mgt project manage

Course:

Research Methodology ( MGT 602)

Instructor

• Ayyaz Mahmood• Assistant Professor at CIIT• BS,MBA,MS, PhD(thesis under evaluation)• 12 years of teaching at University and 9 years of

Industry experience.• Supervised a number of MBA and MS thesis.• Published papers and attended conferences.

Introduction

•Overview of the course :

•Business research is an organized and deliberate process through which organization effectively learn new knowledge and help improve performance.

Introduction

• Objectives of the course :• To understand and develop a systematic approach to business research

• To emphasis on the relationship between theory , research and practice

• To Integrate different research activities in an orderly fashion

• Outcomes of the course are :• To formulate research questions• Develop theoretical framework• Develop hypotheses• Learn to select from different research methodologies

• Develop skills for data analysis and interpretation.

Research Methodology

COURSE OUTLINE:•Course Intro – Building blocks of science in research [1]•Broad problem area , Preliminary Information Survey [ 2]•Literature Review [2]•Literature Review[2]•Theoretical Framework [3]•Theoretical Framework[3]•Hypothesis Development [3]•Hypothesis Development[3]•Elements of Research Design (purpose, investigation type, researcher interference, study setting)[4]•Elements of Research Design (unit of analysis, time horizon, Measurement of variables) [4,6]•Measurement of variables (operational definition) [6]•Measurement of Variable (Scales) [6]•Validity and Reliability [6]•Data Collection Methods (Interviews , Questionnaire) [7]•Data Collection Methods (Questionnaire, observation) [7]•Sampling (Probability Sampling) [8]•Sampling (Non Probability Sampling) [8]•Experimental Design [5]•Refresher on Statistical Terms [9] •Introduction to SPSS •Introduction to SPSS•Data Analysis and Interpretation ( Getting data ready for analysis) [10]•Data Analysis and Interpretation (Feel for Data, Testing the goodness of Data) [10]•Data Analysis and Interpretation (Descriptive Statistics)[10]•Data Analysis and Interpretation (Inferential Statistics( Person Correlation, Hypothesis Testing t-test, ANOVA, Chi Square)[10]•Data Analysis and Interpretation (Inferential Statistics( Hypothesis Testing, Multiple Regression) [10]•Data Analysis and Interpretation (Inferential Statistics( Mediation, Moderation, Rank) (Hand out)•Research Report contents (Sample report)

Business Research Scenarios

A. A manager observes that the customers are not pleasedAre my customers satisfied from my product/service ?

B. It is observed that hydro construction project projectstend to have a low successes rate.What could be reasons behind it. ?

C. The new product introduced is not doing so well.Have we selected the right market, features or price ?

For all the above scenarios management needs to findreliable and creditable information to understand the issueand then take appropriate decisions in order to achieveperformance

Information

Reduces

Uncertainty

I don’t knowif we

shouldreduce our

product prices?

Define Business Research

• Business research is defined as the systematic and objective process of gathering, recording and analyzing data for aid in making business decisions.

• Research information is neither intuitive nor haphazardly gathered.

• Literally, research (re‐search) ‐“search again” • Business research must be objective• Detached and impersonal rather than biased• It facilitates the managerial decision process for all aspects of a business

Research Methods

• Is the way in which research studies are – designs– procedures – by which data is collected are analyzed.– We would be focusing on the survey methodology in which the research is conducted by collecting data and analyzing them to come up with answers to various issues of interest.

• The different areas of problem could be related to Finance, Accounting, HR, Marketing etc.

Types of Research

• Two purpose of research are– To solve a currently exiting problem in the work setting• (Applied Research )

– To add to the general body of knowledge• (Basic Research)

• Applied research is when research is done with the intention of applying the results of it’s findings to solving specific problem currently being experienced in the organization

• e.g. – To improve the attendance at an X organization – A transport service can be introduced, Has flextime improve the employee performance at a university)

• Basic research done mainly to improve our understanding of certain problems that are commonly occur in organizational setting and how to solve them

• e.g. – increase the productivity of clerical workers in service industry,

– increase the effectiveness of project oriented business

Research Philosophy and Choices

• Important assumptions about the way in which one views the world.

• These assumptions effect the research strategy and the methods you choose and practical considerations.

• Researcher concerned with facts, such as the resources needed for manufacturing will have different view on the way research

• Researcher concerned with the feelings and attitudes of the workers towards their managers in that same manufacturing process.

• Their strategies and methods probably will differ considerably and what is important and significant

Philosophy of Choices

• Deductive– Develop a theory and hypothesis (or hypotheses) and design a research strategy to test the hypothesis

• Inductive– Collect data and develop theory as a result of your data analysis

Characteristics of Good Research

• Purposive: Definite aim (Help reduce turnover, absenteeism, complete projects on time )

• Rigor: Sound methodological design, systematic and scientific. Avoid individual biases. (Manager interviews few employee on their preference for flexi time and device policy)

• Testability: After properly selecting the cases/respondents and collection of data logically developed hypothesis statements can be tested using statistical tests.

• Replicability: Applying the same method the finding from more than one study suggest the same results.

• Precision and Confidence: Study of the whole universe of item, events or population of interest is not possible. But we try to come close to reality as possible (precision)and also be confident of our findings that they are correct (confidence).

• Objectivity: The interpretation of the results should be based on facts, not on our own subjective feeling

• Generalizability: Applicability of the finding on a variety of firms/organization

• Parsimony: Simplicity in explaining the phenomena is preferred, rather than managing many factors and their effect (45% variability is explained by 4 variables and 48% variability is explained by 10 variables)

• Management and Behavioral science result are not 100% scientific or exact. We deal with measuring subjective feelings , attitudes, perceptions. Meeting all the characteristics of good research is difficult

Hypothetic‐Deductive Method of Research1. Observation2. Preliminary Information gathering3. Theory formulation4. Hypothesizing5. Further data collection6. Data Analysis7. Deduction

Observation

• One senses certain changes are occurring• New behaviors are surfacing in an environment• When one considers the situation important then move to the next step– E.g. Customers are not pleased as they used to be. Are customers at the store are grumbling or complaining.

Preliminary Information Gathering

• Know more about what has been observed• Talk to more people about it( other employees,customers)

• Know what is happening is happening and why.– E.g. Talk to customers if they are happy with theproduct or service. The customer might be happywith the products but the problem is that therequired products are out of stock and salesperson are not helpful. The salesman input on thisissues reveals that the factory does not deliver ontime so in order to satisfy the customer thesalesmen communicates different delivery dates.

Hypothesizing

• Some testable or educated supposition are made– E.g. – If sufficient inventory is made customers would be less dissatisfied customers

– Accurate and timely information of the delivery to the sales person can also reduce the dissatisfied customer.

Further Scientific Data Collection

• Data with respect to each variable in the hypothesis need to be obtained.

• E.g. – Measure the current level of customer satisfaction and measure the satisfaction level when the stocks are made readily available.

– Measure the current level of accuracy of information to sales person on the stock and the satisfaction level of customer and then measure them again once the level of information has increased.

Data Analysis

• Data gathered statistically is analyzed and see if the hypothesis have been supported or not.– E.g.

• Do an correlation analysis of the tow factors like level of information and satisfaction.

Deduction

• Arriving at a conclusion by interpreting the meaning of the results of the data analysis.– E.g.– If the customer satisfaction has increase by certain amount when the availability of information and the stock.

– We could recommend that these two factors influence the satisfaction of the customers

Recap lecture• We tried to examine what research is?• Research Philosophies and choices• We considered the two types of research• Hall Marks of Research (Purposive, rigor, testable, replicabilty,

precision and confidence, objectivity , genralizability and parsimony)

• The seven steps of hypothetic deductive research method

1. Observation2. Preliminary Information gathering3. Theory formulation4. Hypothesizing5. Further data collection6. Data Analysis7. Deduction


Lecture No : 2

Recap lecture 1• We examined what is research?

– Systematic effort to investigate a problem

• Introduced the Types of research– Applied (solve a current problem of org) – Basic (improve understanding of a problem)

• Why managers should know about research– Identify problems , discriminate b/w good and bad research,

appreciate the multiple influences of different factors ,etc.

• Hall Marks of Scientific Research.– Purposive, Rigor, Testability, Replicability, Precision/confidence,

Objectivity, Generalizbility, Parsimony

• Building Blocks of Scientific Research– Observation, identification of problem area, Theoretical Framework,

Hypothesis, Construct, Concepts operations definitions, Research Design, Data Collection , Analysis, Interpretation, implementation/refinement of theory

Lecture Objective

• To identify broad problem areas that are likely be studied in Organization

• What preliminary information in the work setting can be collected.

• To conduct a literature survey• To develop relevant and comprehensive

bibliography for any organization research area.• To write a literature review • To state research problems clearly and precisely

Outcomes

• We will examine ways to identify variables that would be relevant to the problem situation

• We would be able to develop a literature review.• We would be able to developing specific problems

statements.

Research Process Steps

1. Observation2. Preliminary Data collation3. Problem definition4. Theoretical Framework(variables identification)5. Generation of Hypothesis6. Research Design7. Data Collection & Interpretation8. Deduction 9. Report writing (or other wise)

The Research Process for Applied and Basic Research

• Step 1 to 5 are part of the process to identify the Broad Problem Area, literature review, problem statement,

conceptual framework and the hypothesis generation.

• Step 6 and 7 are part of the design which involves planning of the actual study , location, how to select sample, collect data, and analyze data.

• Step 8 and 9 denote the final deductions from the hypotheses testing. – If all hypothesis are substantiated and research questions

are fully answered we would try to find different ways to solve the problem.

– If not all hypotheses not support we try to examine the reasons for this

Broad Problem Area

• Identify the broad problem area( observation / focus)

• The broad problem area refers to the entire situation where one sees as a possible need for research and problem solving e.g.

– Training programs are perhaps are not effective as were anticipated.

– An increase in the dis-satisfaction of Customers

– Minority groups not making career progress

Broad Problem Area (cont..)

• The specific issue might not be very clear.• The issue could pertain to

– Problems currently existing in an organization– Areas where the managers believes can have

improvements– For better understanding of a Phenomena– Some empirically research is needed.

Example(s):

• Current existing problem: (The removal is essential as it can effect the routine operations of the organization)– People are not regularly attending their work.

• Require Improvement: (The situation needs to enhanced to ensure a better performance of the organization)– People might come but do not always show a 100% commitment to

their work

• Conceptual Issue: (Define the concept, performance)– What is performance (org performance / Employee performance– . How to measure )

• Empirical: (Test empirically )– Attendance and performance related.

Broad Problem Areas

Career progress

Attendance

Flexi Time

Management of complex project

Sales

Preliminary Information Collection

• The broad problem area is narrowed down to specific issues for investigation after some preliminary information gathering.

• This may take the forms of interviews and library research

• i.e. we try understand the problem in more detail and and develop a theory in which we try to illustrate the possible variables that might influence the problem.

Nature of preliminary information collected

• The preliminary information collected can be collected from

– Background info of the org/secondary information

– Prevailing Knowledge on the Topic

Background Info of the org/secondary Info

• Before conducting the first interview – Origin and history of the company ‐Size– Charter ‐ Resources – Charter ‐ Financial position etc

• For example information gathered on the financial status of organization can help identify if the organization cash flow are bad that might indicate a high rate of return of the products.

• This information could be used to gather further information and discussion while interviewing .

• We need to use good judgment as to what kind of preliminary data is needed

• Main idea is to identify the real problems

• After the interviewing the researcher needs to tabulate the various types of information and determine if there are any patterns to the responses.

Prevailing Knowledge on the Topic

• Certain factors are frequently mentioned e.g. untrained personnel , un safe work environment etc

• This gives the researcher a good idea about how to proceed to the next step of surveying the prevailing knowledge on the topic through literature review

• The literature can help see how other have perceived these factors in other work settings.

Literature Survey

• Literature survey is the documentation of a comprehensive review of the published and unpublished work from secondary sources of data in the area of specific interest to the researcher.

• Library, books, WWW, magazines, conference proceedings, thesis, government publications, and financial reports.

Why have Literature Survey

• A good literature survey ensures that:– Distinction between symptoms and real problem– Important variables are identified– Develop theoretical framework and hypothesis– Problem statement can be made with more precision.– Avoid in reinventing the wheel.– Recognition in the scientific community

Conducting the literature Survey

1. Identify the relevant sources2. Extracting the relevant information3. Writing up the literature review

• Relevant source– Bibliographic database (article name, date, author..)– Abstract Database (all above + summary)– Full

Evaluating the Literature

• Searching might exhibit hundreds of articles and books• Careful selection is needed • We need to find (A)Relevance (B) Quality of the

literature• (A) Relevance

– Titles of articles/books– Abstracts of an article

• Overview of the purpose• General research plan• Findings• Conclusion

– Introduction in an article• Overview of the problem addressed • Specific research objectives• Ends with the summary research questions

– Table of contents in a book• Quality

– You need to ask • Is the research question / problem clearly stated• Does this study build on previous research• Used appropriate quantitative and qualitative tool etc….

– You need to also check if it has been published in good journal• i.e peer reviewed , impact factor

Extracting the Relevant Information

• From the articles extract these following information– Problem– Variables– Sample– Data collection– Data analysis– Results– Conclusion

Writing up the literature Review• Documenting of relevant studies citing the author and

the year of the study is called literature review.

• Reference key studies , Reference books and article which are latest

• The literature survey is a clear presentation of relevant research work done thus far in the area of investigation.

• All relevant information should be in a coherent and logical manner instead of chronological manner

Writing up the literature Review(Cont..)

• Introduce the subject (Importance + Purpose of the study + define the key concepts)

• Identify the major research literature and the gaps

• Finally discuss the variables and their relationship to help you to formulate your frame work and hypothesis.

• Article “ Effects of Flexi Time on Employee Attendance and Performance”

Examples of Bibliography and References (APA)

• Lehsin, C. B. (1997). Management on the World wide Web. Engle wood Cliff, Prentice Hall.

• More examples on pg 61

Referencing and Quotation in Literature review

• Todd (1998) has show• In 1997, Kyle compared the dual careers and dual …• Perter Drucker (1986) “staff work should be limited to

few tasks of high priority.”

• More examples on page 64

Defining Problem Statement

• After interviews and literature review the researcher are in better position to narrow down the problem from the broad problem area to more specific problem.

• A problem statement is a clear, precise statement of the specific issue that research intends to address.

• A problem could be an interest in a issues where finding the right answer might help to improve the existing situation.

• We need to be care full that we do not define Symptoms as problems

Symptom Problem v.s. Real Problem

• Symptom Problem: Low Productivity

• Real Problem : Low moral

• Solution to Symptom is increase in piece rate

• Solution to Real Problem : Recognition

Examples of Well defined Problem Statements

• To what extent has the new advertising campaign been successful in creating a high quality , customer centered corporate image?

• How has new packaging affected the sales of the product?

• How do price and quality rate on consumer’s evaluation?

• Does better automation lead to greater investment ?

Example of Broad Problem Area, Lit Review, Problem

Statement.• Broad Problem Area: Low productivity of employee.

• Lit Review: faulty machines, low pay rate, low moral

• Problem Statement: Is the low moral of employee at plant x the cause of low productivity?

Exercise

• Identify the Broad Problem area, define the problem, and how would you proceed further.

• Pioneers minivans and pickup take a big share of the truck market , while it’s cars lag behind those of its competitors. Quality issues like faulty electrical system, and head lights are a major concern to the management.

Summary

• Identify the first three steps in the research process• Identification of the broad problem area

– Preliminary information gathering through interviews and literature survey

– Problem definition• APA format of referencing • Next lecture we would cover the next two steps of the

research process– Framework– Hypotheses


Lecture No : 3

Recap lecture 2• Broad problem: the entire situation where

one sees a need for problem solving and research.

• Literature Review: To understand the problem more detail information is needed

• Different sources of information is gathered from books, reports, published research papers etc.

Recap lecture 2

• More aspects of research are exposed.• More variables which play an important role

are uncovered.• This allows us to develop a more robust

theory.• We start documenting a comprehensive

review• To conclude we are able to identify the gaps

and develop our precise problems statements.

Lecture Objective

• Revisit at literature review– Why literature review is important– Methods of writing a review– Contents of a review

• Developing a theoretical framework– Identify the relationships and the theory

supporting the relationship• Describing Variables

– Describing what are variables and the different types

Purpose of Literature Review

• Every research study requires the researcher to review pertinent literature on the topic.

• 1. To avoid unnecessary duplication of research.• 2. To identify variables that may influence the

problem• 3. To identify promising procedures and

instruments• 4. To limit the problem.

Two steps in conduction literature review

• Survey of literature (search)• Documenting of the literature (write)

Survey of literature

• Survey different sources– Books– Research Articles– Theses– Conference preceding

• You can obtain them from– Libraries– Internet– Online databases (Full text, abstract)

Documenting the literature

• Three activities are involved while documenting the literature which you have surveyed– Method of documenting the list of reviewed articles. (Modes)

– Referencing and quoting the studies (Cite)– Organizing and documenting the contents of the reviewed articles (writing the review)

Method of documenting the list of reviewed articles.

• References / Bibliography is a list of work that is relevant to the main topic arranged in an alphabetical order.

• The difference between reference list and bibliography is that reference list is a subset of the list of articles which have been referenced in the research.

• Bibliography is a list which includes all the referenced and non referenced articles in your research but are relevant to your research

Examples of Modes of Reference listingThere are different modes of referencing in business research. For example the APA (Publication of Manual of the American Psychological Association), Chicago Manual Style, Harvard style, Turabian Style.Each manual specifies with examples how books, newspaper, research journal are to be referenced in your research. Following are the example in APA style

Referencing and quoting the studies

• Cite the references in the body of the paper using author-year method of citation; i.e. surname of author(s) and the year of publications

• E.g. Kaleem(2004) has shown….• In the recent studies of employee motivation

(Freeman,2007 ;Mitnzberg, 2007) it has …• In 1997, Kyle compared the different models of

motivation..• As pointed out by (Tucker & Snell, 1989),…..

Referencing and quoting the studies (cont …)

Organizing and documenting the contents of the reviewed articles

• While writing the review the text needs to arranged in the following manner

• 1. Introduction -– Importance of the subject , – states the purpose or scope of the review

• 2. Define the key concepts– What are the different definitions found in the

literature. Which definition is better or much closer your research objective.

Organization of a Literature Review:

• 3. Critical review -– Describe the relationships between the different

variables identified in the previous studies– Do not list one study after another, but rather

classify, compare & contrast as they relate to your problem statement.

– Organize the review around different themes.• 4. Summarize

– states the status of what exists on the topic and identifies the gaps which provide the rationale for your study.

Example of a short Review

• Pg 44

Example of a short review

• Introduction to Organization effectiveness• Identified the problem and the purpose

– No consensus on the how to conceptualize and measure OE

• Summarize the previous work and identify the gaps in the literature – Variables from different streams related to the

OE uncovered– Leading to the forming of the research

questions

• Questions – What could be the dimensions used for

measuring OE ?– What factors effect the OE ?

• Once the research questions have been stated then one is ready to develop a theoretical frame work of their research

• While developing your theoretical frame work you basically

• Theorize on the bases of your belief that how are certain phenomena's are related.

• So theoretical framework is a representation of your beliefs on how certain phenomena ( or variables or concepts ) are related to each other(model) and an explanation of why you believe that these are associated with each other (theory)

Theoretical Framework

• So there are two components to theoretical frame work– Identification of variables and their

relationship– Describing the relationship with arguments

• While identifying the different variables we need to differentiate between the different kinds of variables

Variables

• Any thing that can take on different or varying values is a variable

• Values can be different at various times for the same object or person or at the same time for different objects or persons E.g.– Production units (Employee 1 (10 units on Monday) Production

units (Employee 1 (11 units on Tuesday)– Production units (Employee 2 (12 units on Monday)– Production units (Employee 2 (10 units on Tuesday)– Attendance at department x on Monday(10), Tuesday(2)

Types of Variables

• Independent• Dependent• Moderating• Mediating

Types of Variables

• Dependent – (Criterion Variable) – primary interest – Describe or explain the variability or predict it.– We study what variables influence dependent

variable – So by studying these we might able to find a

solution of the problem– E.g. Sales are low , employee loyalty is

dropping

• Independent – (Predictor variable)– Which influences the dependent variable– The influence might be positive or negative– When independent variable is present the

dependent variable is also present.– With each unit of increase in independent

variable there is an increase or decrease in the dependent variable

– E.g. Advertising on sales, recognition on loyalty

• Moderating (surfaces in between the independent and dependent at a given time)

• Mediating (Effects the relationship between independent and dependent)

Exercise : List the independent variable

• A manager believes that good supervision and training would increase the production level of the workers.

Recap

• Literature Review involves searching and documenting

• There are different formats of Documenting (APA)

• There is a structure of review (importance, objectives, definitions, relationships identified, gaps)

• Theoretical framework is representation of your belief on how variables related and why

• Variables are of 4 different kinds


Lecture No : 4(Theoretical Framework)

Recap

• Literature Review involves searching and documenting

• There are different formats of Documenting (APA)

• There is a structure of review (importance, objectives, definitions, relationships identified, gaps)


• Variables are of 4 different kinds

Theoretical Framework• After conducting literature review, survey and defining the problem (research questions)

• We develop our theoretical framework• Theoretical framework is a conceptual model of how we theorarize the relationships among several factors that have been identified to the problem.– Problem is depleting sales – Factors influencing are quality of products, price, competition etc ( based on the literature)

• Based on the previous literature we discuss the interrelationship between the different variables which are of interest to us and concerns the problem.

• By developing this kind of conceptual framework would help us claim and test certain relationships.

• i.e. From this framework we develop hypothesis statements which are then tested to find out if our theory was valid or not

Sales

Quality

Price

Competition

Types of Variables

• Dependent – (Criterion Variable) – primary interest – Describe or explain the variability or predict it.– We study what variables influence dependent

variable – So by studying these we might able to find a

solution of the problem– E.g. Sales are low , employee loyalty is

dropping

• Independent – (Predictor variable)– Which influences the dependent variable– The influence might be positive or negative– When independent variable is present the

dependent variable is also present.– With each unit of increase in independent

variable there is an increase or decrease in the dependent variable

– E.g. Advertising on sales, recognition on loyalty

Exercise : List the independent variable

• A manager believes that good supervision and training would increase the production level of the workers.

Moderating Variables

• Moderating Variables have strong contingent (conditional) effect on the independent – dependent variables relationship.

• i.e. in the presences of the a third variable the relationship between the independent and dependent is modified

Distinction between Independent and Moderating Variable

• Some times one gets confused as to when a variable is to be treated as independent variable and when it becomes a moderating variable

Situation A

Willingness to learn new ways

Quality of Training Programs

Growth Need of employee

Situation B

Willingness to learn new ways

Quality of Training Prog

Growth Need

High/Low

• Both the scenarios have 3 variables• First scenario training programs and growth needs are independent variables that influence the dependent variable

• Second scenario dependent variable stays the same growth need becomes the moderating variables

• i.e. only those who have high growth need will become more willing to learn new things when quality of the trainings is increased.

• Hence the relationship between dependent and independent variable become contingent (conditional) on the existence of the moderator.

The linear effect of training and growth need on willingness

The effect of training is contingent on high/low growth need (slope/intensity)

Mediating/Intervening

• A variable which surfaces between the time the independent variable operates to influence the dependent variable.

• Temporal /sequential quality• Surfaces as a function of the independent variable

Exam diff Exam Performance

Exam Difficulty

StressExam

Performance

Workforce Diversity

Organization Effectiveness

Integrating Moderating, Mediating Variables

Theoretical Framework

• Is a conceptual model• Foundation of the research• Logically developed, described and elaborated network of association as a result of interviews, observation and literature survey.– So we identify a problem– Identify the important variables from literature etc.– Logically developing network of associations and elaborate – Generate hypotheses and later tested

Components of Theoretical Framework

• Identification of variables ( name and type)• Discussion how and why these variables are related• Direction of the relationship need to be theorized and discussed (positive/negative)

• Discussion on why these relationships exists, support from previous research.

• A schematic diagram

• Note: Must read example on page 93

Recap

• Types of Variables– Independent, Dependent, Moderating, Mediating( Intervening)

• Examples of their relationships with each other

• Developing of Theoretical Framework– Variables, logical Relationships, Directions, Explanations


Lecture No : 5

(Theoretical Framework - Hypothesis Development)

Recap

• Types of Variables– Independent, Dependent, Moderating, Mediating( Intervening)

• Examples of relationships with each other• Developing of Theoretical Framework

– Variables, logical Relationships, Directions, Explanations

• We wanted to break down a problem into easily measurable into testable cases.

Exercise

• A production manager is concerned about the low output levels of his employee. The articles that he reads on job performance frequently mentioned three variables as important to job performance: skill required by job, rewards and satisfaction. In several of the articles it was also indicated that only if the rewards were attractive to the recipients, did satisfaction, and job performance increase not otherwise.

Theoretical Framework( Description and Discussion of the Variables)

• In this section of theoretical framework we need to provide the description of the variables and their relationships with different variables. For example..

• Rewards are two types, intrinsic and extrinsic ….., where as job enrichment is making the job more challenging and utilizes all the skills of the employee…when the.. . …Rewards are known to enhance the satisfaction of employees which leads to higher organization performance ……… But for some employees the rewards are not attractive hence does not contribute to the satisfaction of employee ….etc

Theoretical Framework(Schematic Diagram)

Job Enrichment

Rewards

Employee Satisfaction

Organization Performance

Attraction for

rewards

Research Questions

• Does job enrichment and rewards influence the performance ?

• Does the satisfaction intervenes the relationship between rewards and performance?

• Does the satisfaction intervenes the relationship between job enrichment and performance?

• Does attractiveness of the rewards moderate the relationship between rewards and satisfaction.

Hypotheses Development

• The research problem could be better solved when we formulate the appropriate research questions.

• The logically placed relationships need to be tested. • So we develop statements which would be easily testable

• Formulating such testable statements is called hypothesis development.

Hypothesis Statements

• A hypothesis can be defined as a logically speculated relationship between two or more variables expressed in the form of a test able statement.

• Different Hypotheses statements can be drawn from the theoretical framework developed earlier.

• E.g.• Ha1: Job Enrichment leads to higher job satisfaction • Ha2: If rewards are offered the job satisfaction level be high• Ha3: Organization performance is effected by job enrichment

through satisfaction

• The logical relationships have been now stated in a testable format.

• We need to statistically examine the relationship between the variables “ Rewards” and “satisfaction” or “Job Enrichment” and “Satisfaction”

• We need to also statistically establish that the satisfaction mediates the relationship between rewards , job enrichment and organization performance

• We need to statistically see if there is positive correlation between these variables is significant (large enough) then we would state that the hypotheses have been substantiated(proved)

• In social sciences we call a relationship statistically significant when we are confident that 95 times out of 100, the observed relationship will hold true.

• It is through data analysis our logical relationships are tested.

• In case our hypothesis are not proved then we would search for possible reasons. May be some other variables which influence the relationship e.g. some moderating variables.

• It is again the literature which can provide us with the directions. Hence a good literature review is important.

Hypotheses Statement Formats

• Hypotheses statements could be to test– Difference between groups– Relationship between variables

• The statements could be in the shape of – Proposition (suggestion)– If‐then Else statement

• Theses statements could be direction or non directional

Examples of different formats of Hypotheses statements

• Difference between groups– There is difference between the motivation level of men and women

• Relationship between variables– There is a relationship between age and job satisfaction

• Proposition style– Employees who are more healthy will take sick leaves less frequently

• If‐then else style– If employees are more healthy, then they will take sick leave less frequently

• Directional– The greater the stress experienced on the job , the lower the job satisfaction of the employees

– The motivation level of women is more then motivation level of men

– The age and job satisfaction are negatively related• Non Directional

– There is a relations between stress and job satisfaction– There is a difference between motivation level of men and women.

• The way the statements are formulated is dependent on the state of the research.

• When little support from the previous research is available then a more guarded approach is used to form the hypothesis statements.

• i.e. the direction of the relationship or the statement on the clear differences are avoided.

• But where ever direction is known from the previous literature it is better to state the directional hypotheses.

Null and Alternative Hypotheses

• Null hypothesis is a proposition that states a definite, exact relationship between two variables. i.e. it states that the population correlation between two variables is equal to zero or some definite number or the difference between the two groups is zero

• The alternative hypothesis is the opposite of the null hypothesis. It is a statement expressing a relationship between two variables or indicating difference between groups.

• Null is stated as no significant relationship between the variables or no significant difference between the groups exists.

• Alternate is stated as there is a significant relationship between variables or significant difference exists between the groups.

• The null hypotheses are formed with the objective of rejection.

• As when we reject the null hypothesis then all other alternate hypotheses can be supported.

• It is the theory which gives us the faith that the alternative hypotheses are true.

• Therefore we need to have strong literature support for developing our theory on which are alternate hypothesis are based

Exercise

• A fourth and fifth hypothesis can be developed that is • HA4: Motivation mediates the relationship between need for

achievement and job involvement • HA5: Motivation mediates the relationship between work

ethic values and job involvement

RECAP

• Keeping in view the literature review we develop research questions to address the research problem.

• In order statistically respond to the research questions we develop the Hypotheses statements.

• These statements are stated in such way that they can be easily testable

• Hypotheses statement are written in directional, non directional formats for testing group differences, relationship between variables.

• We develop null and alternate hypotheses


Lecture No : 6(Hypothesis Development)

Recap

• We learned to develop Hypotheses statements

• Directional ,non Directions• Relationship or Group Difference type• Null and Alternate statements

Statistical Notations

• When testing the group differences we need to• Obtain the Mean of the focus variable by each group.• Example:• Mean Motivation Level of a group is obtained and it is denoted by

μ(Motivation of a group)

• We need to compare the Mean Motivation Level of Men vs Women

μ(Motivation‐Men) Vs μ(Motivation‐Women)

• First we state our Null Hypothesis• i.e. There is no difference between the mean motivational level of men vs the mean motivation of women

• So the for the Null Hypothesis we use the following notations

Ho: μ(Motivation‐Men) ‐ μ(Motivation‐Women)=0

• Based on the prior knowledge/ literature we can develop different types of variables

• So the for the possible Alternative Hypothesis we have one of the following statements and its notations(a) The mean motivational level of men more then mean motivation of women

Ha: μ(Motivation‐Men) > μ(Motivation‐Women)

(b) The mean motivational level of men is less then mean motivation of women

Ha: μ(Motivation‐Men) < μ(Motivation‐Women)

(c) There is no difference between the mean motivational level of men vs the mean motivation of women

Ha: μ(Motivation‐Men) ≠ μ(Motivation‐Women)

• When testing the relationship between two variables• We find the Correlation between the two variables • It is denoted by “ρ”• Either ρ >0 ρ <0 ρ =0• For the Null Hypothesis statement we state that• There is no relationship between stress and satisfaction

• Ho: ρ =0

• Based on the available literature we can have different alternate statements

• The possible Alternative Hypothesis we can have notations

• (a)There is a positive relationship between stress and job satisfaction.

Ha: ρ>0

(b) There is a negative relationship between stress and job satisfaction.

Ha: ρ<0

(c) i.e. The is a relationship between stress and job satisfaction

Ha: ρ≠0

Summarized Table of Statistical Notations for Hypotheses

Relationship Group Difference

Ho: Ha: Ho: Ha:

Directional ρ=0ρ>0ORρ<0

µa=µbµa>µbORµa<µb

Non‐Directional ρ=0 ρ#0 µa=µb µa # µb

• Example 1:

• In this example we identified that workforce diversity is transformed into creative synergy which leads to organizational effectiveness. We also said that the synergy would be possible when the organization have experienced managers to handle diverse workforce.

• Based on this information we just develop the hypothesis statements

• Ha1: The workforce diversity is related to creative synergy.

• Ha2: The higher the creative synergy the more the organization effectiveness

• Ha3: The creative synergy mediates the relationship between workforce diversity and organization effectiveness.

• Ha4: The relationship between workforce diversity and creative synergy is moderated by managerial expertise.

Example:2

• Different Hypotheses statements could be generated• Ha1:The more the loyalty the higher the organization commitment

• Ha2:Loyality acts as an intervening variable between job level, age, length of service, pride of working for the organization.– Ha2.1: Loyalty mediates the relationship between age and organization commitment

– Ha2.2: Loyalty mediates the relationship between length of service and organization commitment

• Ha2.3: Loyalty mediates the relationship between job level and organization commitment.

• Ha2.4: Loyalty mediates the relationship between pride working for organization and organization commitment.

• Ha3.: Only employees who do not have lust for job hopping, would job level, age, length of service, pride working for organization be related to Loyalty for the organization .

• Ha3.1: Lust for job hopping would moderate the relationship between job level and Loyalty.

• Ha3.2: Lust for job hopping would moderate the relationship between age and Loyalty.

• Ha3.2: Lust for job hopping would moderate the relationship between length of service and Loyalty.

• Etc…

• An other research question might be poised • Does the blue collar worker are more loyal or white collar ?

• To find the answer to this question a hypothesis statement could be generated as follows

• Ha4: There is difference between the loyalty level between the blue collar workers(labor) and white collar workers(officers)

Steps Following the Hypothesis testing

• State the null and the Alternate hypotheses• Choose appropriate test based on the data collected (parametric like Pearson correlation, t test, ANOVA)

• non parametric like spearman ‘s rank correlation, Kendall’s X2)

• Determine the level of significance desired – Usually set to 0.05 can be more or less

• See the output results of generated from the software. See if the differences are significant or the relationship significant.

• If the differences/relationship are not significant then we accept the null hypotheses other wise accept the alternate

• In case the you are using tables check if the calculated values larger than the critical value, the null hypotheses is rejected and alternate accepted

• ( More practice would be covered in later sections of the course)

Deductive and Inductive Hypothesis

• The hypothesis generating and testing can be done both through Deduction and Induction.

• In deduction we first develop the theoretical model, then generate hypothesis statements, data is collected and then hypothesis are tested.

• In induction new hypothesis are generated based on the data already collected, which then is tested

• In the initial session we discussed the case of the Hawthorne experiments, where new hypothesis were developed after the data already collected did not substantiated any of the original hypotheses.

• New Hypotheses might be developed after the data is collected.

• Creative insights might compel researchers to test a new hypothesis from exiting data which when substantiated would add to new knowledge and help build theory.

Hypothesis testing with Qualitative Research: negative case analysis

• Hypothesis testing can also be tested with qualitative data.

• Example:• After interview we develop the theoretical framework that unethical practices by employees are a function of their ability to discriminate between right and wrong, or due to need for money, or the organization indifference to such practices.

• Search for data prove the hypothesis to be false

• When no support is found an there is this case where an individual is deliberately engage in the unethical practices even though he is able to discriminate from right from wrong, and is not in need for money, and the organization would not be indifferent to his behavior.

• He simply wants to get back to the systems because the system would not listen to his advice.

• This new discovery is different from the previous hypothesis is know as negative case method and enables to revise their theory.

RECAP

• Hypothesis notations• Examples on how to develop hypothesis statements• Steps to test the hypothesis statements• Hypothesis testing through inductive method• Hypothesis testing with qualitative research


Lecture No : 7(Research Design)

1

RECAP

• Hypotheses statements are stated in such way that they can be easily testable


• We develop null and alternate hypotheses• We now want to design the research in such a way that the data can obtained and analyzed in away that we arrive at a solution

2

Elements of Research Design

• Refers to the outline, plan, or strategy specifying the procedure to be used in answering research questions

• It encompasses many issues.

• We need to decide on the different choices.

3

• To decide for any given situation – the type of investigation needed, – the study setting, – the extent of researcher interference, – the unit of analysis, – the time horizon of the study– To identify whether a casual or a correlation study would be more appropriate in a given situation

4

The Research DesignTypes of Investigation

Establishing:-Casual relationship- Correlation's- Groupdifferenceranks, etc.

Purpose of the study

ExploratoryDescriptionHypotheses

Testing

Extent of Researcherinterference

Minimal: studyingevents as theynormally occurManipulation

Study setting

contrived

non-contrived

1. Feel fordata

2.Goofiness of data

3. HypothesisTesting

Units of analysis(population to be

studied)

individualsdyadsgroups

organizations\machines

etc

Samplingdesign

Probability/Non-probabilitySample size (n)

Time horizon

one-shot(cross-sectional)

Longitudinal

Data collectionmethod

ObservationInterview

QuestionnairePhysical

measurementUn-obstructive

Measurement& Measures

Operational Definitionscaling categorizingcoding

5

THE PURPOSE OF THE STUDY

• Studies can be either exploratory in nature, or descriptive, or they can be conducted to test hypotheses.

• The nature of the study ‐ whether it is exploratory, descriptive or hypothesis testing ‐ depends on the stage to which knowledge about the research topic has advanced.

6

• The Case Studies, which is an examination of studies done in similar organizational situations, is also a method of solving problems, or for understanding phenomena of interest and generating additional knowledge in that area.

7

• Exploratory StudyExploratory studies are undertaken to better comprehend the nature of the problem, since very few studies might have been conducted in that area.

• Extensive interviews with many people might have to be undertaken to get handle on the situation and to understand the phenomena.

• After obtaining a better understanding, more rigorous research proceed. 8

• Some qualitative studies (as opposed to quantitative data gathered through questionnaire, etc.) where data are collected through observation or interviews, are exploratory studies in nature.

• When the data reveals some pattern regarding the phenomena of interest, theories are developed and hypotheses formulated for subsequent testing.

9

Example: Managers of firm wants to explore the nature of managerial work (Mitnizberg in 1970)

Based on the analysis of his interview data, he formulated theories of managerial roles, the nature and types of managerial activities, and so on.

10

Example : What is the role of virtual markets for e ‐commerce ? (in 2005)

The recent development of the internet and the busy life style of the people in the west, lots of the individuals are showing interests in accessing internet .

11

• Descriptive Study:A descriptive study is under taken in order to ascertain and be able to describe the characteristics of the variables of interest in a situation.

• For instance a study of class in terms of the percentage of members who are in their senior and junior years, gender composition, age groupings, number of semesters until graduation, and number of business courses taken, can only be considered as descriptive in nature

12

• Descriptive studies that present data in a meaningful form help to:

• 1. Understand the characteristics of a group in a given situation.

• 2. Think systematically about aspects in a given situation.

• 3. Offer ideas for further probe and research• 4. Help make certain simple decisions (such as how many and what type of individuals should be transferred from one department to another

13

• Example:• A bank manager wants to have a profile of the individuals who have loan payments outstanding for six months and more. It would include details of their average age, earnings, type of occupation they are in, full time/part time employment status, and the like.

• This information might help to ask for further information or make an immediate decision on the types of individuals to whom he would not extend loans in future.

14

• Example:• The ministry of science and technology wants to know how many projects have failed, what were the reasons. Out of the triple constraints (cost, time, scope) how many failed due to scope constraint.

• The information received can help tighten the scope definition process at the MOST technology projects.

15

• Hypotheses Testing:Hypothesis testing is undertaken to explain the variance in the dependent variable or to predict organizational outcomes.

• Similar to the kind of examples we had discussed in the theoretical framework chapter

16

• Example: • A Marketing manager would like to know the sales of the company will increase if he doubles the advertising dollars.

• Here, the manager wants to know the nature of the relationship between advertising and sales that can be established by testing the hypothesis:

17

• H0: There is no relationship between sales and advertisement

• Ha: If advertising is increased, then sales will also increase

• Ho:ρ =0• Ha: ρ >0

18

• Example: The manager of a manufacturing firm believes that the voluntary turn over is more of with it’s female employees. The manager would like to test the difference between the turnover rates of male and female.

19

• Ho: There is no difference between the turn over rate of men and women

• Ha: There is a difference between the turn over rate of men and women

• Ho:μturn‐over‐men = μturn‐over‐men

• Ha:μturn‐over‐men ≠ μturn‐over‐men

20

• So exploratory studies are focused on understanding the characteristics of a phenomenon of interest.

• A pilot study on small scale interviewing individuals is done. ( What is an internet club)

• A Descriptive study is when characteristics of the phenomenon are known and we want to describe it better ( How many internet clubs are in the city, how many are open for 24 hrs etc)

• A hypothesis testing is when we try test certain theories. (Internet clubs have a cased a decline in the social values ) 21

Types of Investigation: Causal versus Correlation

• When the researcher wants to define the cause of one or more problems, then the study is called a Causal Study.

• When the researcher is interested in outline the important variables that are associated with the problem, it is called a Correlational Study.

22

• Example:• A causal study question:

– Does smoking cause cancer?• A correlational question:

– Are smoking, chewing tobacco related to cancer ?• A causal study hypothesis:

– Smoking causes cancer.• A correlational hypothesis:

– Smoking and cancer are related– Chewing and cancer are related 23

Extent of Researcher Interference with the Study

• The extent to which the researcher interferes with the normal flow of work at the workplace has direct bearing on whether the study undertaken is casual or correlational.

• A correlational study is conducted in the natural environment of the organization, with the researcher interfering minimally with the normal flow of work.

24

• For example, • if a researcher wants to study the factors influencing training effectiveness

• (a correlational study), • the individual simply has to develop a theoretical framework, collect the relevant data, and analyze them to come up with the findings.

25

• Although there is some disruption to the normal flow of work in the system as the researcher interviews employees and administers questionnaire at the workplace, the researcher’s interference in the system is minimal compared with that in causal studies.

26

• In case of causal study the researcher would try to manipulate certain variables so as to study the effect on the dependent variable

• Example. • Effect of lighting on employee performance• The researcher's interfere is high

27

Recap

• We covered some of the research design elements• We talked about the research purpose

– (exploratory, descriptive, hypothesis testing)

• Type of investigation– (causal, correlations)

• Extent of researcher's interference– (High,moderate,low)

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Lecture No : 8(Research Design-continue)

Recap


– (exploratory, descriptive, hypothesis testing)

• Type of investigation– (causal, correlations)

• Extent of researcher's interference– (High,moderate,low)

Study Setting: Contrived and Non‐contrived

• Organizational research can be done in the natural environment where work proceeds normally (i.e., in non‐contrived setting) or in artificial, contrived settings.

• Correlation studies are invariably conducted in non‐contrived settings, whereas rigorous causal studies are done in contrived lab setting

• Correlation studies done in organizations are called field studies – ( factors influencing in a call center it’s employees turn over ).

• Studies to establish cause and effect relationships using the same natural environment in which employees normally function are called field experiments

• Example:• employees who have been given recognition and employee who have not been given recognition.

• Cause effect studies in contrived environment in which The environment extraneous factors are controlled are termed as lab experiments.

• Example:• Select all new employees with the same scores in the entry test and provide one group training and the other no training and controlling that they are not exposed to any senior employee who could guide them.)

Unit of Analysis: Individuals, Dyads, Groups, Organizations, Cultures

• The unit of analysis refers to the level of aggregation of the data collected during the subsequent data analysis stages.

• Individuals: If the problem statement focuses on how to raise the motivational levels of employees in general, then we are interested in individual employees in the organization and would like to find out what we can do to raise their motivation.

• Here the unit of analysis is the individual.(managers’ perception on the factors which influence the success of the project)

• Dyads: If the researcher is interested in studying two‐person interactions, then several two‐person groups, is known as dyads and will become unit of analysis.

• For example, analysis of husband‐wife(are they satisfied with the education provided by the school) in families and mentor‐mentee (perception on the benefit of mentoring).

• Groups: If the problem statement is related to group effectiveness, however, then obviously the unit of analysis would be at group level.

• For example, if we wish to study group decision‐making patterns, we would probably examining such aspects as group size, group structure, cohesiveness, and the like, in trying to explain the variance in group decision making.

• In such cases the unit of analysis will be groups.(use of I.T by the different department)

• Organizations: If we compare different departments in the organization, then the data analysis will be done at the departmental level ‐ that is, the individuals in the department will be treated as one unit and comparison made treating the department as the unit of analysis.

• (Conservation of energy initiatives by public and private organization)

• Cultures: If we want to study cultural differences among nations, we will have to collect data from different countries and study the underlying patterns of culture in each country, here the unit of analysis used will be cultures.

• (Moral values of Eastern vs Western cultures)

Time Horizon: Cross‐sectional versus Longitudinal

• Cross‐Sectional Studies• A study can be done in which data are gathered just once, perhaps over a period of days or weeks or months, in order to answer a research question. Such studies are called one‐shot or cross‐sectional studies.

• (data collected from project managers and their psychological well being between October till December)

• Longitudinal Studies• In some cases, the researcher might want to study people or phenomena at more than one point in time in order to answer the research question. For example, the researcher might want to study employees behavior before and after a change in the top management, to learn the effects of change.

• Or when data on the dependent variable are gathered at two or more points in time to answer the research question, are called longitudinal studies. (use of electricity by a city in summers and then in winters)

Scenarios

• Following are some scenarios , for each indicate how researcher should proceed, giving reasons:

1. Purpose of the study2. Type of investigation3. Researcher Interference4. Study setting5. Time Horizon6. Unit of analysis

Recap

• Research Design elements• Study setting• Time Horizon• Unit of analysis• Secnarios


Lecture No : 9(Measurement of Variables/Operational Definition)

1

Recap

• Research Design elements• Study setting• Time Horizon• Unit of analysis

2

Measurement of Variables

• In order to find answers to our question and in order to test our hypothesis we need measure our variables of concern.

3

Why the need for measuring

• To test the hypothesis the variables need to measured.

• Finding the answers to our questions is possible when we have some statistics/ numbers .

• Some variables are easily measurable e.g. Height, salary, hours worked.

• Some are not so easily measured motivation level, success level of projects, satisfaction, loyalty etc.

4

• Questions like 1. How long have you been working in this

organization?2. What is your marital status ?3. How much is your salary ?4. What was the cost of last project ?• But some variables are abstract and subjective e.g. satisfaction, happiness, achievement motivation, effectiveness of the organization.

5

• One cannot simply ask what is the achievement motivation level of your employees.

• But before we start measuring the variables it’s abstractness needs to be addressed.

• There are ways to in which the abstractness of the notion could be simplified into observable characteristics.

6

• For instance “Thirst” cannot be seen but we expect that a thirsty person would consume lots of liquid.

• Hence the behavior of the thirsty person is that he would drink fluids.

• If several individuals say they are thirsty we can measure thirst by measuring their consumption of liquid, although the concept itself is abstract.

7

• Reducing abstract concepts so they are measurable is called operationalizing.

• Operationally defining a concept so that it becomes measurable is achieved by looking at the behavioral dimensions, facets , or properties represent by the concept.

8

Steps to Operationalization

• one needs the define component of the concept.

• Under each concept possible quantitative measurable elements need to identified.

• Against each developed concepts specific questions could be formulated. The questions could be supported by secondary data, observation or self report

9

Operational Definition

10

• The operational definition of Learning could be stated as “The ability to recall the lesson, it is also the ability apply the lesson learned to practical situation and finally it is the understanding of a lesson”.

• Even though these dimension have to an extent reduced some of the ambiguity but we still need to further classify what is meant by understanding, application so that we can measure learning as a whole.

11

• With some effort we can define what is meant by understanding , i.e. the ability to answer questions correctly and give appropriate answers. We also define what is application, which is the ability to solve problem by applying the lesson learned and integrate it with other relevant material.

• Now we are in better position to measure the concept learning.

• At this stage we can develop questions which address the synthesized concepts and obtain data on them.

12

What is not operational definition

• It does not define the correlates of a concept • i.e. motivation and performance are two separate concepts and they might be correlated we cannot substitute one with the other

• Motivation can lead to performance but we do not measure performance by motivation.

• We need to differentiate between the reasons (factors/antecedents) with dimensions.

• Dimensions are the sub components of a concept and factors/ antecedents the causes of the concept

18

• We operationally define concepts and ask questions that are likely to measure the concept.

• So for abstract concepts we need to define the subjective feelings and attitudes.

• For straight forward variables , objective data is used such as salary, number of tee shirts.

• A number of subjective concepts have been opertionalized by the subject experts and we can use them for research.

19

Recap

• Measurement is necessary to give answers or to the research question , or to test our hypotheses.

• The opeationalizing of certain subjective variables are necessary for measurement.

• The abstract concepts are broken down to dimensions and its elements.

• Questions are formulated on them• Not to confuse dimensions with antecedents

20


Lecture No : 10(Measurement of Variables/Scales)

1

Recap





2

Scales and Measurement

• We have operationalized the concepts and converted them into dimensions and elements

• We also have attached questions with these elements against which we would collect some data.

• Each question needs to measured

3

• Measurement is the process of assigning numbers or labels to objects, persons, states of nature, or events.

• Done according to set of rules that reflect qualities or quantities of what is being measured.

4

• Measurement means that scales are used.

• Scales are a set of symbols or numbers, assigned by rule to individuals, their behaviors, or attributes associated with them

5

Types of Scales

• Four types of scales are used in research, each with specific applications and properties. The scales are

• Nominal• Ordinal• Interval• Ratio

6

• Nominal Scale:• Simply the Nominal scale is count of the objects belonging to different categories.

• Ordinal Scale:• The ordinal scale positions objects in some order

• ( such as it indicates that pineapples are juicer then apples and oranges are even more juicer than pineapples)

7

• Interval Scale:• It can gives us information as to what extent(level) one is juicer than the other.

• How much better is the pineapple than the apple and orange is better than the pine apple.

• Is pine apple only marginally better than the apple .

• Ratio Scale:• It is most comprehensive scale, has all characteristics of other scales.

8

Nominal Scales

• Nominal scales are used to classify objects, individuals, groups, or even phenomena.

• Examples of nominal variables:– Gender– State of residence– Country – Ethnicity

9

• Nominal scales are mutually exclusive• (meaning that those items being classified will fit into one classification).

• These scales are also collectively exhaustive, (meaning that every element being classified can fit into the scale).

10

• As it might appear on a questionnaire, examples of nominally scaled questions included:

– What is your class rank at CIIT? 1.Freshman 3. Junior2.Sophomore 4. Senior

11

– The numbers themselves do not have meaning (we could have used letters, too),

– They are used just to identify the possible responses to the question.

– Thus, in evaluating responses to this you cannot use the mean.

– Permitted statistics; frequencies (% and counts, modes )

12

Nominal scale is always used for obtaining personal data such as gender or department in which one works, where grouping of individuals or objects is useful, as shown below.

1. Your gender 2. Your department___Male ___Production

___Female ___Sales___Accounting___Finance___Personnel___R & D___Other (specify)

13

Ordinal Scales

• These scales allow for labeling (or categorization) as in nominal scales, but they also allow for ranking.

• Example: Rate these vacation destinations in terms of how much you would like to visit from one to five with one your most preferred and five your least preferred.1. Bermuda2. Florida3. Hawaii4. Aspen5. London

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• This type of scale can provide information about some item having more or less of an attribute than others, but no information on the degree of this.

• Permitted statistics: Frequencies, median, mode

15

Ordinal scale is used to rank the preferences or usage of various brands of a product by the individuals and to ranks order individuals, objects, or events as per the examples below.

16

• Rank the following personnel computers with respect their usage in your office, assigning the number 1 to the most used system, 2 to the next most used, and so on. If a particular system is not used at all, in your office, put a 0 next to it.

____Apple____Hewlett Packard

____Compaq ____IBM____Comp USA ____Packard Bell____Dell Computer

____Sony____Gateway ____Toshiba

17

Interval Scales

• Contains the information available in ordinal scales (ranking) but with the added benefit of magnitude of ranking.

• Interval scales have equal distances between the points of a scale.

• These scales can contain a zero point, but they are subjective and are not meaningful (0° C = 32° F). Temperature is an example of a interval scale

• Permitted statistics; mean, median, mode, as well as more advanced tests.

18

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On a scale of one to five, with five meaning you strongly agree, and one meaning you strongly disagree consider this statement ‘I believe my college education has prepared me well to begin my career’.

1 2 3 4 5

Strongly disagree

Somewhat

disagreeNeither

Somewhat agree

Strongly agree

Ratio Scale

• The most comprehensive scale• Has all of the characteristics of the other three with the additional benefit of an absolute, meaningful zero point.

• Examples include:– Weight– Sales volume– Income– Age

• Permitted statistics same as with interval data.20

• A ratio variable, has all the properties of an interval variable, and also has a clear definition of 0.0. When the variable equals 0.0, there is none of that variable. Variables like height, weight, enzyme activity are ratio variables.

21

• Temperature, expressed in F or C, is not a ratio variable. A temperature of 0.0 on either of those scales does not mean 'no temperature'.

• However, temperature in Kelvin is a ratio variable, as 0.0 Kelvin really does mean 'no temperature'.

22

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• Ratio scales are usually used in organization research when exact numbers on objective as opposed to subjective factors are called for, as in the following question:

• How many other organizations did you work for before Date joining this system?

• Please indicate the number of children you have in each of the following categories?

‐‐‐‐ below 3 years‐‐‐‐ between 3 and 6‐‐‐‐ over 6 years but under 12‐‐‐‐ 12 years and over

• How many retail outlets do you operate?

24

Comparison between scales

• The researcher would like to know what is the percentage of people who like Pepsi, 7up, Coke, Miranda?

• Choose the soft Drink you want to order.Pepsi7UpCokeMarinda

25

• The researcher would like to know among the 4 soft dinks which they prefer the most ,assigning 1 to most and 4 to the least

Pepsi7UpCokeMarinda

26

• The researcher would like to know what extent the 4 drinks are liked

• On a scale of one to five, with five meaning you strongly like, and one meaning you strongly dislike consider this statement ‘I like/dislike this soft drink ’.

• Pepsi 1 2 3 4 5• Coke 1 2 3 4 5• 7up 1 2 3 4 5• Marinda 1 2 3 4 5

27

• The researcher would like to know how many Pepsi , Mrindia , etc you consume in a monthPepsi: _____7Up: _____Coke: _____Marinda:_____

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30

Very badBadNeither good nor badGoodVery good

PoorFairGoodVery goodExcellent

How good a car is Honda?

Balanced or Unbalanced

31

Very badBadNeither good nor badGoodVery good

Very badBadNeither good nor badGoodVery goodNo opinionDon’t know

Forced or Unforced Choices

How good a car is Honda?

Rating Scales

32

33

I plan to purchase a laptop in the 12 months.

YesNo

Simple Category (Dichotomous) Scale

34

What newspaper do you read most often for financial news?

East City GazetteWest City TribuneRegional newspaperNational newspaperOther (specify:_____________)

Multiple‐Choice, Single Response Scale

35

What sources did you use when designing your new home? Please check all that apply.

Online planning servicesMagazinesIndependent contractor/builderDesignerArchitectOther (specify:_____________)

Multiple‐Choice, Multiple Response Scale

36

The Internet is superior to traditional libraries for comprehensive searches.

Strongly disagreeDisagreeNeither agree nor disagreeAgreeStrongly agree

Likert Scale

37

Semantic Differential

A measure of attitudes that consists of a series of seven-point rating scales that use bipolar adjectives to anchor the beginning and end of

each scale.

38

Numerical Scale

An attitude rating scale similar to a semantic differential except that it uses numbers, instead of verbal descriptions, as response options to identify

response positions.

39

Stapel Scales

A measure of attitudes that consists of a single adjective in the center of an even number of numerical values.

40

Constant‐Sum Scales

A measure of attitudes in which respondents are asked to divide a constant sum to indicate the relative importance of attributes; respondents often sort

cards, but the task may also be a rating task.

41

Graphic Rating Scales

A measure of attitude that allows respondents to rate an object by choosing any point along a graphic continuum.


Lecture No : 11(Goodness Of Measures)

1

Recap

• Measurement is the process of assigning numbers or labels to objects, persons, states of nature, or events.


2

• Using these scales we complete the development of our instrument.

• It is to bee seen if these instruments accurately and measure the concept.

4

Sources of Measurement Differences

Why do ‘scores’ vary? Among the reasons legitimate differences, are differences due to error (systematic or random)

1. That there is a true difference in what is being measured.

2. That there are differences in stable characteristics of individual respondents On satisfaction measures, there are systematic

differences in response based on the age of the respondent.

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3.Differences due to short term personal factors – mood swings, fatigue, time constraints, or other transistoryfactors. Example – telephone survey of same person, difference may be due to these factors (tired versus refreshed) may cause differences in measurement.

4.Differences due to situational factors – calling when someone may be distracted by something versus full attention.

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• 5.Differences resulting from variations in administering the survey – voice inflection, non verbal communication, etc.

• Differences due to the sampling of items included in the questionnaire.

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7. Differences due to a lack of clarity in measurement instrument (measurement instrument error). Example; unclear or ambiguous questions.

8. Differences due to mechanical or instrument factors – blurred questionnaires, bad phone connections.

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Goodness of Measure

• Once we have operationalized, and assigned scales we want to make sure that these instruments developed measure the concept accurately and appropriately.

• Measure what is suppose to be measured• Measure as well as possible

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• Validity : checks as to how well an instrument that is developed measured the concept

• Reliability: checks how consistently an instrument measures

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Ways to Check for Reliability

How to check for reliability of measurement instruments or the stability of measures and internal consistency of measures?

Two methods are discussed to check the stability .1. Stability

(a) Test – Retest Use the same instrument, administer the test

shortly after the first time, taking measurement in as close to the original conditions as possible, to the same participants.

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If there are few differences in scores between the two tests, then the instrument is stable. The instrument has shown test‐retest reliability.

Problems with this approach. Difficult to get cooperation a second time Respondents may have learned from the first test, and thus responses are altered

Other factors may be present to alter results (environment, etc.)

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(b)Equivalent Form Reliability This approach attempts to overcome some of the

problems associated with the test‐retest measurement of reliability.

Two questionnaires, designed to measure the same thing, are administered to the same group on two separate occasions (recommended interval is two weeks).

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If the scores obtained from these tests are correlated, then the instruments have equivalent form reliability.

Tough to create two distinct forms that are equivalent.

An impractical method (as with test‐retest) and not used often in applied research.

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(2)Internal Consistency Reliability

This is a test of the consistency of respondents answer to all the items in a measure . The items should ‘hang together as a set.

i.e. the items are independent measures of the same concept, they will correlated with one another

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Developing questions on the Concept Enriched Job

Validity

• Definition: Whether what was intended to be measured was actually measured?

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Face Validity• The weakest form of validity• Researcher simply looks at the measurement instrument and concludes that it will measure what is intended.

• Thus it is by definition subjective.

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Content Validity

The degree to which the instrument items represent the universe of the concepts under study.

In English: did the measurement instrument cover all aspects of the topic at hand?

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Criterion Related Validity• The degree to which the measurement instrument can predict a variable known as the criterion variable.

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• Two subcategories of criterion related validity• Predictive Validity

– Is the ability of the test or measure to differentiate among individuals with reference to a future criterion.

– E.g. an instrument which is suppose to measure the aptitude of an individual, when used can be compared with the future job performance of a different individual. Good performance (Actual) should also have scored high in the aptitude test and vise versa 22

• Concurrent Validity– Is established when the scale discriminates individuals who are known to be different that is they should score differently on the test.

– E.g. individuals who are happy at availing welfare and individuals who prefer to do job must score differently on a scale/ instrument which measures work ethics.

Construct Validity• Does the measurement conform to some underlying theoretical expectations. If so then the measure has construct validity.

• i.e. If we are measuring consumer attitudes about product purchases then do the measure adhere to the constructs of consumer behavior theory.

• This is the territory of academic researchers

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• Two approaches are used to measure construct validity

• Convergent Validity– A high degree of correlation among 2 different measures intended to measure same construct

• Discriminant Validity– The degree of low correlation among varaibles that are assumed to be different.

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• To check validity through Correlation analysis, Factor Analysis, Multi trait , Multi matrix correlation etc

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• Reflective vs Formative measure scales:• In some multi item measure where it is measuring different dimensions of a concept do not hang together

• Such is the case of Job Description Index measure which measures job satisfaction from 5 different dimension i.e Regular Promotions, Fairly good chance for promotion, Income adequate, Highly Paid, good opportunity for accomplishment.

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• In this case some items of dimensions Incomeadequate and Highly paid to be correlated but dimension items of Opportunity for Advancement and Highly Paid might not correlated.

• In this measure not all the items would related to each other as it’s dimensions address different aspect of job satisfaction.

• This measure /scale is termed as Formative scale

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• In some cases the measure dimensions and items correlate.

• In this kind of measure/scale the different dimensions share a common basis ( common interest)

• An example is of a scale on Attitude towards the Offer scale.

• Since the items are all focused on the price of an item, all the items are related hence this scale is termed as Reflective Scale.

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Recap

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Lecture No : 12(Data Collection-Interview)

1

Recap

2

Primary Data

• Primary Data = information obtained exclusively for current research

• Personal Interview• Focus Groups• Panels• Delphi Technique• Telephone Interview – Computer assisted telephone interviewing and Computer administered telephone survey

• Self‐Administered Surveys

Secondary Data

• Company Archives• Gov Publications• Industry Analysis

Primary Data Collection Methods

• Focus Group• Panels• Interviews (face to face, telephone, electronic media)• Questionnaires (personally, mail, electronic) • Observation • Other (projective tests)

• Focus Group:• Usually consist of 8 to 10 members , with a moderator leading the discussion for 2 hours on a particular topic, concept or product.

• Member are chosen on the bases of their expertise on the topic.

• E.g Discussion on computers and computing , or women mothers , social networking etc

• Less expensive and usually done for exploratory information. Cannot be generalized

6

• Panels:• Similar to focus group but meets more than once in order to study the change or interventions need to be studies over a period of time.

• Members are randomly chosen• E.g effect of advertisement of a certain brand need to be assessed quickly, panel members could be exposed to the advertisement and intention of purchase could be assessed.

• When the product is modified then the response of the panel can be observed 7

• Observation measures:• Methods through which primary data is collected without the involving people.

• E.g: Wear and tear of books , section of an office, seating area of railway station which indicate the popularity, frequency of use etc.

• E.g: The number of cans in the dust bin and their brands, the number of motor cycles vs cars parked in the university parking lot

8

• Interviewing: • Collect data from the respondent on an issue of interest.

• Usually administered at the exploratory stage of the research.

• In case large set of respondents are needed then more than one interviewer are used , hence they need to be trained so that biases , voice inflections, difference in wording are avoided

• Structured and Unstructured

• Un Structured: • No planned sequence of questions, help in exploring preliminary issues.

e.g. Tell me something about your unit and department , and perhaps even the organization as a whole in terms of work, employee and whatever else you think is important”“Compared to other departments, what are the strengths and weakness of your department”

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• In case they identify a difference you can ask • “How can you improve the situation ?”

• Encouraging the respondent to reflect on the positive and negative aspects of it.

• Try to pleasant and see if the respondent is not comfortable.

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• Through unstructured the different major areas might be exposed. It from these the researcher can pick some areas as focus variables which need further probing.

• Now the researcher can device a more focused approach and develop a more structured interview emphasizing on some particular issues.

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Structured:• Know at the outset what information is needed. Focusing on factors relevant to the problem.

• The focus is on the factors which have surfaced during the un structured interview.

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• E.g: During the previous unstructured interview it was identified that the department needs improvement.

• Now you can focus on questions which addresses how to improve the department, i.e. the factors which can improve the department

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• This can be done through face to face, over the telephone or through the computers via internet.

• Specific same questions are asked from different respondents.

• The information collected is tabulated and then the data is analyzed.

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• The result could highlight the important factors influencing the issues.

• This information is of qualitative in nature which could be then empirically tested and verified using other methods like questionnaires.

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Guideline for Interviews

• Listen carefully• Motivate the respondents• How to take notes• Built proper trust and rapport with interviewee• Clarification of complex issues• Physical setting• Explaining the reasons for research and criteria of selection

• Face to Face

• Adv :Clarify doubts, repeating, rephrasing, getting non verbal cues

• Dis : vast resources required, cost, anonymity

Telephone:

• Adv : Wider reach in short time, some time easy to discuss personal information over the phone

• Dis: Can be terminated without warning, cannot have a prolonged interview, non verbal cue.

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Closed vs. Open Questions• Easy.• Cost of coding is reduced.• Quicker, standardized interviews.• Can be answered without thinking.• Pre‐testing is a must.• Limit the richness of data.

Recap

• The Data is collected from primary and secondary sources

• The primary data collect via• Observation, panels, interviews, questionnaires etc• Interview are structure and unstructured• While interviewing there are certain guidelines• There are structured and unstructed interviews• There are some advantages / disadvantages of face to face vs telephone interviews .

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Lecture No : 13(Data Collection-Questionnaire)

1

Recap



2

Questionnaires

• Data Collection is mechanism when the researcher knows exactly what is required and how to measure the variables of interest.

• Types of Questionnaire:– Personally administered questionnaire– Mail Questionnaire

Personally Administered Questionnaires

• Mostly local area based, org is willing to have a group of employee respond to it.

• It is Cheaper then interviews, helps remove doubts, motivating respondents

Mail Questionnaires:

• Wide geographical area can be reached, respondents have flexibility of time , It is more cost effective but the response rate is low,

• Can improve by giving some incentives and doubts cannot be clarified.

Guidelines for Principles

• Content and purpose of question (Subjective/Objective)• Language and wording( Jargon/ Technical)• Type and Form (open ended, closed ended)• Positively and Negatively Worded• Biases ( loaded, leading, social desirable, double barreled)• Sequencing of Questions

Content and purpose of Question:

• If the variables tapped are subjective feeling we need to measure the dimension and elements .. Use interval scales

• If the variables are objectives/ facts a single direct question may be asked.

Language and Wording of question:

• The level of respondents have to be considered. Slang and Technical jargon has to be avoided

• e.g. Work is a drag, she is a compulsive worker. Tech Jargon like organizational structure , 360 degree appraisal

Type and Form of Questions:

– Open ended vs Closed Ended

– Positively vs Negatively Worded

Open ended vs Closed Ended

• In open ended the respondent chooses any way they like. E.g. any five things which interest him at his job.

• In close ended the respondent have to make a choice among the given alternatives e.g. out of the list of 10 job characteristics rank any 5

Positively vs Negatively Worded :

• Have some positive and some negative worded questions to break the monotony.

• E.g. Coming to work is great fun or coming to work is no great fun

Biases in Questions:

Double Barreled: Questions has more than one question within it.

• E.g. Do you think that the course content is adequate and it applicable at your work?

Ambiguous Question:

Respondent does not know what it means. E.g. To what extent would you say you are happy?

Do you discuss you work with your boss regularly? Do you go to movies frequently?

Frequently may mean once in a week, or once in a month. Regularly may mean every day, or every week , or every month.

Recall Dependent:

• Questions based on past experiences and rely on memory.

• E.g. After 30 years of work one would not remember the first job details such as name of the boss/ years worked in a department

Leading Questions:• Are worded in such a way that it would lead the respondent to answer in a way that the researcher would like to or want to give.

• E.g. Don’t you think that in these days of escalating costs of living employee should be given good pay raise?

• Better.. To what extent do you agree that employee should be given higher pay raise.

• Example:• Don’t you think that more women should be promoted to decision making line positions in organization

Loaded Questions:• Are when they are phrased in an emotionallycharged manner.

• E.g. To what extent do you think management is likely to be vindictive/(cruel) if the union decides to go on strike.

• Better…. To what extent you favor strike … To what extent you fear that there would be a adversereaction from the management.

• Did P.T.I Lose the elections in Punjab

• Better P.T.I was not chosen in Punjab

Social Desirability:

Is when questions are worded such that they elicit(draw out) socially desirable response

e.g. Do you think that older people should be laid off?

..better …There are advantages and disadvantages to retaining senior citizens in the workforce. To what extent do you think companies should continue to keep the elderlyon their payroll.

Exercise

• If you have been in the company for fifteen years please indicate the year of joining or the name of you colleague.

• Bad question as it is recall dependent

• My colleague is good and efficient .

• Bad Question: Double Barreled

• Working Women should not have children.

• Bad: Loaded question an emotional issue for women

• Investment in children's future should be an important goal of the administration.

• Bad Question: Socially desirability

• This job uses a lot of skills I have.

• Okay no problem with the wordings

• For this country to keep on remaining competitive should we not spend more on research.

• Bad: Leading question

Other Guide lines

• Length (20 words)• Sequencing (funneling , same positive and negative question)

• Classification Data or Personal Data

Recap

• Questionnaires• Personally Administered Questionnaires• Mail questionnaires• Guide line for wordings

– Content and purpose (Subjective vs Objective)– Language and wording ( slang/technical)– Types of formats (open / closed ended)– Positively worded and Negatively worded– Bias/ Favoritism(Leading, loaded, ambiguous, double barrel, socially desirable)

• Length of the question• Funneling


Lecture No :14(Sampling Design)

Recap

• Data collection Interviews and Questionnaires• Personally Administered Questionnaires• Mail questionnaires• Guide line for wordings



Lecture Objectives

• Define sampling, reasons for sampling, sample,population, element, sampling unit and subject

• Sampling process• Different sampling design

Sampling

The process of selecting the right individuals,objects, or events as representative of entirepopulation is known as sampling.

Population

Sample

Relationship between sample and population

Reasons for Sampling

• Budget and time Constraints (in case of largepopulations)

• High degree of accuracy and reliability (if sampleis representative of population)

• Sampling may sometimes produce moreaccurate results than taking a census as in thelatter, there are more risks for makinginterviewer and other errors due to the highvolume of persons contacted and the number ofcensus takers, some of whom may not be well-trained

Population

It refers to the entire group of people, events orthings of interest that the researcher wishes toinvestigate.Example: If regulators want to know how patientsin nursing homes run by a company in France arecared for, then all the patients in all the nursinghomes run by them will from the population.

Element

An element is a single member of a populationExample: If 1000 blue collar workers(laborworkers) in a particular organization happen to bethe population of interest to a researcher, eachblue collar worker therein is an element.

Sample

A sample is a subset or subgroup of thepopulation. By studying the sample, the researchershould be able to draw conclusions that aregeneralizable to the population of interest.

Example: If there are 145 in-patients in a hospitaland 40 of them are to be surveyed by the hospitaladministrator to access their level of satisfactionwith the treatment received, then these 40members will be the sample.

Sampling unit

It is the element or set of elements that is availablefor selection in some stage of sampling process.

Example: Sampling units in a multistage sampleare city blocks, households, and individuals withinthe households.

Subject

It is a single member of the sample, just as anelement is a single member of the population.

Example: If a sample of 50 machines from a totalof 500 machines is to inspected, then everyone ofthe 50 machines is a subject, just as every singlemachine in the population of total population of500 machines is an element

Parameters

The characteristics of the population such as thepopulation mean, the population standarddeviation, and the population variance are referredto as its parameters.

Example: Average weight, µ, of all 30 year oldwomen in Australia, % of voters, p, in N.S.W whothink the Government is doing a good job tocontrol inflation.

The Sampling Process

Sampling is the process of selecting a sufficientnumber of right elements from the population so,the major steps in the sampling include.1. Defining the population2. Determine the sample process3. Determine the sampling design4. Determine the appropriate sample size5. Execute the sampling process

The Sampling Process

Defining the populationSampling begins with precisely defining the targetpopulation. The target population must be definedin terms of elements, geographical boundaries andtime.

Example: A target population may be, for example,all faculty members in the Department ofManagement Sciences in the V-COMSATSnetwork,All housewives in Islamabad,All pre-college students in Rawalpindi,

• The target group should be clearly defined if possible, for example, do all pre-college students include only primary and secondary students or also students in other specialized educational institutions?

Determining the sample frameThe sampling frame is a (physical) representationof all the elements in the population from which hesample is drawn. Also termed as a List.

• Often, the list does not include the entirepopulation. The discrepancy is often a source oferror associated with the selection of the sample(sampling frame error)

• Information relating to sampling frames can beobtained from commercial organizations

Example: Student telephone directory (for thestudent population), the list of companies on thestock exchange, the directory of medical doctorsand specialists, the yellow pages (for businesses)

Determining the sample design

Two major types of sampling• Probability samplingThe elements in the population have some known,non zero chances or probability of being selectedas sample subjects.• Non probability samplingThe elements do not have a known orpredetermined chance of being selected assubjects.

Factors affecting sampling design

• The relevant target population of focus to thestudy

• The parameters we are interested ininvestigating

• The kind of sample frame is available

• Costs and Time are attached to the sampledesign and collection of Data

Determining the sample size

The decision about the how large the sample sizeshould be can be very difficult one. These factorsaffecting the sampling decision are• The research objective• The extent of precision desired(the confidence

interval)• The acceptance risk in predicting that level of

precision(confidence level)• The amount of variability in the population itself• The cost and time constraints• In some cases, the size of population itself

Executing the sample process

In this final stage of sampling process, decisionwith respect to thethe target population,the sampling frame,the sample technique, andthe sample size have to be implemented.

• Example:• A young researcher was investigating the

antecedents of salesperson performance.

• To examine his hypotheses, data were collected from the chief sales executive in the Pakistan (the target population) via mail questionnaire.

• The sample was initially drawn from the published business register (the sampling frame), but supplemented with respondent recommendations and other additions, in a judgment sampling methodology.

• The questionnaires were subsequently distributed to sales executives of 450 companies (the sample size).

Non response and non response errors

• A failure to obtain information from a number ofsubjects included in the sample

• Those who do respond to your survey aredifferent from those who did not on (one of the)characteristics of interest in your study

• Two important sources of non response errorsare not at homes and refusals

Reducing the rate of refusals

• The rate of refusals depends, among otherthings, on the length of the survey, the datacollection method and the backing of research.

• Decrease in survey length, personalinterviews/questionnaire instead of mailquestionnaire and the sponsorship of theresearch often improve the overall return rate.

Recap

• Sampling is the process of selecting the rightindividuals

• Sample is used to represent the whole data orpopulation

• Sampling process include defining population,sample frame, sampling design, sample sizeand sampling process


Lecture No :15(Sampling Design / Probability vs Non probility)

Recap



• Sampling process include defining population,sample frame, sampling design, sample sizeand sampling process

Lecture Objectives

• Differentiate between probability and nonprobability sampling

• Learn about the types of probability sampling, itsadvantages and disadvantages

• Learn about the types of non probabilitysampling, its advantages and disadvantages

• Issues relevant to sample design and collection

Probability Sampling Unrestricted or simple random sampling

• Technique which ensures that each element inthe population has an equal chance of beingselected for the sample.

• The simple random sampling is the least biasand offer the most generalizability.

Probability Sampling

• The major advantage of simple randomsampling is its simplicity.

• The sampling process could becomecumbersome and expensive.

Example: Choosing raffle tickets from a drum,computer-generated selections, random-digittelephone dialing.

Simple random sampling


Restricted or complex probability sampling:

• It is an alternate to simple random samplingdesign, several complex probability samplingdesigns can be used.

• Efficiency is improved in that more informationcan be obtained for a given sample size usingthe complex probability sampling procedures.


The most common complex probability samplingdesign1. Systematic sampling2. Stratified sampling3. Cluster sampling

1. Area sampling4. Double sampling

Probability Sampling Systematic Sampling:• Technique in which an initial starting point is

selected by a random process, after which everynth number on the list is selected to constitutepart of the sample.

• Sampling interval (SI) = population list size (N)divided by a pre-determined sample size (n)

• How to draw:• 1) calculate SI, say (200/20)=10• 2) select a number between 1 and SI randomly, i.e. 1-10• 3) go to this number as the starting point and the item on the list

here is the first in the sample, e.g 3• 4) add SI to the position number of this item and the new position

will be the second sampled item, e.g 3+10=13• 5) continue this process until desired sample size is reached.

• For systematic sampling to work best, the listshould be random in nature and not have someunderlying systematic pattern.

• E.g: Office directory with the Senior Manager,Middle manager ….names are listed in eachdepartment. This can create as systematicproblem

Probability Sampling Stratified Sampling:• Technique in which simple random subsamples

are drawn from within different strata that sharesome common characteristic. Within the groupthey are homogenous and among the groupthey are heterogeneous.


Stratified SamplingExample: The student body of CIIT is divided intotwo groups (management science, engineering)and from each group, students are selected for asample using simple random sampling in each ofthe two groups, whereby the size of the sample foreach group is determined by that group’s overallstrength.


Cluster Sampling• Technique in which the target population is first

divided into clusters. Then, a random sample ofclusters is drawn and for each selected clustereither all the elements or a sample of elementsare included in the sample.

• Cluster samples offer more heterogeneity withingroups and more homogeneity among groups


Area samplingSpecific type of cluster sampling in which clustersconsist of geographic areas such as counties, cityblocks, or particular boundaries within a locality.• Area sampling is less expensive than most other

sampling designs and it is not dependent onsampling frame.

• Key motivation in cluster sampling is costreduction.


Area samplingExample: A city map showing the blocks of the cityis adequate information to allow the researcher totake a sample of the blocks and obtain data fromthe resident therein.Example: If you wanted to survey the residents ofthe city, you would get a city map, take a sample ofcity blocks and select respondents within each cityblock.


Single stage and multistage cluster sampling• Single stage cluster sampling involves the

division of population into convenient clusters,randomly choosing the required number ofclusters as sample subjects, and investigating allthe elements in each of the randomly chosenclusters

• Cluster sampling can also be done in severalstages and is then known as multistage clustersampling.


Example: If we were to do a national survey of theaverage monthly bank deposits, cluster samplingwould be used to select the urban, semi urban andrural geographical location for study. At the nextstage particular areas in each of these locationswould be chosen. At the third stage, banks withineach area would be chosen.Example:


Double sampling:• A sampling design where initially a sample is

used in a study to collect some preliminaryinformation of interest, and later a subsample ofthis primary sample is use to examine the matterin more detail.


Double samplingExample: A structured interview might indicate thata subgroup of respondents has more insight intothe problems of the organization. Theserespondents might be interviewed again and againand asked additional questions.

Non-Probability Sampling

Convenience Sampling:• Sampling technique which selects those

sampling units most conveniently available at acertain point in, or over a period, of time.


Convenience Sampling:• Major advantages of convenience sampling is

that is quick, convenient and economical; amajor disadvantage is that the sample may notbe representative.

• Convenience sampling is best used for thepurpose of exploratory research andsupplemented subsequently with probabilitysampling.


Judgment (purposive) Sampling:• Sampling technique in which the business

researcher selects the sample based onjudgment about some appropriate characteristicof the sample members.

Example: Selection of certain students who areactive in the university activities to inquire aboutthe sports and recreation facilities at the university.

Recap

• Simple random sampling and restrictedsampling are two basic types of probabilitysampling.

• Probability ( Simple Random, Systematic,Cluster, Single stage/multistage, Doublesampling)

• Non Probability (Convenience, judgment)


Lecture No :16( Sampling / Non Probability, Confidence and Precision, Sample size)

Recap Lecture

• Systematic ,stratified sampling, cluster, area anddouble sampling are the common types ofcomplex sampling.

• Convenience, judgment, quota and snowballsampling are the common types of nonprobability sampling.

Lecture Objectives

• Non Probability Based sampling (Quota/snowball)

• Discuss about the precision and the confidence.

• Precision and Confidence

• Factors to be taken into consideration fordetermining sample size.

• Managerial implications of sampling.

Non-Probability SamplingQuota Sampling:This is a sampling technique in which the businessresearcher ensures that certain characteristics of apopulation are represented in the sample to anextent which is he or she desires.


Quota SamplingExample: A business researcher wants to determinethrough interview, the demand for Product X in adistrict which is very diverse in terms of its ethniccomposition.

If the sample size is to consist of 100 units, thenumber of individuals from each ethnic groupinterviewed should correspond to the group’spercentage composition of the total population of thatdistrict.

Quota Sampling

Example: Quotas havebeen set for gender only.Under thecircumstances, it’s nosurprise that the sampleis representative of thepopulation only in termsof gender, not in terms ofrace. Interviewers areonly human;.

Non-Probability SamplingSnowball Sampling :• This is a sampling technique in which individuals

or organizations are selected first by probabilitymethods, and then additional respondents areidentified based on information provided by thefirst group of respondents


Snowball Sampling• The advantage of snowball sampling is that smaller

sample sizes and costs are necessary; a majordisadvantage is that the second group ofrespondents suggested by the first group may bevery similar and not representative of the populationwith that characteristic.

Example: Through a sample of 500 individuals, 20antique car enthusiasts are identified which, in turn,identify a number of other antique car enthuiasts

More Snowball Sampling…More systematic versions of snowball sampling canreduce the potential for bias. For example,“respondent-driven sampling” gives financialincentives to respondents to recruit peers.

Issues in Sample Design and Selection

• Availability of Information – Often information onpotential sample participants in the form of lists,directories etc. is unavailable (especially indeveloping countries) which makes somesampling techniques (e.g. systematic sampling)impossible to undertake.

• Resources – Time, money and individual or institutional capacity are very important considerations due to the limitation on them. Often, these resources must be “traded” against accuracy.

Issues in Sample Design and Selection

• Geographical Considerations – The number anddispersion of population elements maydetermine the sampling technique used (e.g.cluster sampling).

• Statistical Analysis – This should be performedonly on samples which have been createdthrough probability sampling (i.e. not probabilitysampling).

• Accuracy – Samples should be representative ofthe target population (less accuracy is requiredfor exploratory research than for conclusiveresearch projects).

Issues of precision and confidence indetermining sample size

Precision• Precision is how close our estimate is to the true

population characteristic.• Precision is the function of the range of

variability in the sampling distribution of thesample mean.

Population and Sample distinctiveness

• Sample Statistics( Mean, Std Deviation, Variance) and Population parameters ( Mean, Std Deviation, Variance)

• Compare the Sample estimates and population characteristic. Where the estimates should be the representative of the population charactertics

• Sample statistics (mean, sd, ..) should be representative of the population parameters(mean, sd …)

Issues of precision and confidence indetermining sample size

Precision:•How close are the estimates to the population.

•While expecting that the population mean would itfall between (+,- )10 points or (+,-) 5 points basedon the sample estimates is precision.

•The narrower the more precise our statement is

•E.g: The average age of the a particular classbased on the sample is between 20 and 25•Or it between 18 and 28.

•How close are the estimates to the population.

Confidence• Confidence denotes how certain we are that our

estimate will hold true for the population.• The level of confidence can range from 0 to

100%. However 95% confidence is theconventionally accepted for most businessresearch.

• The more we want to be precise the less confident we become that our statement is going to be true.

• So at one level we want to be accurate in our statement but on the other we taking a higher risk of proved incorrect.

• In order to maintain the precision and increase the confidence or increase the precision and the confidence we need to have a larger sample.

Determining sample size

Roscoe (1975) proposes the following rules ofthumb for determining sample size.

• Sample sizes larger than 30 and less than 500are appropriate for most research

• Where sample sizes are broken into subsamples(males/females, juniors/seniors etc.), a minimumsample size of 30 for each category isnecessary.

Determining sample size

• In multivariate research (including multiple regression analysis), the sample size should be several times (preferably ten times or more) as large as the number of variables in the study.

• For simple experimental research with tight experimental controls (matched pairs, etc.), successful research is possible with samples as small as 10 to 20 in size.

• Tools and mathematical equations are available to establish the right size of the sample.

• Refer to the book for the sample size calculation equation.

• Standard Tables are available

• Use a software like RAO calculator available on the internet.

Types of Sampling Designs

Sampling Designs

Non-probability Probability

Convenience Judgmental Quota Snowball

Systematic Stratified Cluster Other SamplingTechniques

Simple Random

Managerial Implications

• Awareness of sampling designs and sample sizehelps managers to understand why a particularof sampling is used by researchers.

• It also facilitates understanding of the costimplications of different designs, and the tradeoff between precision and confidence vis-à-visthe costs.

Managerial Implications

• This enables managers to understand the riskthey take in implementing changes based on theresults of the research study.

• By reading journal articles, this knowledge alsohelps managers to assess the generazibility ofthe findings and analyze the implications oftrying out the recommendations made therein intheir own system.

Recap

• Non Probability based sampling (• Precision we estimate the population parameter

to fall within a range, based on sample estimate.• Confidence is the certainty that our estimate will

hold true for the population.• Roscoe (1975) rules of thumb for determining

sample size.• Some sampling designs are more efficient than

the others.• The knowledge about sampling is used for

different managerial implications.


Lecture No :17( Research Paper -1 and 2 )

Recap

• Non Probability based sampling (• Precision we estimate the population parameter

to fall within a range, based on sample estimate.• Confidence is the certainty that our estimate will

hold true for the population.• Roscoe (1975) rules of thumb for determining

sample size.• Some sampling designs are more efficient than

the others.• The knowledge about sampling is used for

different managerial implications.

Objective

• 2 Research Papers • First Review Paper• Second Empirical Study

Important Information to be noted

• Title • Author(s)• Year of publication • Journal of publication• Key variables ( Independent, Dependent)• Relationships between variables• Model• Hypothesis

• Method• Findings• Discussions• Implications• Future Directions• References

• Research Paper/ Thesis / Research Report• Deliverables of Research • While

Qualitative Paper


Lecture No :18(Experimental Design)

Recap

• Difference between • Research Paper Qualitative in nature• Research paper Empirical

Objective

• Experimental Design• Causal vs Correlations• Field Experiment vs Lab Experiments

• When we want to find cause ?• Such as Absenteeism and Incentives.• Some give bonus days , some give cash and some recognition.

• 22% of companies said that their incentive where effective, 66% some what effective and 12% not effective

• Question is which incentives cause 22% companies to be effective in reducing absenteeism

Causal Vs Correlation

• What factor are related to decrease in sales ?

• What causes the decrease in sales ?

• To establish that X cases Y three conditions need to be meet.

• (A) Both X and Y should covary• (B) X should precede Y• (C) No other Variable should possibly be causing the change in Y

• Lab Experiment:– Tight Control on the confounding variables hence higher internal validity

– Manipulation of independent variable

• Field Experiment:– Less control on confounding variable but good external validity( Generalizability)

– Manipulation of independent variable


Lecture No :19(Experimental Design-Cont)

Recap

• Causal vs Correlation• Field Study vs Field Experiment• Control and Treatment• Confounding variables

– controllable un controllable• Factors effecting Internal Validity

– (History, Maturation, Testing effect, Instrument,selection Biases, Mortality..)

Objective

• Factors effecting External Validity• When Experimental Design is necessary• Different types of experimental Designs

When to Conduct Experimental Design

• Control Group• Experimental Group• Expose (Treatment)• Pretest score (Instrument)• Post Test Score (Instrument)• Difference

Pretest and Posttest(Problem Instrument Effect)

Posttest(Problem , Matching ,Mortality Effect)

Pretest and Posttest Experimental and Control GroupRandomized hence no effect of history, maturation,

testing, instrument(Problem of Mortaility)

• Different internal validity issues are taken care of such as

• Pretest and posttest of Group 2 allows to take care of history, maturation, instrumentation, regression.

• Group 3 remove the testing effect

Recap

• When to use experimental designs• Pretest and Posttest Experimental Group Design

• Posttests only with Experimental and Control Group

• Pretest and Posttest Experimental and Control Group Design

• Solomon Four Group Design

• When to have lab experiments and field experiments

• Issues of internal validity• Issues of external validity • Certain experimental design counter the effects of internal validity


Lecture No :20(User Response to an Online Information System: A Field Experiment )

Recap

• Experimental Design– When to use experimental designs– Pretest and Posttest Experimental Group Design– Posttests only with Experimental and Control Group

– Pretest and Posttest Experimental and Control Group Design

– Solomon Four Group Design

• Issues of internal validity• Issues of external validity • Certain experimental design counter the effects of internal validity

Objective

• Review a research article which has applied an experimental type of methodology

Important Information to be noted while reviewing an article

• Title • Author(s)• Year of publication • Journal of publication• Key variables ( Independent, Dependent)• Relationships between variables• Model• Hypothesis

• Method• Findings• Discussions• Implications• Future Directions• References

User Response to an Online Information System: A Field Experiment

• Experimental Design Research Paper• Author(s): Charles R. Franz, Daniel Robey and Robert R. Koeblitz Source: MIS Quarterly, Vol. 10, No. 1 (Mar., 1986), pp. 29‐42

• Published by: Management Information Systems Research Center, University of Minnesota

• Stable URL: http://www.jstor.org/stable/248877• Accessed: 25/06/2013 07:40

Abstract

Problem / the issue

Literature support for the problem

Literature Gap

Literature Support for the Gap

Research methodology Direction

Research Objectives / Research Problem/ Research Question

Hypothesis

Null Hypothesis 1(1.1,1.2,1.3,1.4,1.5,1.6)

Null Hypothesis 2(2.1,2.2,2.3)

Field Settings

Research Design

Measures

Measurement/Scales

Results


Lecture No : 21Data Preparation and Data Entry

Recap Lecture

In the last few lectures we discussed about:

• Research Design• The purpose, investigation type, researcher

interference, study setting, unit of analysis, timehorizon, Measurement of variables

• Sources of Data• Sampling• Experimental Design

Lecture Objectives

Getting the data ready for analysis• Data preparation• Coding, codebook, pre-coding, coding rules• Data entry• Editing data• Data transformation

Data Preparation and Description

• Data preparation includes editing, coding, anddata entry

• It is the activity that ensures the accuracy of thedata and their conversion from raw form toreduced and classified forms that are moreappropriate for analysis.

• Preparing a descriptive statistic summary isanother preliminary step that allows data entryerrors to be identified and corrected.

Getting the Data Ready for Analysis

• After data obtained through questionnaire, theyneed to be coded, keyed in, and edited.

• Outliers, inconsistencies and blank responses, ifany, have to be handled in some way.

Coding

• Data coding involves assigning a number to theparticipants responses so, they can be entered intodata base.

• In coding, categories are the partitions of a data setof a given variable. For instance, if the variable isgender, the categories are male and female.

• Categorization is the process of using rules topartition a body of data.

• Both closed and open questions must be coded.

Coding Cont.

• Numeric coding simplifies the researcher’s taskin converting a nominal variable like gender to a1 or 2.

Code Construction

There are two basic rules for code construction.• First, the coding categories should be

exhaustive, meaning that a coding categoryshould exist for all possible responses.

• For example, household size might be coded 1,2, 3, 4, and 5 or more.

• The “5 or more” category assures all subjects ofa place in a category.

Code Construction Cont.

• Second, the coding categories should bemutually exclusive and independent.

• This means that there should be no overlapamong the categories to ensure that a subject orresponse can be placed in only one category.

Code Construction Cont.

• Missing data should also be represented with acode.

• In the “good old days” of computer cards, anumeric value such as 9 or 99 was used torepresent missing data.

• Today, most software will understand that eithera period or a blank response represents missingdata.

Codebook

• A codebook contains each variable in the studyand specifies the application of coding rules tothe variable.

• It is used by the researcher or research staff topromote more accurate and more efficient dataentry.

• It is the definitive source for locating thepositions of variables in the data file duringanalysis.

Sample Codebook

Pre-coding

• Pre-coding means assigning codebook codes tovariables in a study and recording them on thequestionnaire.

• Or you could design the questionnaire in such away that apart from the respondents choice italso indicates the appropriate code next to it.

• With a pre-coded instrument, the codes forvariable categories are accessible directly fromthe questionnaire.

Sample Pre-coded Instrument

Coding Open-Ended Questions

• One of the primary reasons for using open-ended questions is that insufficient informationor lack of a hypothesis may prohibit preparingresponse categories in advance. Researchersare forced to categorize responses after the dataare collected.

Coding Open-Ended Questions Cont.

• In the Figure on the next slide, question 6illustrates the use of an open-ended question.After preliminary evaluation, responsecategories were created for that item. They canbe seen in the codebook.

Coding Open-Ended Questions Cont.

Coding Rules

Categories should be

Appropriate to the research problemExhaustive

Mutually exclusive Derived from one classification principle

Data Entry

• After responses have been coded, they can beentered into data base.

• Raw data can be entered through any softwareprogram.

• For example: SPSS Data Editor.

Data Entry Cont.

Database Programs

Optical Recognition

Digital/Barcodes

Voicerecognition

Keyboarding

Editing Data

• After data entered, the blank responses, if any,have to be handled in some way, andinconsistent data have to be checked andfollowed up.

• Data editing deals with detecting and correctingillogical, inconsistent, or illegal data andomissions in the information returned by theparticipants of study.

Editing Data Cont.

Criteria

Consistent

Uniformly entered

Arranged forsimplification

Complete

Accurate

Field Editing

• Field Editing Review

• Entry Gaps Callback

• Validates Re-interviewing

Field Editing Review

• In large projects, field editing review is aresponsibility of the field supervisor.

• It should be done soon after the data have beencollected.

• During the stress of data collection, datacollectors often use ad hoc abbreviations andspecial symbols.

• If the forms are not completed soon, the field interviewer may not recall what the respondent said.

• Therefore, reporting forms should be reviewed regularly.

Field Editing Cont.

• Entry Gaps Callback

• When entry gaps are present, a callback shouldbe made rather than guessing what therespondent probably said.

Field Editing Cont.

• Validates Re-interviewing

• The field supervisor also validates field resultsby re-interviewing some percentage of therespondents on some questions to verify thatthey have participated.

• Ten percent is the typical amount used in datavalidation.

Central Editing

• Scale of Study Number of Editors

• At this point, the data should get a thoroughediting.

• For a small study, a single editor will producemaximum consistency.

• For large studies, editing tasks should beallocated by sections.

Central Editing Cont.

• Wrong Entry Replacements

• Sometimes it is obvious that an entry is incorrectand the editor may be able to detect the properanswer by reviewing other information in thedata set.

• This should only be done when the correctanswer is obvious.

• If an answer given is inappropriate, the editorcan replace it with a no answer or unknown.


• Fakery Open-ended Questions

• The editor can also detect instances of armchairinterviewing, fake interviews, during this phase.

• This is easiest to spot with open-endedquestions.


Be familiar with instructions given to interviewers and coders

Do not destroy the original entry

Make all editing entries identifiable and in standardized form

Initial all answers changed or supplied

Place initials and date of editing on each instrument completed

Guidelines for Editors

Handling “Don’t Know” Responses

• When the number of “don’t know” (DK)responses is low, it is not a problem. However, ifthere are several given, it may mean that thequestion was poorly designed, too sensitive, ortoo challenging for the respondent.

• The best way to deal with undesired DK answersis to design better questions at the beginning.

• If DK response is legitimate, it should be kept asa separate reply category.

Data Transformation

• Data transformation, a variation of data coding,is a process of changing the original numericalrepresentation of a quantitative value to anothervalue.

• E.g: The data given is in per year consumptionand we need it for each month.

• Data are typically changed to avoid problems inthe next stage of data analysis process.

Data Transformation Cont.

• For example, economists often use a logarithmictransformation so that the data are more evenlydistributed.

• Data transformation is also necessary whenseveral questions have been used to measure asingle concept.

• E.g: Intentions to leave is measured through 10questions which need to be transformed into asingle value for a single respondent

Recap

• Questionnaire checking involves eliminatingunacceptable questionnaires.

• These questionnaires may be incomplete,instructions not followed, missing pages, pastcutoff date or respondent not qualified.

• Editing looks to correct illegible, incomplete,inconsistent and ambiguous answers.

• Coding typically assigns alpha or numeric codesto answers that do not already have them so thatstatistical techniques can be applied.

Recap Cont.

• Cleaning reviews data for consistencies.Inconsistencies may arise from faulty logic, outof range or extreme values.

• Statistical adjustments applies to data thatrequires weighting and scale transformations.


Lecture No : 22Introduction to SPSS

Recap

• Questionnaire checking involves eliminatingunacceptable questionnaires.

• Editing looks to correct illegible, incomplete,inconsistent and ambiguous answers.

• Coding typically assigns numeric codes toanswers that do not already have them so thatstatistical techniques can be applied.

• Some times we need to treat the missingvalues.

Recap Cont.

• Cleaning reviews data for consistencies.Inconsistencies may arise from faulty logic, outof range or extreme values.

• Statistical adjustments applies to data thatrequires weighting and scale transformations.

objective

• How to use SPSS for Data entry– Defining variables– Assigning them values– Assigning sizes and constraints– Data entry using data from coded Questionnaires

• How to generate simple descriptive summaries

Job Satisfaction

Intention to Leave


Lecture No :23(Feel of the Data)

Recap Lecture

In the last lecture we discussed about:• How to use SPSS for Data entry

– Defining variables– Assigning them values– Assigning sizes and constraints– Data entry using data from coded Questionnaires


Lecture Objectives

Getting the feel for the data• Frequencies• Bar charts and pie charts• Histogram• Stem and leaf display• Pareto diagram• Box plot• SPSS cross tabulation

Getting a Feel for the Data

• We can acquire the feel for the data by obtaininga visual summary or by checking the centraltendency and the dispersion of the variable.

• We can also get to know our data by examiningthe relationship between two variables.

Getting a Feel for the Data Cont.

• Getting a feel for the data is thus the necessaryfirst step in all data analysis.

• Based on this initial feel, further detailed analysismay be undertaken to test the goodness of thedata.

Frequencies

• Frequencies simply refer to the number of timesvarious subcategories of a certain phenomenonoccur,

• Percentage and the cumulative percentage oftheir occurrence can be easily calculated.

Frequency Cont.

Frequency and Percentage

Example: Ad Recall

Bar Charts and Pie Charts

• Frequencies can also be visually displayed asbar charts, histograms, or pie charts.

• Bar charts, histograms, and pie charts help us tounderstand our data.

Bar Chart

In this slide, the same data are presented in theform of a bar chart. (Nominal Data)

Pie Chart

Data may be more readily understood whenpresented graphically. (Nominal Data)

Histogram

• A histogram is a graphical bar chart that groupscontinuous data values into equal intervals, withone bar for each interval. (Ratio Data)

Histogram Cont.

Stem-and-Leaf Display Cont.

• The stem-and-leaf display is a technique that isclosely related to the histogram. It shares someof the histogram’s features but offers severalunique advantages.

• (Continuous data/ Ratio scale)• In contrast to histograms, which lose information

by grouping data values into intervals, the stem-and-leaf presents actual data values that can beinspected directly, without the use of enclosedbar or asterisks as the representation medium.

Stem-and-Leaf Display(e.g. Annual Purchase)

Stem-and-Leaf Display Cont.

• Visualization is the second advantage of stem-and-leaf displays.

• The range of values is apparent at a glance, andboth shape and spread impressions areimmediate. (56,56,56) concentration and spread

• Patterns in the data are easily observed.• Each line or row in the display is referred to as a

stem, and each piece of information on the stemis called a leaf.

Pareto Diagram

• Pareto diagrams represent frequency data as abar chart, ordered from most to least, overlaidwith a line graph

• (Nominal Data)• The cumulative percentage at each variable

level is shown.

• The percentages sum to 100 percent.

Pareto Diagram Cont.

Pareto Diagram Cont.

• The data are derived from a multiple-choice-single-response scale,

• For multiple-choice-multiple-response scale, orfrequency counts of words or themes fromcontent analysis. Nominal Scale but muli-response e.g.

• Which soft drinks you consume:– Obs1 obs2 obs3…………..Frequency

Coke x x x 3 Mrinda x 1 Sprite x 1 Amrit x 1

Boxplot Components

• The boxplot, or box-and-whisker plot, is anothertechnique used frequently in exploratory dataanalysis.

• A boxplot reduces the detail of the stem-and-leafdisplay and provides a different visual image ofthe distribution’s location,

• spread,• shape,• tail length,• outliers.

Boxplot Components Cont.

The ingredients of the plot are• The rectangular plot that encompasses 50% of

the data values.• A center line--marking the median and going

through the width of the box.• Consists of the median, the upper and lower

quartiles, and the largest and smallestobservations.

Boxplot Components Cont.

Boxplot Comparison

SPSS Cross-Tabulation

• Cross-tabulation is a technique for comparingdata from two or more categorical variables(Nominal Data).

• It is used with demographic (male/female)variables and the study’s target variables (takenoverseas assignment).

• The technique uses tables having rows andcolumns that correspond to the levels or codevalues of each variable’s categories.

SPSS Cross-Tabulation Cont.

• Row and column totals, called marginal’s,appear at the bottom and right “margins” ofthe table.

• When tables are constructed for statisticaltesting, we call them contingency tables andthe test determines if the classificationvariables are independent of each other.


• The figure is an example of a computer-generated cross-tabulation. This table has tworows for gender and two columns for assignmentselection.

• The combination produces four cells. Dependingon what you request for each cell, it can containa count of the cases of the joint classificationand also the row, column, and/or the totalpercentages.

Percentages in Cross-Tabulation

Percentages serve two purposes in datapresentation.• They simplify the data by reducing all numbers

to a range from 0 to 100. (Standardize)• They also translate the data into standard form

with a base of 100 for relative comparisons.

Percentages in Cross-Tabulation Cont.

Percentages in Cross-Tabulation Cont.

• One can see in the figure that the percentage offemales selected for overseas assignments rosefrom 15.8 to 22.5 percent of their respectivesamples. (Female and Yes)(Row %

• Among all overseas selectees, in the first study,21.4% were women, while in the second study,37.5% were women.

• The tables verify an increase in women withoverseas assignments, but we cannot concludethat their gender had anything to do with theincrease.

Recap

• Frequency refers to number of times various subcategorizes occur in the same pattern.

• Frequencies can also be visually displayed asbar charts, histograms, or pie charts.

• Histogram is graphical bar chart.• The stem-and-leaf presents actual data values

that can be inspected directly.

Recap Cont.

• A boxplot reduces the detail of the stem-and-leafdisplay.

• Cross-tabulation is a technique for comparingdata from two or more categorical variables.

• Percentages serve two purposes in datapresentation.


Lecture No :24

Recap LectureIn the last lecture we discussed about:• Frequencies• Bar charts and pie charts• Histogram• Stem and leaf display• Pareto diagram• Box plot• SPSS cross tabulation

Lecture Objectives

Getting the feel for the data• Measure of central tendency• Measure of Dispersion• Relationship Between Variables• χ² Test

Lecture Objectives Cont.

Testing the goodness of dataReliability• Cronbach’s alpha• Split halfValidity• Factorial• Criterion• Convergent• Discriminant

Measure of Central Tendency

There are three measures of central tendency1. The mean2. The median3. The mode

Measure of Central Tendency Cont.

The mean• The mean or the average, is a measure of

central tendency that offers a general picture ofthe data.

• The mean or average of a set of, say, tenobservations, is the sum of ten individualobservations divided by ten (the total no ofobservations).

• (54+50+35+67+50)/5=51.2


The median• The median is the central item in a group of

observations when they are arrayed in either anascending or a descending order.

• 35,50,50,54,67------50


The mode• In some cases, a set of observations does not

lend itself to meaningful representation througheither the mean or the median, but can besignified by the most frequently occurringphenomenon.

• 54,50,35,67,50-----50

Measure of Dispersion

• Dispersion is the variability that exist in a set ofobservations.

• Two sets of data might have the same mean, butthe dispersion could be different.

54 3450 5050 50

35 35

67 87

mean 51.2 51.2

sdv 11.43241 21.46392

Measure of Dispersion Cont.

The three measures of dispersions connected withthe mean are1. The range2. The variance3. The standard deviation


The range• Range refers to the extreme values in a set of

observations.• 54,50,35,67,50• (35,67)


The variance• The variance is calculated by subtracting the

mean from each of the observations in the dataset, taking a square of this difference, anddividing the total of these by the number ofobservations.


The standard deviation• Another measure of dispersion for interval and

ratio scaled data, offers an index of the spreadof a distribution or the variability in the data.

• It is a very commonly used, measure ofdispersion, and is simply square root of thevariance.

Relationship Between Variables

• Parametric tests from testing relationshipbetween variables such as Person Correlationusing interval and ratio scales

• Nonparametric tests are available to assess therelationship between variables measured on anominal or an ordinal scale.

• Spearman’s rank correlation and Kendall’s rankcorrelation are used to examine relationshipsbetween interval and/or ratio variables.

Pearson Correlation

Rank Correlations

• To test the strength and direction of association that exists between two variables

• The variables are using ordinal scale• E.g Students’ score in two different exams

i.e. English and Math• Correlations (SPSS)

» Bi vitiate » Spearman

– Check for value of r and P

Relationship Between Nominal Variables: χ² Test

• Sometimes we want to know if there is arelationship between two nominal variables orwhether they are independent of each other.

• The χ² test compares the expected frequencies(based on the probability) and the observedfrequency.

Testing Goodness of Data

Goodness of data can be tested by two measures• Reliability• Validity

Reliability

• The reliability of a measure is established bytesting for both consistency and stability.

• Consistency indicates how well the itemsmeasured a concept having together as a set.

Reliability Cont.

• Cronbach’s alpha is a reliability coefficient thatindicates how well the items in a set arepositively correlated to one another.

• Cronbach’s alpha is computed in terms of theaverage intercorrelations among the itemsmeasuring the concept.

• The closer Cronbach’s alpha is to one, thehigher the internal consistency reliability.

Reliability Cont.

• Another measure of consistency reliability usedin specific situations is the split half reliabilitycoefficient.

• Split half reliability is obtained to test forconsistency when more than one scale,dimensions, or factor is assessed.

Validity

• Factorial validity can be established bysubmitting the data for factor analysis.

• Factor analysis reveals whether the dimensionsare indeed tapped by the items in the measure,as theorized.

Validity Cont.

• Criterion related validity can be established bytesting for the power of the measure todifferentiate individuals who are known to bedifferent.

Validity Cont.

• Convergent validity can be established whenthere is high degree of correlation between twodifferent sources responding to the samemeasure.

• Example: Both supervisors and subordinatesrespond similarly to a perceived reward systemmeasure administered to them.

Validity Cont.

• Discriminant validity can be established whentwo distinctly different concepts are notcorrelated to each other .

• Example: Courage and honesty, leadership andmotivation, attitudes and behaviors.

SPSS

• Cronbach Alpha (Reliability)• Factor Analysis (Validity)

Recap

• Goodness of data is measured by reliability andvalidity.

• Three measures of central tendency: mean,median and mode.

• Dispersion is the variability.• Three measures of dispersion are: range,

variance and standard deviation.• Correlation• SPSS Cronbach Alpha (Reliability) Factor

Analysis (Validity)


Lecture No :25(Hypothesis Testing – Difference in Groups)

Recap

• Goodness of data is measured by reliability andvalidity.

• Three measures of central tendency: mean, medianand mode.

• Dispersion is the variability.• Three measures of dispersion are: range, varianceand standard deviation.

• Correlation• SPSS Cronbach Alpha (Reliability) Factor Analysis(Validity)

Hypotheses Testing

• Difference between groups

• Relationship between variables

Types of Hypotheses

Null•that no statistically significant difference exists between the groups•No Statistically significant relationship exists between variables

Alternative•logical opposite of the null hypothesis•that a statistically significant difference does exist between groups •That statistically significant relationship exists

Choose Appropriate Tests

• Based on the number of variables i.e. two variables relationship (Univariate)and many variables (Multivariate) statistical techniques.

• The type of scales Nominal, Ordinal(Non Metric) , Interval and Ratio(Metric) used choose appropriate tests

• See page 338 of the text book.

Computer Outputs

• See the output results of the computer generated outputs indicating the significance level.

Testing for Statistical Significance

•State the null hypothesis•Choose the statistical test

•Select the desired level of significance•Compute the calculated difference value•Obtain the critical value•Usually the software now provides the standard significance values and the f or t values. Based on the significance level value one can interpret the test

•Interpret the test

Selected Group Difference Cases

• Group difference – Testing single mean– Testing two related means (ratio)– Testing two related samples when data is in ordinal / nominal

– Testing two in unrelated means– Testing when more than two groups on their mean scores

Testing a hypothesis about a single mean

• One sample t test • Mean of the population from which a sample is drawn is equal to comparison standard.

• i.e. we known that the in general the students on an average study for 32 hours.

• Now you want to test that the students at V‐CIIT which are part of the student population study less.

• So the sample of V‐CIIT differ from the rest of the population needs to be tested.

• Hypothesis generated would be • Ho: The number of study hours of students V_CIIT is equal to the number of hours studied in general.(same)(no difference)

• Ha: The number of hours students of V_CIIT is less then the number of hours studied in general (< )

• SPSS • Analysis Compare means One sample T Test. • Say you set the significance level to 0.05 then• See the output results of generated from the software. See if the differences are significant or the relationship significant. (lecture 6‐7)

• If the differences are not significant then we accept the null hypotheses other wise accept the alternate

• Out Put (T value and significance level)

Testing hypotheses about two related means

• Paired samples t‐test• Examine the difference in the same group before and after the treatment

• Performance before training and after training• Two observation each employee• Null hypothesis

– There is no difference between the performance of before and after the training

• SPSS • use pair t test and see the value of t and it’s significance level

• If the differences are not significant then we accept the null hypotheses other wise accept the alternate

• Meaning the before and after training there was no change i.e. Null hypothesis is accepted – There is no difference between the performance of before and after the training

Non Parametric Test for paired sampled

• When population cannot be assumed to be normally be assumed distributed

• Use Wilcoxon singed –rank test , • Use McNemar’s test for non parametric and nominal data

Testing about two unrelated means

• Group difference when groups are not related and variable of interest data is in interval and ratio scales.

• E.g: Groups MBA and Non MBA compared on sales achieved.

• SPSS Analyze Compare means Independent samples T Test

• If more than two groups use ANOVA ( sales by different level of education(Metric, FA, BA/BS,Masters )

• SPSS excercises


Lecture No :26(Hypothesis Testing – Relationship)

Recap

• Null and Alternate hypotheses• Choosing the appropriate test based on number of variables and the type of scales

• Setting criteria for acceptance and rejection (significance level)

• Group difference

Objective

• Hypothesis testing the relationship/Association

• Correlations• Regression

We already know

• Descriptive versus Inferential Statistics• Statistic versus Parameter• Continuous(Ratio, Interval) versus Discrete Variables (Nominal, Ordinal)

• Measures of Central Tendency• Measures of Variability• Parametric (data normal distribution) Vs. Nonparametric (no need for normal distribution)

Measure association

• Pearson correlation coefficient• r symbolized the coefficient's estimate of linear association based on sampling data

• Correlation coefficients reveal the magnitude and direction of relationships

• Coefficient’s sign (+ or ‐) signifies the direction of the relationship

• Assumptions of r • Linearity• Bivariate normal distribution

Correlations among Variables

Regression

• Inferential statistics• Simple Regression

– (One independent and One Dependent variable)– Lowering the salary influences the performance

• Multiple Regressions – When simultaneously multiple independent variables influence the dependent variables

– Independent variables jointly are regressed– Need interval or ratio scale to use regression

• R‐Square is the value which indicates that the amount of variance explained on the dependent variable by the independent variable.

• X Y• Y=f(x)• Y=a+bx1+e• Here, x is person birth year, while a and b symbolize constants (fixed numbers).

• These constants are the regression coefficients, or, to be more exact, the a is often called the constant or the intercept

• while the b is called variable x’s regression coefficient because it determines how the predicted y values change as the value of x changes.

• The value of R‐Square is between 0 and 1• Say we receive R‐Square value .11 and sign level is 0.099 and standard error is 0.80 constant is 0.04

• It means that 11 percent of variance in the dependent variable is explained by the independent variable and the chances of it not to be true is 9 to 10 percent.

• In case there are multiple independent variables then we need to see their separate contribution

Multi Regression

Stepwise Multi Regression

• The independent variables are customer perceptions of

• 1) cost/speed valuation, • 2) security, and • 3) reliability. • In model 3, reliability is added. Looking at the R2 column, you can see that the cost/speed variable explains 77% of customer usage.

• The adjusted R2 for model 3 is .871. R2 is adjusted to reflect the model’s goodness of fit for the population.

• The standard error of model 3 is .4937.

• Unstandardized regression coefficients for all three models are shown in the lower table in the column headed B.

• The equation can be constructed as

• Y= ‐.093 + .448X1 + .315X2 + .254X3+0.497

• Standardized regression coefficients are shown in the column labeled Beta.

• Standard error is a measure of the sampling variability of each regression coefficient.

Examples

• A study in behavior consider many variables influencing the an individual intentions

• The researcher is interested to test the role of attitude , subjective norms and perceived behavior control.

• They theorize the model as attitude , subjective norms and perceived behavior control effects the intentions.

• They also hypothesize that attitude influence the intentions in positive manner.

• They hypothesize that subject norms have positive effect on intentions.

• They also hypothesize that perceived intentions control effects the behavior in positive way.

• They also hypothesize that attitude, subjective norms, perceived behavior control will significantly explain the variability in the intentions

• Attitude, subjective norm and Perceived Behavior control effect the intnetions of the individual.

• Model equation: Int=f(Att,Pbc,SN)

• The correlations between the different variables are

Correlations

1 .472** .665** .767** .525**.000 .000 .000 .000

60 60 60 60 60.472** 1 .505** .411** .379**.000 .000 .001 .003

60 60 60 60 60.665** .505** 1 .458** .496**.000 .000 .000 .000

60 60 60 60 60.767** .411** .458** 1 .503**.000 .001 .000 .000

60 60 60 60 60.525** .379** .496** .503** 1.000 .003 .000 .000

60 60 60 60 60

Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N

Attitude

SubNorm

PBC

Intent

Behavior

Attitude SubNorm PBC Intent Behavior

Correlation is significant at the 0.01 level (2-tailed).**.

Model Summary

.774a .600 .578 2.48849Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), PBC, SubNorm, Attitudea.

ANOVAb

519.799 3 173.266 27.980 .000a

346.784 56 6.193866.583 59

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), PBC, SubNorm, Attitudea.

Dependent Variable: Intentb.

Coefficientsa

.807 6.966 .000

.095 .946 .348-.126 -1.069 .290

AttitudeSubNormPBC

Model1

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Intenta.

• SPSS and Regression


Lecture No :27(Sample Research Project Using SPSS – Part ‐A)

Recap

• Hypothesis testing the relationship/Association

• Correlations• Regression

Objective

• Develop a research project from the start– Problem definition– Importance of research– Gap– Research objective/ questions – Introduction and Literature review– Theoretical framework–Methodology

• Apply SPSS for Data Analysis

Research Area and Problem

• Knowledge • Projects Knowledge• Senior Project Manager do not share their knowledge

Importance of the issue

• Experienced project managers can pass on their knowledge to their juniors which allow them to become better project managers.

• Training costs in millions and yet the area focused is seldom achieved but with senior project managers can deliver knowledge which is very pertinent to your customer and your organization.

• Organization can gain efficiency and have higher success rate , etc..

Gap

• A number of researcher have conducted research to find the antecedents to knowledge sharing (ref … ref …..ref…..)

• Among them some also have explored the knowledge sharing from the cognitive level (ref …., ref …..)

• But just one has studied knowledge sharing from the project management aspect and recommends that more research is needed (ref ….)

Introduction

• What is knowledge • What is a project• Role of Project manager• Specifics of project experience• Behavior and Intentions• Intentions formation• Theory of Reasoned Action

Theory of Reasoned Action

Subjective Norm for sharing PROJECT

knowledge

(Normative Belief & Motivation to Comply)

Intention to share PROJECT knowledge

Attitude towards sharing PROJECT knowledge

• Intentions are influenced by attitude and subjective norms

• The subjective norms concept is operationalized to have 2 sub dimensions– Norms Belief–Motivation to Comply

Literature Review• Knowledge sharing can be defined as a process of

conveying knowledge from a person to another and also to collect shared knowledge through information and technology (Hwie Seo et al., 2003)…..

• Riege (2005) lists three dozen of these barriers which need to be addressed in order to implement a knowledge management strategy. One way to understand the effect of these barriers is through the Theory of Reasoned Action (TRA). TRA helps us understand the cognitive process of formation of intentions and it has been successfully used in numerous studies to understand intentions and predict behavior (Sheppard et al., 1998)……

• One study by ……tried to study the ….knowledge sharing of projects ….. and recommended more to be conducted..

Objectives of Research/ Research Questions

• To develop a better understanding as to how knowledge sharing behavior is formed IN THE PROJECT MANGERS.– Through the cognitive (mental)process of intentions formation– Through studying intention difference between different

demographic variables • To what extent does attitude influence intentions for

sharing of project knowledge ?• To what extent does subjective norms influence intentions

for sharing of project knowledge ?• Does attitude for project knowledge mediates the

relationship between subjective norm and intentions ?• Is there a difference between the intentions to share

project knowledge and the gender?

Theoretical Framework• The attitude towards a specific action will lead to formation

of intentions , which will lead to the behavior …..• Knowledge sharing is one such act , if you have attitude

towards sharing then you would also show intent to share.• The norms influences the behavior, individual get

influenced by the people around them specially the people who they consider important. If the norms of the important people is to share then and then individuals are influenced by that but it also important that to note that individuals motivation to comply with the norm is also important ins determining the effect norms in an organization……

• So we theorize that the attitude for sharing one’s knowledge on certain ( types )projects would lead to formation intentions to share that knowledge and ultimately it would lead to actual sharing.

• So we theorize that the norms for sharing one’s knowledge on certain (types) projects in an organization by the important people would lead to formation of intentions to share provided the individual also have motivation to comply to the norms. ………

• Norms have direct impact on intentions and also indirect impact through attitude as well……

Schematic Diagram

Subjective Norm for

sharing projects knowledge

Intention to share project knowledge

Attitude towards sharing project

knowledge

position Nature

Normative Belief Motivation to Comply

Hypotheses• H1: The higher the attitude towards projects knowledge sharing the

higher the intentions to share PROJECT knowledge.•• H2: The higher the subjective norm of projects knowledge sharing the

higher the intentions to share projects knowledge.•• H3: The higher the subjective norm the higher the attitude to share

projects knowledge•• H4: The attitude mediates the relationship between subjective norm and

intentions

• H5: The women have higher level of sharing their knowledge about projects then men

• H6: The project managers permanent /temporary positions at the company would moderate the relationship between attitude and intentions

Methods• Population : Senior I.T project managers in the 150 software

house of Islamabad.• Sample: Randomly select 50 companies and approach

around 150 senior managers to be part of the study. • A 5‐point Likert scale anchored by “strongly disagree” (1) to

“strongly agree” (5) is used. It is ensured that not more than 3 responses per firms are obtained.

• Data collection: Adapted Questionnaire from (ref …) personally administered or Mailed

• Feel of data ( Descriptive Analysis‐Mean, Percentage)• Goodness of Data (Reliability and Validity‐Cron Bach,

Factor Analysis)• Group Difference ( Independent sample T test)• Inferential Statistics : Correlations and Regression Analysis

Instrument•• Attitude Towards PROJECT Knowledge Sharing [Adapted from Bock et al(2005)]•• To me, sharing PROJECT knowledge with my co‐workers is harmful…………..•• To me, sharing PROJECT knowledge with my co‐workers is good……………...•• To me, sharing PROJECT knowledge with my co‐workers is pleasant………….•• To me, sharing PROJECT knowledge with my co‐workers is worthless………...•• To me, sharing PROJECT knowledge with my co‐workers is wise………………•• Affect of Subjective Norm Towards PROJECT Knowledge sharing [Adapted from Bock et al(2005)]• My CEO/Head of organization thinks I should share PROJECT knowledge with my coworkers …………………………………………………………………•• My Boss thinks I should share PROJECT knowledge with my co‐worker ………•• My colleagues thinks I should share PROJECT knowledge with my co‐workers………………………………………………………………………….•• Generally Speaking, I accept and carry out my CEO’s policy and intentions •• Generally Speaking, I accept and carry out my Boss decision even though it is different form mine ....................................…………………….•• Generally Speaking, I respect and put in practice my colleagues decisions •

•

• Intentions to Share PROJECT Knowledge [Adapted from Bock et al (2005)]• If given opportunity, I would share PROJECT knowledge with my co‐workers…• If given opportunity, I would share my work experience with my co‐workers…………………………………………………………………………..• If given opportunity, I would share know‐how or ticks of the trade • with my co‐workers…………………………………………………………….• If given opportunity, I would share expertise from education Or training with my co‐

workers……………………………………………..• If given opportunity, I would share know‐why knowledge from work with my

coworkers…………………………………………………………………...

• Demographic: Please provide some personal Information• 1. Your gender: □Male □ Female 2. Your age? ____ (in years) •• 3. Your level of your education? □FA/FSc □Diploma □Bachelor □Masters □PhD•• 4‐Nature of your Job : □Software Development/Support □ Networking □Other( Specify)____________ •• 5‐ Your Name: ______________________(* optional)•• 6‐ Your Organization:__________________(*optional)•• 7‐ Your e‐mail : ____________________ ( Interested in receiving the results of this study) □Yes□ No •• 8‐ How long have you been working in Information Technology Industry?• □less than 1 year □1‐3 years □4‐6 years □over 6 years•• 9‐. How long have you been working with this organization?• □less than 1 year □1‐3 years □4‐6 years □over 6 years• 10‐ Your Position at the company is permanent of contractual• □Permanent □Contractual• ☺☺☺ THANK YOU ☺☺☺•


Lecture No :28(Sample Research Project Using SPSS – Part ‐B)

Recap

• Develop a research project from the start– Problem definition– Importance of research– Gap– Research objective/ questions – Introduction and Literature review– Theoretical framework– Methodology

• Apply SPSS for Data Analysis• Descriptive and Reliability

Objectives

• Analysis using SPSS– Descriptive – Reliability (Cron Bach Alpha)– Validity ( Factor Analysis)– Correlations– Regression– Interpretations

Schematic Diagram

Subjective Norm for




knowledge

position Nature


Instrument•• Attitude Towards PROJECT Knowledge Sharing [Adapted from Bock et al(2005)]•• To me, sharing PROJECT knowledge with my co‐workers is harmful…………..•• To me, sharing PROJECT knowledge with my co‐workers is good……………...•• To me, sharing PROJECT knowledge with my co‐workers is pleasant………….•• To me, sharing PROJECT knowledge with my co‐workers is worthless………...•• To me, sharing PROJECT knowledge with my co‐workers is wise………………•• Affect of Subjective Norm Towards PROJECT Knowledge sharing [Adapted from Bock et al(2005)]• My CEO/Head of organization thinks I should share PROJECT knowledge with my coworkers …………………………………………………………………•• My Boss thinks I should share PROJECT knowledge with my co‐worker ………•• My colleagues thinks I should share PROJECT knowledge with my co‐workers………………………………………………………………………….•• Generally Speaking, I accept and carry out my CEO’s policy and intentions •• Generally Speaking, I accept and carry out my Boss decision even though it is different form mine ....................................…………………….•• Generally Speaking, I respect and put in practice my colleagues decisions •

•

• Intentions to Share PROJECT Knowledge [Adapted from Bock et al (2005)]• If given opportunity, I would share PROJECT knowledge with my co‐workers…• If given opportunity, I would share my work experience with my co‐workers…………………………………………………………………………..• If given opportunity, I would share know‐how or ticks of the trade • with my co‐workers…………………………………………………………….• If given opportunity, I would share expertise from education Or training with my co‐

workers……………………………………………..• If given opportunity, I would share know‐why knowledge from work with my

coworkers…………………………………………………………………...

• Demographic: Please provide some personal Information• 1. Your gender: □Male □ Female 2. Your age? ____ (in years) •• 3. Your level of your education? □FA/FSc □Diploma □Bachelor □Masters □PhD•• 4‐Nature of your Job : □Software Development/Support □ Networking □Other( Specify)____________ •• 5‐ Your Name: ______________________(* optional)•• 6‐ Your Organization:__________________(*optional)•• 7‐ Your e‐mail : ____________________ ( Interested in receiving the results of this study) □Yes□ No •• 8‐ How long have you been working in Information Technology Industry?• □less than 1 year □1‐3 years □4‐6 years □over 6 years•• 9‐. How long have you been working with this organization?• □less than 1 year □1‐3 years □4‐6 years □over 6 years• 10‐ Your Position at the company is permanent of contractual• □Permanent □Contractual• ☺☺☺ THANK YOU ☺☺☺•

Reliability

Reliability StatisticsCronbach's Alpha N of Items.734 5

Validity (Factor Analysis)

Correlation


Lecture No :29(Sample Research Project Using SPSS – Part ‐C)

Recap

• Develop a research project from the start– Problem definition– Importance of research– Gap– Research objective/ questions – Introduction and Literature review– Theoretical framework–Methodology

• Apply SPSS for Data Analysis• Descriptive and Reliability

Recap

• Analysis using SPSS– Descriptive – Reliability (Cron Bach Alpha)– Validity ( Factor Analysis)– Correlations– Regression– Interpretations

Objectives

• Moderation • Mediation• Group difference (Independent Sample t Test)

Moderation

• Scenario• Anxiety level effects the depression level of the individuals. But the relationship is moderated by Anxiety Free days.

• To test the moderation we need to have develop hierarchical regression equations and see if there is any change in the R‐square. If the change is significant then we claim there is moderation

• Step 1 : Create a new interaction variable Anxiety level * Anxiety Free days

• Step 2: Due to the multiplication of variables there is a possibility of number of problems such as multi co linearity.– This can be avoided by creating the Z scores of the variables

– The raw mean score is subtracted from the mean and divided by the std

– So while running the regression then the Z scores are used instead of the raw score.

• Step 3: SPSS (converting into Z scores)» Analyze» Descriptive Statistics» Select the variables» Save standardized values

• Step 4: SPSS (computing the Interaction variable with z scores)

» Transform» Compute» New variable (Interaction) » Target Z score variables (*)» okay

• Step 5: Regression Analysis» Linear» Dependent Variable (Depression)» Independent Variables (Anxiety , Anxiety Free Days)» Next» Block 2/2» Enter the Interaction variable» Statistic tick Change in R‐square» Okay

– Results» Change in R‐square and F statistics and F significance

Schematic Diagram

Subjective Norm for




knowledge

position Nature


MediationBarron & Kenny method

HC↑ c PF↑ c=PF=f(HC)=0.374,P=0.003

Hc PF a=cc= f(HC)b a b=Pf=f(cc)cc

ć=f(HC,CC)=0.12388,P=0.2053If ć becomes zero then full mediation exists other wise

it will be partial mediation.

Preacher & Hayes method

Generates confidence interval between the 2 scenarios.If the confidence interval include zero it indicates a lackof significance.

If zero is not included then mediation is significant.If there exists zero between upper and lower valuesthen there is no mediation.

Group Difference

• Independent Sample T test ( Intentions and Gender)

• Anova ( intentions and education levels)

Miscellaneous features (SPSS)

• Normal Distribution • Scatter Plots• Missing Values


Lecture No :30(Research Output Discussions and Report Format)

Objectives

• Findings and Discussion section of the research• Research Report Layout

• Two research articles and their findings would be discussed.

• These article have already been partially covered

• Now the focus is on the Results/Findings section, conclusion and recommendation sections.

Research Report Layout

• Title• Introduction• A brief literature review• Research Questions• Theoretical Framework• Hypothesis• Method section

– Study Design (cross sectional , …)– Population and Sample

– Variables and measures– Their reliability and Validity

• Data Collection

• Data Analysis • Discussion of Results• Recommendations


Lecture No :31(Revision Chapter 1,2,3,4,5,6,7)

Introduction

•Overview of the course :

•Business research is an organized and deliberate process through which organization effectively learn new knowledge and help improve performance.

Introduction

• Objectives of the course :• To understand and develop a systematic approach to business research

• To emphasis on the relationship between theory , research and practice

• To Integrate different research activities in an orderly fashion

• Outcomes of the course are :1. To formulate research questions2. Develop theoretical framework3. Develop hypotheses4. Learn to select from different research

methodologies5. Develop skills for data analysis and

interpretation.

• Research is a – Systematic effort to investigate a problem

• Types of research– Applied (solve a current problem of org) – Basic (improve understanding of a problem)

– Research Philosophical Choice– Deduction / Induction

• Why managers should know about research– Identify problems , discriminate b/w good and bad research, appreciate the multiple influences of different factors ,etc.

• Hall Marks of Scientific Research.– Purposive, Rigor, Testability, Reliability, Precision/confidence, Objectivity, Generalizbility, Parsimony

• Building Blocks of Scientific Research– Observation, identification of problem area, Theoretical Framework, Hypothesis, Construct, Concepts operations definitions, Research Design, Data Collection , Analysis, Interpretation, implementation/refinement of theory

Problem/Literature/Question• Identification of the broad problem area

– Preliminary information gathering through interviews and literature survey

– Problem definition• Literature Review involves searching and documenting– There is a structure of review (importance, objectives, definitions, relationships identified, gaps)

– There are different formats of Documenting (APA)• Based on the gaps identify your research objectives/problem definition/research questions

Theoretical Framework and Variables


• Variables are of 4 different kinds– Independent, Dependent, Moderating, Mediating( Intervening)

Hypotheses

• In order statistically respond to the research questions we develop the Hypotheses statements.

• These statements are stated in such way that they can be easily testable


• We develop null and alternate hypotheses

Summarized Table of Statistical Notations for Hypotheses

Relationship Group Difference

Ho: Ha: Ho: Ha:

Directional ρ=0ρ>0ORρ<0

µa=µbµa>µbORµa<µb

Non‐Directional ρ=0 ρ#0 µa=µb µa # µb

Research Design


– (exploratory, descriptive, hypothesis testing)• Type of investigation

– (causal, correlations)• Extent of researcher's interference

– (High, moderate, low)

The Research Design

Types of Investigation

Establishing:‐Casual relationship‐ Correlation's‐ Groupdifferenceranks, etc.

Purpose of the study

ExploratoryDescriptionHypothesesTesting

Extent of Researcherinterference

Minimal: studyingevents as theynormally occurManipulation

Study setting

contrived

non‐contrived

1. Feel fordata

2.Goofiness of data

3. HypothesisTesting

Units of analysis(population to be

studied)

individualsdyadsgroups

organizations\machines

etc

Samplingdesign

Probability/Non‐probabilitySample size (n)

Time horizon

one‐shot(cross‐sectional)

Longitudinal

Data collectionmethod

ObservationInterview

QuestionnairePhysical

measurementUn‐obstructive

Measurement& Measures

Operational Definitionscaling categorizingcoding

Opertionalization





14

Research Question/Itemsfor the five Dimensions

16

Scales

• Measurement means that scales are used.


• Nominal , Ordinal, Interval, Ratio

17

Goodness of Data

• Four types of scales are used in research, each with specific applications and properties. The scales are

• Nominal• Ordinal• Interval• Ratio

18


Lecture No :32(Revision Chapters 8,9,10,11,SPSS)

Data Collection



2

Questionnaire Design

• Questionnaires• Personally Administered Questionnaires• Mail questionnaires• Guide line for wordings



Sampling



• Sampling process include defining population,sample frame, sampling design, sample size andsampling process

Sampling

• Simple random sampling and restricted sampling aretwo basic types of probability sampling.

• Probability: Probability of selection is known

• Non Probability : Probability is not known

Sampling

• Precision we estimate the population parameterto fall within a range, based on sample estimate.

• Confidence is the certainty that our estimate willhold true for the population.

• Sample size.• Some sampling designs are more efficient thanthe others.

• The knowledge about sampling is used fordifferent managerial implications.

Types of Sampling Designs

Sampling Designs

Non‐probability Probability

Convenience Judgmental Quota Snowball

Systematic Stratified Cluster Other SamplingTechniques

Simple Random

Experimental Design

• Causal vs Correlations• Experimental Design• Field Experiment vs Lab Experiments

Experimental Design

Experimental Designs

•When to use experimental designs

•Pretest and Posttest Experimental Group Design

•Posttests only with Experimental and Control Group

•Pretest and Posttest Experimental and Control Group Design

•Solomon Four Group Design

(Data Analysis)Getting Data Ready

• Questionnaire checking involves eliminating unacceptablequestionnaires.

• These questionnaires may be incomplete, instructions notfollowed, missing pages, past cutoff date or respondent notqualified.

• Editing looks to correct illegible, incomplete, inconsistent andambiguous answers.

• Coding typically assigns alpha or numeric codes to answersthat do not already have them so that statistical techniquescan be applied.

• Cleaning data for consistencies. Inconsistencies mayarise from faulty logic, out of range or extremevalues.

• Statistical adjustments applies to data that requiresweighting and scale transformations.

(DATA ANALYSIS)Hypotheses Testing using SPSS

• How to use SPSS for Data entry– Defining variables– Assigning them values– Assigning sizes and constraints– Data entry using data from coded Questionnaires– Serial number the questionnaire


SPSS

• Frequency refers to number of times various subcategorizes occur in the same pattern.

• Frequencies can also be visually displayed as barcharts, histograms, or pie charts.

• Histogram is graphical bar chart.• The stem‐and‐leaf presents actual data values thatcan be inspected directly.

SPSS

• A boxplot reduces the detail of the stem‐and‐leafdisplay.

• Cross‐tabulation is a technique for comparing datafrom two or more categorical variables.

• Percentages serve two purposes in datapresentation.

SPSS

• Three measures of central tendency: mean, medianand mode.

• Dispersion is the variability.• Three measures of dispersion are: range, varianceand standard deviation.

• Correlation• Goodness of data is measured by reliability andvalidity.

• SPSS Cronbach Alpha (Reliability) Factor Analysis(Validity)

SPSS‐Hypotheses Testing

• Hypothesis testing(Difference between groups, Relationship)

• Null and Alternate Hypotheses• Choose appropriate test • Significance• Group difference

– Testing single mean– Testing two related means (ratio)– Testing two related samples when data is in ordinal / nominal– Testing two in unrelated means– Testing when more than two groups on their mean scores

Hypothesis Testing

• Hypothesis testing the relationship/Association• Correlations• Regression

Research Proposal

• The purpose of the study• The specific problem to be investigated/problem statement/ Research Questions

• The scope of the study/what is covered and not covered.

• The relevance of the study/importance• The research design offering detail on

– Sampling, data collection methods, data analysis, • Time frame• The budget

Research Report Layout

• Title• Introduction• A literature review• Research Questions• Theoretical Framework• Hypothesis• Method section

– Study Design (cross sectional , …)– Population and Sample

– Variables and measures– Their reliability and Validity

• Data Collection• Data Analysis • Discussion of Results• Recommendations

Final Words

• You have by now developed basic research skills not necessarily become an expert.

• But as a managers / students should be able differentiate between good or bad research.

• You also by now recognized research is an integral part of the organization reality and can improve the organization.

• Should be able to appreciate that for any problem a scientific way to address is by identifying its factors and collecting data systematically on it so that the results would be have some credibility in the industry and academia as well

• By now you should be able to logically conceptualize the relationships among variables

• And able to carry out a small research project

• With this WISH YOU BEST OF LUCK

mgt project manage

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