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 SamplingConvenience sampling

Least expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended fordescriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlledfor certain characteristics

Selection bias, no assurance ofrepresentativeness

Snowball sampling Can estimate rarecharacteristics

Time-consuming

Probability samplingSimple random sampling(SRS)

Easily understood,results projectable

Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.

Systematic sampling Can increaserepresentativeness,easier to implement thanSRS, sampling frame notnecessary

Can decrease representativeness

Stratified sampling Include all importantsubpopulations,precision

Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive

Cluster sampling Easy to implement, costeffective

Imprecise, difficult to compute andinterpret 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

DA

TA

CO

LL

EC

TIO

N

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 responsesThe 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 CategorizationAt 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 AnalysisIn 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 Analysis1) 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 PropertiesThe 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

Distribution Properties

Distribution PropertiesEmpirical 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

http://en.wikipedia.org/wiki/Variance

Numerical Distribution.ppt

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


http://www.asc.org.uk/Register/ShowPackage.asp?ID=162

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


Ch-14 : The Research Report

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


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

Contentsa. 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


Education

Research Method for Business chapter 11-12-14