Research Method for Business chapter 11-12-14

2. For smaller samples (N ‹ 100), there is
little point in sampling. Survey the
entire population.
1. The larger the population size, the
smaller the percentage of the
population required to get a
representative sample
Rules of thumb for determining the
sample size...

4. If the population size is around 1500,
20% should be sampled.
3. If the population size is around 500
(give or take 100), 50% should be
sampled.
5. Beyond a certain point (N = 5000),
the population size is almost
irrelevant and a sample size of 400
may be adequate.
Rules of thumb for determining the
sample size...

Technique Strengths Weaknesses
Nonprobability Sampling
Convenience sampling
Least expensive, least
time-consuming, most
convenient
Selection bias, sample not
representative, not recommended for
descriptive or causal research
Judgmental sampling Low cost, convenient,
not time-consuming
Does not allow generalization,
subjective
Quota sampling Sample can be controlled
for certain characteristics
Selection bias, no assurance of
representativeness
Snowball sampling Can estimate rare
characteristics
Time-consuming
Probability sampling
Simple random sampling
(SRS)
Easily understood,
results projectable
Difficult to construct sampling
frame, expensive, lower precision,
no assurance of representativeness.
Systematic sampling Can increase
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Can decrease representativeness
Stratified sampling Include all important
subpopulations,
precision
Difficult to select relevant
stratification variables, not feasible to
stratify on many variables, expensive
Cluster sampling Easy to implement, cost
effective
Imprecise, difficult to compute and
interpret results
Table 11.3
Strengths and Weaknesses of Basic Sampling Techniques

1. Getting Data Ready for Analysis
• After data are obtained through questionnaires, interviews,
observation, or through secondary sources, they need to be
edited.
• The blank responses, if any, have to be handled in some way,
the data coded, and a categorization scheme has to be set up.
• The data will then have to be keyed in, and some software
program used to analyze them.
Ch- 11 : Data Analysis and Interpretation

1. Getting Data Ready for Analysis
2. Getting a feel for the data
3. Testing goodness of the data
Ch- 11 : Quantitative Data Analysis

DATACOLLECTION
Data analysis
Interpretation
of
results
Discussion
Research
question
answered
?
Getting data ready for
analysis
1. Coding & data entry
2. Editing data
3. Omission
4. Data transformation
Feel for
data
1. Frequencies
2. B&P charts
3. Measuring
of central
tendencies
Goodness
of data
Reliability
Validity
Hypotheses
testing
Appropriate
statistical
manipulations
Diagram 11.1
Flow diagram of data analysis process.

1.1 Editing Data
 Data have to be edited, especially when they relate to
responses to open-ended questions of interviews and
questionnaires, or unstructured observations.
 The edited data should be identifiable through the use of a
different color pencil or ink so that the original information is
still available in case of further doubts later.
 Incoming mailed questionnaire data have to be checked for
incompleteness and inconsistencies.
 Whenever possible, it would be better to follow up with
respondent and get the correct data while editing.

1.2 Handling Blank Responses
 Answers may have been left blank because the respondent did
not understand the question, did not know the answer, was not
willing to answer, or was simply indifferent to the need to
respond the entire questionnaire.
 If a substantial number of questions—say, 25% of the items in
the questionnaire—have been left unanswered, it may be a
good idea to drop the questionnaire.
 One way to handle a blank response to an interval-scaled item
with a mid-point would be to assign the midpoint in the scale as
the response to that particular item.
 An alternative way is to allow the computer to ignore the blank
responses when the analyses are done.

1.3 Coding the responses
The next step is to code the responses. Scanner sheets facilitate
the entry of the responses directly into the computer without
manual keying in of the data.
Also one may use a coding sheet first to transcribe the data from
the questionnaire and then key in the data.
1.4 Categorization
At this point it is useful to set up a scheme for categorizing the
variables such that the several items measuring a concept are all
grouped together.
Responses to some of the negatively worded questions have also
to be reversed so that all answers are in the same direction.

1.5 Entering Data
 If questionnaire data are not collected on scanner answer
sheets, which can be directly entered into the computer as a
data file, the raw data will have to be manually keyed into the
computer.
 Raw data can be entered through any software program.
 For instance, the SPSS Data Editor, which looks like a spread
sheet, can enter, edit, and view the contents of the data file.

2. Data Analysis
2.1 Basic Objectives in Data Analysis
In data analysis we have three objectives:
1) Getting a feel for the data
2) Testing the goodness of data
3) Testing the hypotheses developed for the research.

2. Data Analysis
2.1 Basic Objectives in Data Analysis
1) The first objective - feel for the data will give preliminary ideas of
how good the scales are, how well the coding and entering of data
have been done, and so on.
2) The second objective— testing the goodness of data—can be
accomplished by submitting the data for factor analysis, obtaining
the Cronbach’s alpha or the split-half reliability of the measures,
and so on.
3) The third objective —hypotheses testing –is achieved by choosing
the appropriate menus of the software programs, to test each of
the hypotheses using the relevant statistical test. The results of
these tests will determine whether or not the hypotheses are
substantiated.

 Introducing:
Distribution Properties
The Standard Normal
Distribution
Properties:
1. _________________
2. _________________
3. _________________

 Empirical Rule (The 68-95-99.7 Rule): If the
distribution is normal, then
 Approximately 68% of the data falls within one
standard deviation of the mean
 Approximately 95% of the data falls within two
standard deviations of the mean
 Approximately 99.7% of the data falls within three
standard deviations of the mean

Empirical Rule

 If the data distribution is bell-shaped,
then the interval:
 contains about 68% of the values
in the population or the sample
The Empirical Rule
1σμ 
μ
68%
1σμ

Chap 3-18
 contains about 95% of the values
in the population or the sample
 contains about 99.7% of the
values in the population or the sample
2σμ 
3σμ 
3σμ
99.7%95%
2σμ
The Empirical Rule
σ
σ

Chap 3-19
Shape of a Distribution
 Describes how data are distributed
 Measures of shape
 Symmetric or skewed
Mean = MedianMean < Median Median < Mean
Right-SkewedLeft-Skewed Symmetric

Cronbach's is defined as
where is the number of components (K-items or testlets), the variance
of the observed total test scores, and the variance of component i for
the current sample of persons. See Develles (1991).
Numerical

2.2 Feel for the Data (visual summary)
 We can acquire a feel for the data by checking the central
tendency and the dispersion.
 The mean, the range, the standard deviation, and the variance
in the data will give the researcher a good idea of how the
respondents have reacted to the items in the questionnaire and
how good the items and measures are.
 The maximum and minimum scores, mean, standard deviation,
variance, and other statistics can be easily obtained, and these
will indicate whether the responses range satisfactorily over the
scale.
 A frequency distribution of the nominal variables of interest
should be obtained. Visual displays thereof through
histogram/bar charts, and so on, can also be provided through
programs that generate charts.

Frequencies
 Number of times various subcategories of a certain
phenomenon occur from which the percentage and the
cumulative percentage of their occurrence can be easily
calculated

Measures of central tendencies
 The Mean
 The Median
 Mode
 Range dispersion
 Variance
 Standard deviation
Numerical
distribution

The Normal Distribution Curve
0
0.005
0.01
0.015
0.02
0.025
0 20 40 60 80 100
It is bell-shaped and symmetrical about the mean
The mean, median and mode are equal
Mean, Median, Mode
It is a function of the mean and the standard deviation

 Average often means the ‘mean’
 Mean = total of the numbers divided by how many
numbers.
 Class shoe sizes: 3, 5, 5, 6, 4, 3, 2, 1, 5, 6
 Add up the numbers:
 3 + 5 + 5 + 6 + 4 + 3 + 2 + 1 + 5 + 6 = 40
 Divide by how many numbers:
 40 ÷ 10 = 4
 The class mean shoe size is 4
Mean;

Median;
 Median is the middle value
 Put the numbers in order
 Choose the number in the middle of the list.
 If there are 2 numbers in the middle then it is halfway
between them.
Class shoe sizes: 3, 5, 5, 6, 4, 3, 2, 1, 5, 6
Put in order: 1, 2, 3, 3, 4, 5, 5, 5, 6, 6
The class median shoe size is 4.5

Mode;
 Mode is the most common number
 Choose the number that appears the most frequently.
 Sometimes there may be more than one mode.
Class shoe sizes: 3, 5, 5, 6, 4, 3, 2, 1, 5, 6
Put in order: 1, 2, 3, 3, 4, 5, 5, 5, 6, 6
The class modal shoe size is 5.

Range; (dispersion)
 Range is how far from biggest to smallest.
 Take the smallest number from the largest.
Class shoe sizes: 3, 5, 5, 6, 4, 3, 2, 1, 5, 6
Put in order: 1, 2, 3, 3, 4, 5, 5, 5, 6, 6
Subtract smallest from largest: 6 – 1 = 5
Range: 5

2.3 Testing Goodness of Data
a. Reliability
 The reliability of a measure is established by testing for
both consistency and stability.
 Consistency indicates how well the items measuring a
concept hang together as a set.
 Cronbach’s alpha is a reliability coefficient that indicates
how well the items in a set are positively correlated to one
another.
 Cronbach’s alpha is computed in terms of the average
intercorrelations among the items measuring the concept.
 The closer Cronbach’s alpha is to 1, the higher the internal
consistency reliability.

 Another measure of consistency reliability used in specific
situations is the split-half reliability coefficient.
 Since this reflects the correlations between two halves of a set
of items, the coefficients obtained will vary depending on how
the scale is split. Sometimes split-half reliability is obtained to
test for consistency when more than one scale, dimension, or
factor, is assessed.
 The stability of measures can be assessed through parallel
form reliability and test-retest reliability.
 When a high correlation between two similar forms of a
measure is obtained, parallel form reliability is established.
 Test-retest reliability can be established by computing the
correlation between the same tests administered at two
different time periods.

b. Validity
 Factorial validity can be established by submitting the data for
factor analysis.
 The results of factor analysis (a multivariate technique) will
confirm whether or not the theorized dimensions emerge.
 Factor analysis would reveal whether the dimensions are
indeed tapped by the items in the measure, as theorized.
 Criterion-related validity can be established by testing for the
power of the measure to differentiate individuals who are
known to be different.

 Convergent validity can be established when there is high
degree of correlation between two different sources responding
to the same measure (e.g., both supervisors and subordinates
respond similarly to a perceived reward system measure
administered to them).
 Discriminant validity can be established when two
distinctly different concepts are not correlated to each other as,
for example
 courage and honesty;
 leadership and motivation;
 attitudes and behavior

2.4 Hypothesis Testing
 Once the data are ready for analysis, (i.e., out-of-range/missing
responses, etc., are cleaned up, and the goodness of the
measures is established), the researcher is ready to test the
hypotheses already developed for the study.
 In the Module at the end of the text book, the statistical tests
that would be appropriate for different hypotheses and for data
obtained on different scales are discussed.
3. Data Analysis and Interpretation
 Data analysis and interpretation of results can be best
understood by referring to an example of a business research
project.
 Please see Data Analysis discussion of Excelsior Enterprises
in the text book from Page 309-322.

4. Some Software Packages Useful for Data
Analysis
4.1 SPSS Software Packages
• SPSS has software programs that can create surveys
(questionnaire design) through the SPSS Data Entry Builder
• Collect data over the Internet or Intranet through the SPSS
Data Entry Enterprises Server,
• Enter the collected data through the SPSS Data Entry
Station, and SPSS 11.0 to analyze the data collected.

4.2 Various Other Software Programs
Go to the Internet and explore
http://www.asc.org.uk/Register/ShowPackage.asp?ID=162
and the subsequent IDs it indicates. It shows variety of software
programs with a wide range of capabilities. A few of these are:
1. Askia
2. ATLAS. ti
3. Bellview CATI
4. Brand2hand

4.3 Use of Expert Systems in Choosing the
Appropriate Statistical Tests
• The Expert System employs unique programming techniques to
model the decisions that experts make.
• A considerable body of knowledge fed into the system and some
good software and hardware help the individual using it to make
sound decisions about the problem that he or she is concerned
about solving.

4.3 Use of Expert Systems in Choosing the
Appropriate Statistical Tests
• Expert Systems relating to data analysis help the perplexed
researcher to choose the most appropriate statistical procedure
for testing different types of hypothesis.
• The Statistical Navigator is an Expert System that recommends
one or more statistical procedures after seeking information on
the goals.
• The Statistical Navigator is a useful guide for those who are well
versed in statistics but want to ensure that they use the
appropriate statistical techniques.

Ch-12 : Data Analysis
1. Data ware house
2. Data Mining
3. Operations
4. T-test from single mean

Data Warehousing?
 A Data Warehouse is a computerized collection of
mined data.
What is Data Mining?
 Data Mining is the process of collecting large amounts of
raw data and transforming that data into useful information.
 Data mining is the practice of searching through large
amounts of computerized data to find useful patterns or
trends (American Heritage Dictionary, 2008).

Data Warehousing Advantages
 Access to information
 Data Inconsistency
 Decrease Computing Cost
 Productivity Increase
 Increase company profits

Data Warehousing Disadvantages
 Data must be cleaned, loaded, and extracted
 80% of the overall process
 User Variability
 Proper Training
 Difficult to Maintain
 Incongruence among systems

Data Mining Applications
 Banking
 Detect Fraudulent Activity
 Insurance
 Risk Assessment
 Medicine/Healthcare
 Enhance Research
 Retail
 Track consumer buying trends

Data Mining Advantages
 Improves Customer Satisfaction/service
 Saves Time and Money
 Increases Sales Effectiveness
 Increases profitability

Data Mining Advantages
 Require skilled technical users to interpret and
analyze data from warehouse
 Validity of the patterns
 Related to real world circumstances
 Unable to Identify Casual Relationships
 Reserved for the few instead of the many

Conclusion/Analysis
 Data mining is the extraction of information that can
predict future trends & behaviors
 Requires a large amount of data to be collected, and then
stored in data warehouse
 Possible violation of privacy in some circumstances
 Government is getting involved with regulation, despite
the counterterrorism program being a possible violation

1. The Research Proposal
Contents:
a. The broad goals of the study
b. The specific problem
c. Details of study procedures
d. The Research Design
i. The Sampling Design
ii. Data Collection Methods
iii. Data Analysis
e. Time Frame of the Study
f. The Budget
Ch-14 : The Research Report

2. Written report
a. Descriptive Report
- Investigative
- Understand a problem
- Knowledge of a process
- Understand behavioral variables
b. Report to “Sell” and Idea or Project
- Launch of a New Product
- Investment in a Project
- Restructuring the Organization
- Implementing a new MIS

1. Title
2. Table of Contents
3. Executive Summary
4. Introduction
5. Research Design and
Methodology
a. Preliminary Data
Gathering
b. Literature Survey
c. Problem Definition
d. Theoretical Framework
e. Hypothesis
6. Data Collection
7. Data Analysis
8. Data Interpretation
a. Hypothesis
Testing
b. Main Conclusions
9. Limitations
10. Recommendations
11. References
12. Appendices
a. Secondary Data
b. Questionnaires
c. Other Supporting Data
3. FORMAT OF FINAL REPORT

4. Oral Presentation
Contents
a. Presentation Method: Power Point Slide Show
b. Visual Aids: Charts, graphs
c. Presenter’s appearance and style
d. Good use of Verbal communication Skills
e. Good use of Nonverbal Communication Skills
f. Handling Questions

Research Method for Business chapter 11-12-14

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