Workshop on Data Analysis in Business and Law at University of Nigeria, Nsukka
UNIVERSITY OF NIGERIA, NSUKKA
SCHOOL OF POSTGRADUATE STUDIES
ICT/DATA ANALYSES IN
BUSINESS AND LAW
Resource Person
PROF. GEREALDINE UGWUONAH
DEPT. OF MARKETING
Phone:08033491228
OUTLINE
•
This paper is divided into three parts
namely:
Introduction
Measurement and Scaling
Data preparation
Data Analysis and Interpretation
GOALS AND OBJECTIVES
At the end of this Workshop, you should learn about:
•
Dynamics of Measurement and Scaling
•
Types of Variables
•
Procedures for Data Analysis
•
Interpretation of Results
1. INTRODUCTION
Information and communication technology (ICT) has
contributed immensely to social and economic research. ICT
incorporates electronic technologies and techniques used to
manage information and knowledge, including information-
handling tools used to produce, store, process, distribute and
exchange information.
Benefits of ICT in research can be achieved through access to
online resources like e-journal, online survey, digital data
capture, data sharing, storage, data analysis and report
production. For the purpose of this workshop, we shall be
concentrating on data analysis.
Data Analysis
is the process of systematically applying statistical
and/or logical techniques to describe and illustrate, condense
and recap, and evaluate data. There are two major types ;
Exploratory and descriptive data analyses and Inferential data
analysis. Exploratory data analysis explores the data by inspecting
the distribution of each variable. Descriptive statistics are used to
describe the basic features of the data in a study and can be in
form of table, charts and cross tabulation. Inferential data
analysis provides a way of drawing inductive inferences from data
and distinguishing the signal (the phenomenon of interest) from
the noise (statistical fluctuations) present in the data Shamoo
and Resnik (2003).
2.
It is important to ensure data integrity and accuracy
as well as use of appropriate statistical tool before
carrying out data analysis. A violation of data integrity
rule and improper statistical analyses distort scientific
findings, mislead casual readers (Shepard, 2002), and
may negatively influence the public perception of
research. Integrity issues are just as relevant to analysis
of non-statistical data as well.
•
Dynamics of Measurement and Scaling
Nominal scale
:
A nominal scale is the simplest type of scale. The numbers or
letters assigned to objects serve as labels.
Mention some examples of nominal scale.
An ordinal scale:
An ordinal scale arranges objects in an ordered relationship.
Most ordinal scales are obtained through ranking.
Ordinal scales answers the question of whether objects possess
more or less of what is being. Give four examples.
Interval scales
In interval scales, the numbers obtained represent
equal increment of the attribute being measured. They
also measure the order or distance in units of equal
intervals. The location of the zero point in interval scale
is arbitrary. Examples include temperature in degree
Fahrenheit.
Ratio Scale
•
Ratio scales have absolute rather than
relative quantities. The absolute zero
represents a point on the scale where
there is an absence of the given attribute.
Examples include, age, money and weights.
•
Interval and ratio scale are desirable
because virtually the entire range of all
statistical analysis can be performed on
them.
Types of scales and their properties
Data Preparation
Data preparation is concerned with the following four major activities:
Data editing
Data cleaning
Data coding
Data adjustment and replacement of missing data
Data editing
Helps to locate :
•
Inconsistent or out of range responses.
•
Omissions:
When respondents intentionally or unintentionally fail
to answer some questions.
•
Ambiguity
: When it is unclear as to which option the respondent
chose.
•
Lack of Cooperation
: When respondents refuse to follow the
instruction and ticks arbitrarily.
•
Ineligible respondents
: When a respondent who is not qualified to
be in the sample is found in it.
Data coding:
Translates responses into values suitable for
computer entry and statistical analysis
.
Data cleaning
Once the data have been keyed in the data, they are to be
subjected to a series of computer checks to “clean” them. Checks
can be obtained through single table or cross table frequency
distribution. Two major checks should be carried out.
(a)Range checks
(b)Consistency checks
Data Analysis
There are two major aspects of data analysis, namely:
•
Descriptive and
•
Inferential data analysis.
Descriptive Data Analysis
•
Provide simple summaries about the sample
•
Examples are: count, mean, median, mode and measures of
dispersion like variance and standard deviation.
•
Descriptive statistics may present data in tables, cross
tabulations and/or graphs
Common Inferential Statistics
•
Chi-square
•
t-test
•
Analysis of variance (ANOVA)
•
Correlation analysis
•
Regression analysis
•
Discriminant analysis
•
Factor analysis
Demonstration of Statistical Analysis
We shall use the remaining time to
demonstrate how to analyse data
using SPSS.
WHAT I HAVE LEARNT FROM THE WORKSHOP
Want to share?
Summary/Recommendations/Conclusion
In the course of this module, we have gone
through measurement and scaling, data editing
and analysis of data using descriptive and
inferential statistics. This presentation gives the
participant an idea of what data analysis is all
about and how data can be analysed
Thank You!
This workshop at the University of Nigeria, Nsukka focuses on data analysis in business and law, covering topics such as measurement, scaling, data preparation, analysis, and interpretation. Participants will learn about the importance of data integrity, statistical tools, and the benefits of ICT in research. The workshop aims to enhance understanding of exploratory and descriptive data analyses, inferential data analysis, and ensure accurate research findings.
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UNIVERSITY OF NIGERIA, NSUKKA SCHOOL OF POSTGRADUATE STUDIES ICT/DATA ANALYSES IN BUSINESS AND LAW Resource Person PROF. GEREALDINE UGWUONAH DEPT. OF MARKETING Geraldine.ugwuonah@unn.edu.ng Phone:08033491228
OUTLINE This paper is divided into three parts namely: Introduction Measurement and Scaling Data preparation Data Analysis and Interpretation
GOALS AND OBJECTIVES At the end of this Workshop, you should learn about: Dynamics of Measurement and Scaling Types of Variables Procedures for Data Analysis Interpretation of Results
1. INTRODUCTION Information and communication technology (ICT) has contributed immensely to social and economic research. ICT incorporates electronic technologies and techniques used to manage information and knowledge, including information- handling tools used to produce, store, process, distribute and exchange information. Benefits of ICT in research can be achieved through access to online resources like e-journal, online survey, digital data capture, data sharing, storage, data analysis and report production. For the purpose of this workshop, we shall be concentrating on data analysis.
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. There are two major types ; Exploratory and descriptive data analyses and Inferential data analysis. Exploratory data analysis explores the data by inspecting the distribution of each variable. Descriptive statistics are used to describe the basic features of the data in a study and can be in form of table, charts and cross tabulation. Inferential data analysis provides a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data Shamoo and Resnik (2003).
2. It is important to ensure data integrity and accuracy as well as use of appropriate statistical tool before carrying out data analysis. A violation of data integrity rule and improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well.
Nominal scale: A nominal scale is the simplest type of scale. The numbers or letters assigned to objects serve as labels. Mention some examples of nominal scale. An ordinal scale: An ordinal scale arranges objects in an ordered relationship. Most ordinal scales are obtained through ranking. Ordinal scales answers the question of whether objects possess more or less of what is being. Give four examples.
Interval scales In interval scales, the numbers obtained represent equal increment of the attribute being measured. They also measure the order or distance in units of equal intervals. The location of the zero point in interval scale is arbitrary. Examples include temperature in degree Fahrenheit.
Ratio Scale Ratio scales have absolute rather than relative quantities. The absolute zero represents a point on the scale where there is an absence of the given attribute. Examples include, age, money and weights. Interval and ratio scale are desirable because virtually the entire range of all statistical analysis can be performed on them.
Types of scales and their properties Types Measurement Scale of Typical Application Statistics/Statistical Tests Nominal Classification (by sex, geographic area, social class) Rankings (preference, class standing) Percentages, mode/chi-square Ordinal order or rank Percentile, medium rank-order Friedman ANOVA Interval Index temperature attitude measures number, scales, Mean, standard deviation, product moment correlations,/t-test, ANOVA, regression, factor analysis. Ratio Scales, incomes, units produced, cost, age. Geometric and harmonic mean, coefficient of variation. Mean, standard deviation, product moment correlations,/t-test, ANOVA, regression factor analysis.
Data Preparation Data preparation is concerned with the following four major activities: Data editing Data cleaning Data coding Data adjustment and replacement of missing data
Data editing Helps to locate : Inconsistent or out of range responses. Omissions: When respondents intentionally or unintentionally fail to answer some questions. Ambiguity: When it is unclear as to which option the respondent chose. Lack of Cooperation: When respondents refuse to follow the instruction and ticks arbitrarily. Ineligible respondents: When a respondent who is not qualified to be in the sample is found in it.
Data coding: Translates responses into values suitable for computer entry and statistical analysis. Number 1 Do you eat indomie Question Question Description Range of Permissible Values 0 = no, 1 = yes, 9 = blank 0 = husband, 1 =Myself, 2 = father, 3 = mother, 4 = relative, 5 = friend, 6 = other, 9 = blank. 1= supermarket, 2=open market, 9=blank 0 = less than 1 month, 1 = three months, 2 = six months, 3 = year, 4 = other, 9=blank 2 Who is buys it for you? 3 Where do you buy it? 4 How often do you buy indomie? 5=to a very large extent, 4= to a large extent, 3=to a fair extent, 2=to a low extent, 1=to a very large extent, 9=blank 5To what extent are satisfied with the flavour of indomie?
Data cleaning Once the data have been keyed in the data, they are to be subjected to a series of computer checks to clean them. Checks can be obtained through single table or cross table frequency distribution. Two major checks should be carried out. (a)Range checks (b)Consistency checks
Data Analysis There are two major aspects of data analysis, namely: Descriptive and Inferential data analysis.
Descriptive Data Analysis Provide simple summaries about the sample Examples are: count, mean, median, mode and measures of dispersion like variance and standard deviation. Descriptive statistics may present data in tables, cross tabulations and/or graphs
Common Inferential Statistics Chi-square t-test Analysis of variance (ANOVA) Correlation analysis Regression analysis Discriminant analysis Factor analysis
Demonstration of Statistical Analysis We shall use the remaining time to demonstrate how to analyse data using SPSS.
WHAT I HAVE LEARNT FROM THE WORKSHOP Want to share?
Summary/Recommendations/Conclusion In the course of this module, we have gone through measurement and scaling, data editing and analysis of data using descriptive and inferential statistics. This presentation gives the participant an idea of what data analysis is all about and how data can be analysed Thank You!
