has had to face? Source: ―5-Nation Survey Finds Hope for U.N.,‖New York Times, June 26, 1985, p. 6 Under 21 21-35 36-54 55 & older Should be legalized 27% 40% 37% 24% Should not be legalized 73 60 63 76 100%= (34) (238) (338) (265)
  • 9. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 9 Part of the problem with Table lies in the table relatively small percentages of respondents selecting the two extreme response categories: the UN is doing a very good or a very poor job. This procedure is inappropriate in that it ignores all those respondents who gave the most positive answer of all: ―very good job.‖ In a situation like this, you should combine or ―collapse‖ the two ends of the range of variation combine ―very good‖ with ―good‖ and ―very poor‖ with ―poor.‖ If you were to do this in the analysis of your own data, it would be wise to add the raw frequencies together and recompute percentages for the combined categories (Babbie, 2011, page, 434) After collapsing extreme categories Source: ―5-Nation Survey Finds Hope for U.N.,‖New York Times, June 26, 1985, p. 6 2.8 Handling “Don’t Knows” option Whether to include or exclude the ‗don‘t knows‘ is harder to decide. It‘s usually a good idea to give people the option of saying ―don‘t know‖ or ―no opinion‖ when asking for their opinions on issues. In any event, the truth contained within your data is that a certain percentage said they didn‘t know and the remainder divided their opinions in whatever manner they did (Babbie, 2011, page, 436). 3.0 Bivariate Analysis The analysis of two variables simultaneously, for the purpose of determining the empirical relationship between them. The construction of a simple percentage table or the computation of a simple correlation coefficient are examples of bivariate analyses. However, as with univariate analysis the purpose of subgroup comparisons is largely descriptive. Most bivariate analysis in social research adds on another element: determining relationships between the variables
  • 10. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 10 themselves (Babbie, 2011, page, 436-37). For example: Religious Attendance Reported by Men and Women in 2004. Table describes the church attendance of men & women as reported in 1990 General Social Survey. It shows: comparatively & descriptively – that women in the study attended church more often as compared to men. Source: Babbie, 2011, page, 437 3.1 Constructing and Reading Bivariate Tables Steps involved in constructing of explanatory bivariate tables: 1. The cases are divided into groups according to attributes of the independent variable. 2. Each of these subgroups is then described in terms of attributes of the independent variable. 3. Finally, the table is read by comparing the independent variable subgroups with one another in terms of a given attribute of the dependent variable. Table: Gender and attitudes toward equality for men and women. Source: (Babbie, 2011, 439)
  • 11. Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 11 4.0 Multivariate analysis The analysis of the simultaneous relationships among several variables. Examining simultaneously the effects of Religious Attendance, Gender, and Age would be an example of multivariate analysis (Babbie, 2011, page, 441). . Source: General Social Survey, 1972 – 2006, National Opinion Research Center 5.0 Sociological diagnostics Sociological diagnostics is a quantitative analysis technique for determining the nature of social problems such as ethnic or gender discrimination (Babbie, 2011, page, 442) It can be used to replace opinions with facts and to settle debates with data analysis. For example Issues of gender and income. Because family pattern, women as group have participated less in in the labor force and many only begin outside the home after completing certain child-rearing tasks. Reference Babbie E. (2011). The Practice of Social Research, (Twelfth ed.). California: Wadsworth Cengage Learning. http://www.slideshare.net/asmasemma/quantitative-data-analysis Religious attendance Gender Age