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QUEENS COLLEGE BUSINESS RESEARCH METHODS.pptx

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Queens’ College
WELCOME TO:
BUSINESS RESEARCH METHODS &
TECHNIQUES
Credit hours: 2
Course Chapters
1. Chapter One: Research Introduction
2. Chapter Two-Research proposal and Research
Process
3. Chapter Three-Sampling and Sample Design
4. Chapter Four-Data Collection
5. Chapter Five-Data Analysis Methods
6. Chapter Six-What statistical analysis should We use?
CHAPTER ONE:
RESEARCH INTRODUCTION
Literally, Research (Re-Search) means “To
Search Again”
 Research is a structured inquiry that utilizes
acceptable scientific methodology to solve
problems and creates new knowledge that is
generally applicable.
 Research is a process for collecting, analyzing
and interpreting information to answer
questions.
 Is the application of the scientific method in
searching for the truth about a particular
1.1 Definitions of Research
Research is:
 Refers to the systematic and rational method
consisting of identifying the problem,
formulating the hypothesis, collecting relevant
data, analyzing the data, and reaching certain
conclusions either in the form of solutions
towards the concerned problem or in certain
generalizations for some theoretical
formulation.
Research is a movement from the unknown to
the known
Research is:
 Hence, Business Research is designed to
facilitate the managerial-decision making
process for all aspects of the business such as
finance, marketing, HRM, production,
performance.
Scientific Method-the way researchers go
about using knowledge and evidence to reach
objective conclusions about the real world.
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QUEENS COLLEGE BUSINESS RESEARCH METHODS.pptx

  • 1. Queens’ College WELCOME TO: BUSINESS RESEARCH METHODS & TECHNIQUES Credit hours: 2
  • 2. Course Chapters 1. Chapter One: Research Introduction 2. Chapter Two-Research proposal and Research Process 3. Chapter Three-Sampling and Sample Design 4. Chapter Four-Data Collection 5. Chapter Five-Data Analysis Methods 6. Chapter Six-What statistical analysis should We use?
  • 4. Literally, Research (Re-Search) means “To Search Again”  Research is a structured inquiry that utilizes acceptable scientific methodology to solve problems and creates new knowledge that is generally applicable.  Research is a process for collecting, analyzing and interpreting information to answer questions.  Is the application of the scientific method in searching for the truth about a particular 1.1 Definitions of Research
  • 5. Research is:  Refers to the systematic and rational method consisting of identifying the problem, formulating the hypothesis, collecting relevant data, analyzing the data, and reaching certain conclusions either in the form of solutions towards the concerned problem or in certain generalizations for some theoretical formulation. Research is a movement from the unknown to the known
  • 6. Research is:  Hence, Business Research is designed to facilitate the managerial-decision making process for all aspects of the business such as finance, marketing, HRM, production, performance. Scientific Method-the way researchers go about using knowledge and evidence to reach objective conclusions about the real world.
  • 7. Why Research?  To discover/seek answers to questions through the applications of scientific procedures  To gain familiarity with a phenomenon or to achieve new insights into it (exploratory research studies)  To describe accurately the characteristics of a particular individual, situation or a group (descriptive research);  To determine the frequency with which something occurs (diagnostic research);  To test a hypothesis of a causal relationship between variables (Explanatory). 1.2. OBJECTIVES OF RESEARCH
  • 8. What makes people to undertake research? 1. Desire to get a research degree along with its consequential benefits; 2. Desire to face the challenge in solving the unsolved problems, 3. Desire to get intellectual joy of doing some creative work; 4. Desire to serve the society; 5. Desire to get respectability. 1.3. MOTIVATION IN RESEARCH
  • 9. Research methods refer to all those methods/techniques that are used for conduction of research -research methods can be put into the following three groups: 1. Methods which are concerned with the collection of data.; 2. Statistical techniques which are used for establishing relationships between the data 3. Methods which are used to evaluate the accuracy of the results obtained. 1.4. Research methods vs Research methodology
  • 10. Research methodology  Research methodology is a way to systematically solve the research problem.  A science of studying how research is done scientifically.  The strategy one chooses to answer research questions  It is the various steps that are generally adopted by a researcher in studying his research problem Research method is a part of Research Conti…
  • 11. 1 Business Research may be classified into two based on its Purpose A. Applied Research: Is also called Action Research  It is a research conducted to address a specific business decisions/problem for a specific firm or organization.  A research undertaken to answer questions about specific problems or to make decisions about a particular courses of actions or policies.  Is aimed at certain conclusions (say a solution) facing a concrete business problem.  Focused on immediate solutions B. Basic or Pure or Fundamental Research  A type of research conducted without a specific decision in mind that usually does not address the needs of a specific organization.  It attempts to expand the limits of knowledge in general and is not aimed at solving a particular pragmatic problem.  Focused on formulating knowledge (theory) Types of Research
  • 12. 2. Business Research may be classified into Three on the basis of Specific Objective of the Research A. Descriptive Research  The major purpose of descriptive research is to describes characteristics of objects, people, groups, organizations, or environment.  Tries to paint a picture of a given situation by addressing who, what, when, where, and how questions like the current economic and employment situation of a country, survey on consumer decisions (demand and supply situations)  Description of the state of affairs as it exists at present.  Describes what is happened or what is happening  Its goal is to describe the current status of a given phenomenon. B. Explanatory Research  Is also called causal research.  Seeks to identify cause-and-effect relationships like how will implementing a new employee training program change job performance? Types of Research
  • 13. C. Exploratory Research  Is conducted to clarity ambiguous situations or discover ideas that may be potential business opportunities  is not intended to provide conclusive evidence from which to determine a particular course of action  Is conducted because a problem has not been clearly defined  Is conducted when there are few or no earlier studies to which references can be made for information  Its purpose is to gain background information and better understand and clarify a problem  Can give some indication as to the why, how, and when something occurs.  The results of exploratory research are not usually useful for decision-making by themselves, but they can provide significant insight into a given situation.  In business exploratory research is particularly useful in “new product development” Types of Research
  • 14. 3. Business Research may be classified into Two based on “Approaches” of research A. Quantitative Research  Quantitative research generates statistics through the use of large-scale survey research, using methods such as questionnaires or structured interviews.  is based on the measurement of quantity or amount.  It is applicable to phenomena that can be expressed in terms of quantity. Quantitative research helps: 1. Precise measurement 2. Knowing trends or changes overtime 3. Comparing trends or individual units
  • 15. 2. Qualitative Research  Qualitative research explores attitudes, behaviour and experiences through such methods as interviews or focus groups.  There is typically a high level of researcher involvement with subjects; strategies of participant observation and in-depth, unstructured interviews are often used.  The data produced provide a description, usually narrative, of people living through events in situations.  This type of research aims at discovering the underlying motives and desires,  Attitude or opinion research i.e., research designed to find out how people feel or what they think about a particular subject or institution.
  • 16. Quantitative versus Qualitative Quantitative Research Strategy  Investigation aims to assess a pre- stated theory (Deductive Reasoning)  Often involves hypothesis testing  Attempts to minimise the influence of the researcher on the outcome  Quantitative data infers statistics  Data collection therefore requires ‘closed’ responses Qualitative Research Strategy  Investigation aims to create a novel theory (Inductive Reasoning)  Researcher becomes an inherent part of the study  Qualitative data infers complex statements or opinions  Data collection therefore permits ‘open’ responses
  • 19. Research proposal and Research Process Chapter Two
  • 20. What is a research proposal?  The research proposal is a systematic plan, which brings to focus the preliminary planning that will be needed to accomplish the purpose of the proposed study.  The research proposal is the detailed plan of study  Is a document which sets out your ideas in an easily accessible way.  The objective in writing a proposal is to describe what you will do, why it should be done, how you will do it and what you expect will result.  The written proposal forces the students/researchers to clarify their thoughts and to think about all aspects of the study. The Research Proposal
  • 21. A well-thought out and well-written proposal can be judged according to three main criteria.  Is it adequate to answer the research question(s), and achieve the study objective?  Is it feasible in the particular set-up for the study?  Does it provide enough detail that can allow another investigator to do the study and arrive at comparable results? Importance of Research Proposal:  It serves as a basis for determining the feasibility of the project.  It gives the research supervisor a basis for guiding the researcher while conducting the study.  It reduces the probability of costly mistakes. The Research Proposal
  • 22. • What do you want to do? – research question • Why do you want to do it? – Any information gap • Why is it important? – any practical importance or knowledge advancement • Who has done similar work? - background • How are you going to do it? -methodology • How long will it take? – plan of work What a proposal should contain? It is based on your clear research question.
  • 23. Components of the Research Proposal The basic components of a proposal are described in the order in which they most logically appear in a proposal. 1. Preliminary Part/Section o Title Page o Abstract o Table of Contents o List of Abbreviations o List of Figures and Tables 2. Introduction o Background of the Study o Statement of the Problem o Research Objectives o Research Questions o Significance of the Study o Scope and Delimitations of the Study o Organization of the Paper 3. Review of Related Literature o Conceptual Definitions o Empirical Findings o Conceptual Framework and Conclusion The Research Proposal…
  • 24. 4. Research Design and Methods o Research Design o Sampling Techniques and Procedure o Sources of Data and Collection Methods o Operational Definition of Variables o Method of Data Analysis 5. Bibliography/References 6. Work Plan 7. Budget Schedule 8. Appendices/Annexes The Research Proposal…
  • 25. 1. The Title  It should give sufficient information about the nature of study  The title should not be too lengthy. It should be specific to the area of study.  The title should not be burdened by pompous words and the language in the title should be professional  The title of your research proposal should state your topic exactly in the smallest possible number of words.  All words in the title should be chosen with great care, and association with one another must be carefully managed.  Put your name, the name of your department/faculty/college, the name of your advisor(s) and date of delivery under the title. 2. Abstract: is a one page brief summary of the proposal. Components of the Research Proposal…
  • 26. 2. Introduction Background of the Study The background of the research proposal should address the following points:  Sufficient background information to allow the reader to understand the context and significance of the question you are trying to address  Proper acknowledgement of the previous work on which you are building  The introduction should be focused on the research question(s).  Explain the scope of your work, what will and will not be included.  It gives a background information about the issue under study Components of the Research Proposal…
  • 27. 2. Introduction Statement of the Problem  Concerns the reason why you are going to conduct a research on this issue  Effective problem statements answer the question “Why does this research need to be conducted.”  The purpose of the problem statement is to identify the issue that is a concern and focus it in a way that allows it to be studied in a systematic way.  Identifying the research gap (Time, Methodological, Conceptual) Objectives and Research Questions  Consists clear research goal (what to attain if the problem is solved)  Develops key research questions that show the major building blocks of the problem statement that need answer in your research  General and Specific (tells one thing at a time) Components of the Research Proposal…
  • 28. Delimitations of the Study/Scope of the study  Delimiting research is giving full disclosure of what the researcher intends to do or does not intend to do.  When a researcher is able to set the scope of the study, one can make the research manageable. At the same time, this can lead to the choice of research method to employ. Significance of the study  The justification for the need of the research.  The benefit you get as a researcher  How the results of the study will be useful to Components of the Research Proposal…
  • 29. Review of Related Literature  Discusses the theoretical and empirical framework  provides information about what was done, how it was done, and what results were gathered.  Defines the technical terms and phrases that operationalize the concepts and that have special meanings.  Summary of the literature that contains the major issues that you will adapt to your research or that you will use in your research. Components of the Research Proposal…
  • 30. Researchers ask the following questions before any research  what types of research has been done in the area?  What has been found in previous studies?  What suggestions do other researchers make for further study?  What has not been investigated?  How can the proposed study add to our knowledge of the area?  What research methods were used in previous studies? Answers to these questions will usually help develop Components of the Research Proposal…
  • 31. Research Design and Methods 1. A Research Design:-  Is a master plan that specifies the methods and procedures for collecting and analyzing the needed information.  It constitutes:- where will the study be carried out? What type of data is required? Where can the required data be found? What techniques of data collection will be used? How will the data be analyzed? 2. Sampling Techniques and Procedures:-  A sample design is a definite plan for obtaining a sample from a given population.  Probability Vs Non-probability Sampling Techniques  Steps contain Clearly define the population, List down the sample frame, Determine the optimum “sample Size”, Decide the sampling techniques/procedures. Components of the Research Proposal…
  • 32. 3. Sources of Data and Collection Methods:-  Is the process of gathering or collecting data from sample units  Sources od data may be “Primary Vs Secondary”  Data Collection Methods: Questionnaire, Interview, Direct Observation, Focus Group Discussion 4. Methods of Data Analysis:  After the data have been collected, the researcher turn to the task of editing, coding, and analyzing the data.  Is the application of reasoning to understand the data that have been gathered Components of the Research Proposal…
  • 33. Bibliography/References: You should include a short list of references used in the proposal. Work Plan-Time Schedule Budget Schedule/financial breakdown/ Components of the Research Proposal…
  • 35. Assignment -1 1. Prepare a Research Proposal
  • 36. 2.4. Research Process  Research process consists of series of actions or steps necessary to effectively carry out research A brief description of these steps is as follows: 1. Formulating the Research Problem 2. Extensive Literature Review 3. Developing Working Hypothesis 4. Preparing the Research Design 5. Determining Sampling Design 6. Collecting the data 7. Execution of the project 8. Analysis of Data 9. Hypothesis Testing 10. Generalization and Interpretation 11. Preparation of the Research Report
  • 37. 1. Formulating the Research Problem The first and most important step in the research process. It means defining the problem precisely It is like determination of the destination before undertaking a journey. A problem defined is half solved- Formulation of problem is often more essential than its solution  It refers to some difficulty which a researcher experiences in the context of either a theoretical or practical situation and wants to obtain a solution for the same There are two types of research problems: A. Those which relate to states of nature and; B. Those which relate to relationships between
  • 38. Conti… Formulation of a problem involves the following steps: a) Statement of the problem in a general way b) Understanding the nature of the problem c) Surveying the available literature d) Developing the idea through discussion e) Rephrasing the research problem into a working proposition. NB- it should answers “ why you are conducting research on the given topic”
  • 39. Criteria for selecting a problem Internal • researcher’s interest • researcher’s competence • researcher’s own resources i.e., finance, time, etc. External • Researchability i.e., (problems having solutions) • Importance, urgency, usefulness and social relevance, i.e., relative importance and significance of problem visa -a -vis utility of expected findings • Novelty or originality • Feasibility •Availability of data •Suitable methodology •Cooperation of organizations and individuals •Available time • Facilities /infrastructure • Reason validity 39 8/9/2023
  • 40. Some important sources for selecting a problem: • Professional Experience, • Contact and Discussion with People, • Inference from theory, • Professional Literature, and • Technological and Social Changes. 40 8/9/2023
  • 41. Evaluation of the Problem • Before the final decision is passed on the investigation of the problem, the feasibility of the problem has to be tested in terms of personal suitability of the researcher and social value of the problem. • In short, the research problem should be evaluated in terms of the following criteria: •Is the problem researchable? •Is the problem new? •Is the problem significant? •Is the problem feasible for the particular researcher? 41 8/9/2023
  • 42. A research problem is explained in the form of: • Objective of the study • Basic Research questions • Hypothesis 42 8/9/2023
  • 43. 2. Extensive Literature Review Once the problem is formulated, a brief summery of it should be written down.  Literature Review gives an overview of the problem  Provide indication of why the problem is worth considering  Explain what contribution the study will make  Cite one or more studies that are directly relevant to the proposed study or lead to the theoretical justification.
  • 44. Literature review:  It is a body of text that aims to review the critical points of current knowledge including substantive findings as well as theoretical and methodological contributions to a particular topic. Reasons for Reviewing Literature a) Bringing clarity and focus to the research problem b) Improving the methodology c) Broadening the researcher knowledge in the research area. d). Contextualize your findings. Reviewing a literature is a continuous process. 44 8/9/2023
  • 45. Four main reasons of reviewing the Literature 45 8/9/2023
  • 46. Conti… Procedures in reviewing the literature There are four steps involved in conducting a literature review:  a) Search for existing literature in your area of study  b) Review the literature selected  c) Develop a theoretical framework  d) Develop a conceptual framework.  1. Distinguish theoretical framework and conceptual framework?
  • 47. Styles of Referencing  Styles of referencing differ.  Currently two referencing styles are commonly used: • the Harvard style and • the American Psychological Association (APA) style, both of which are author-date systems.  It is important to apply an adopted style strictly and consistently 47 8/9/2023
  • 48. 1. The Harvard style  Referencing in the text (in-text citation)  The Harvard style is an author-date system.  It usually uses the author’s name and year of publication to identify cited documents within the text. Referencing in the references or bibliography -AUTHOR(S) (Year) Title. Edition. Place of publication: Publisher. 48 8/9/2023
  • 49. 2) APA Style  Referencing in the text (in-text citation) The APA style is also an author-date system. It usually uses the author’s name and year of publication with punctuation to identify cited documents within the text. Referencing in the references or bibliography -AUTHOR(S),Title. Edition. Place of 49 8/9/2023
  • 50. 3. Developing Working Hypothesis  After extensive literature survey, researcher should state the working hypothesis in clear terms. It is tentative assumption made in order to draw out and test its logical or empirical consequences. Hypotheses affect the manner in which tests must be conducted in the analysis of data and indirectly the quality of data which is required for the analysis. Hypotheses should be very specific, limited, operationalisable, conceptually clear and should be related to the body of knowledge. It should also be stated in precise and clearly defined terms and also it has to be tested.
  • 51. Conti…  It indicates the type of data, the type of methods of data analysis to be used and it guides the researcher by delimiting the area of research and keep him on the right track there are two common categories of hypothesis:  1. Research Hypothesis  2. Alternate Hypothesis
  • 52. Steps involved in hypothesis testing The various steps involved in hypothesis testing are stated below: 1. Making a formal statement 2. Selecting a significance level 3. Deciding the distribution to use 4. Selecting a random sample and computing an appropriate value 5. Calculation of the probability 6. Comparing the probability
  • 53. Errors in Hypothesis Testing In hypothesis testing, there are four possible outcomes:  The hypothesis is true but our test leads to its rejection  The hypothesis is false but our test leads to acceptance  The hypothesis is true and our test leads to acceptance  The hypothesis is false and our test leads to its rejection The first two lead to an erroneous decision. The first
  • 54. Deduction & Induction A. Deductive reasoning- works from the more general to the more specific. Sometimes this is informally called a "top-down" approach. It begins with thinking up a theory about a topic of interest; then narrow to more specific hypotheses
  • 55. B. Inductive reasoning Inductive reasoning- works the other way, moving from specific observations to broader generalizations and theories. Informally, sometimes called as "bottom up" approach. It begins with specific observations and measures; then formulate some tentative hypotheses, and finally end up with developing some general theories.
  • 56. 4. Preparing the Research Design  Research Design- Decisions regarding what, where, when, how much, by what means concerning an inquiry or a research study constitute  A research design is the arrangement of conditions or the blueprint for collection, measurement and analysis of data.  Research design is a plan, structure and strategy of investigation so conceived as to obtain answers to research questions or problems
  • 57. Characteristics of a Good Design • The design that clearly stated the objective of the problem to be studied, the nature of the problem to be studied, • The design which is characterized by adjectives like flexible, appropriate, efficient, economical, etc. ; • The design which minimizes bias and maximizes the reliability of the data collected and analyzed; • The design which gives the smallest experimental error; 57
  • 58. • The design which yields relevant information and provides an opportunity for considering many aspects of a problem; • The design that assumes the availability and skills of the researcher • The design that consider the availability of time and money for the research work 58
  • 59. decisions happen to be in respect of: (i) What is the study about? (ii) Why is the study being made? (iii) Where will the study be carried out? (iv) What type of data is required? (v) Where can the required data be found? (vi) What periods of time will the study include? (vii) What will be the sample design? (viii) What techniques of data collection will be used? (ix) How will the data be analyzed?
  • 60. Research designs can be broadly categorized into three: • Exploratory • Descriptive • Experimental 60
  • 61. Exploratory Design The main purpose is to discover new ideas and insights A very flexible, open-ended process.  Require qualitative or mixed approaches 61
  • 62. Descriptive Design • Describes attitudes, perceptions, characteristics, activities and situations. • Examines who, what, when, where, why, & how questions • Most of the social researches fall under this category • Require quantitative, qualitative and mixed method approach 62
  • 63. Experimental Design • Provides evidence that a cause- and-effect relationship exists or does not exist. • Premise is that something (and independent variable) directly influences the behavior of something else (the dependent variable). • Most practical to talk about associations or impact of one variable on another. • Require quantitative or mixed approaches 63
  • 64. 5. Determining Sampling Design  A sample design is a definite plan determined before any data are actually collected for obtaining a sample from a given population.  Sample design consists about 1. Sample size and 2. Sampling techniques Sampling techniques can be either probability sampling or non-probability sampling.
  • 65. 6. Collecting the data  Some times data at hand are inadequate, and hence, it becomes necessary to collect appropriate data There are two major sources of data A. Primary Data B. Secondary Data  Primary data can be collected either through experiments or through survey.  If the researcher conducts experiment, he observes some quantitative measurements or data with examines or test the truth
  • 66. Conti… But in the case of a survey, data can be collected by any one of the following ways: a. Observation b. Interview c. Questionnaire
  • 67. 7. Execution of the project  The project should be executed in a systematic manner and in time. 8. Analysis of Data  The analysis of data requires a number of closely related operations such as: a). Establishment of categories- the application of these categories to raw data through coding, classification, tabulation, and graphing; b). Data description (measure of central tendencies, dispersion, and relation) and data interpretation c). Drawing statistical inferences.
  • 68. 9. Hypothesis Testing  After analyzing the data, test the hypothesis.  Various tests of hypotheses, such as Chi square test, t- test, F-test may be applied. 10. Generalization & Interpretation  If a hypotheses is tested, it is possible for the researcher to arrive at generalization, i.e., to build a theory.  If the researcher had no hypotheses, the findings on the basis of some theory is known as interpretation. 11. Preparation of the Research Report  Finally, the researcher has to prepare the report in appropriate formats and appropriate language.
  • 71. How and why sampling relate to business research?  The world is large and full of people  We wanted to find out things about people  Sampling is a practical way of studying people and their activities, thoughts, attitudes, abilities, relationships in relation to business through taking small bit  Note that sample must be representative of the population from
  • 72. Why we need sampling? Sampling makes possible the study of a large, heterogeneous population. Sampling is for economy.  Sampling is for speed .  Sometimes,Sampling is for accuracy. 72
  • 73. 73 Sampling: Important terms  Population: is the total set of units in which a researcher is interested; Can be finite or infinite population  Examples: All employees of an organization to study the reasons of employee turnover  Element/case: a single member of the population.  Census: includes all the elements in the population  Two conditions are appropriate for census study:  when the population is small ( for populations under 50 it is usually more sensible to collect data from the entire population)  When the variability is high (when the elements are quite different from each other) and when the size is manageable  Sampling: is the process of selecting units into a sample from a larger set of the same units (Population)
  • 74. 74 Sampling: Important terms  Sampling frame: a listing of all the elements in the population from which the sample is drawn  For example the list of employees found in personnel department to get information on employee turnover  Unit of analysis: the type of object whose characteristics the researcher wants to measure and study.  For example: If data are collected on Employees, the unit of analysis is employee.  Is the object that the hypothesis describes.  All variables in a hypothesis must be operationalized for the same unit of analysis.
  • 75. 75 Sampling: Important terms  Sampling unit: a unit or set of units considered for selection at a stage of sampling.  Sampling unit may or may not be the same as a unit of analysis. It is possible to include several units of analysis.  For example, if the researcher wants to interview senior managers in the public sector, the senior managers become the unit of analysis and the public organisations across the country become sampling unit.  Parameter: is a characteristics of the population about which researchers are interested to find out.  Example: The average income of all families in a city or the age distribution of the city’s population.
  • 76. 76 Sampling: Important terms  Statistics: characteristics of a sample and is developed from information about the member of the sample,  are used to make estimates of population parameters  Example: The mean income computed from a sample or the age distribution of that sample are statistics.  Sampling errors: the difference between population parameter and the statistical estimate.  sampling error can be expressed through the use of confidence levels and confidence intervals. Example: being 95% confident that the population
  • 77. 77 Sampling: Important terms  Sample bias: misrepresentation of the population by the sample. Caused by the flaw in the design or in the implementation of sampling procedures.  Sampling fraction: % of population selected for the sample  Sample size: the number of elements selected for the sample to represent the population.
  • 78. Determination of Sample Size Sample size determination is influenced by:  The purpose of the study,  Population size,  The risk of selecting a "bad“ sample,  Nature of the population- The degree of Heterogeneous or homogenous  Nature of study (qualitative or quantitative)  Sampling design or type of sample  The resource availability 78
  • 79. Characteristics of a Good Sampling Design • Truly representative • Having small sampling error • Economically viable • Systematic bias is controlled (in a better way) • Results can be applied to the population in general with a reasonable level of confidence • Optimum size (adequately large) • Similar to population-should have all the characteristics that are present in the population 79
  • 80. Steps in sampling Design Defining the population – target population Listing the population – sampling frame Obtaining an adequate sample size – Based on different approaches Selecting a representative sample – based on appropriate method 80
  • 81.  STRATEGIES FOR DETERMINING SAMPLE SIZE  using a census for small populations (e.g., 50 or less).  imitating a sample size of similar studies,  using published tables, and  applying formulas to calculate a sample size  For continuous values  Where n is sample size, Z is the abscissa at specific confidence level, s standard deviation of the sample and d is the precision level. Sample size 2          d Zs n
  • 82. 82 Sample Design  Sample design: the set of procedures for selecting the units from the population that are to be in the sample. Two major types of sample design  Probability/Random sampling:  Non probability/None random sampling
  • 83. 83 A. Probability/ Random sampling Four types of probability sampling I. Simple random sampling: II. Systematic random sampling III. Stratified random sampling IV. Cluster sampling I. Simple Random Sampling  Each unit in the population has equal chance of being selected.  Can be lottery method or a random number table  It requires a complete list of the study population. The researcher assigns each member of sampling frame a number before selecting
  • 84. 84 Simple random sampling  Helps to eliminate the inadvertent introduction of sample bias. Example: assume there are 150 employees (with BA degree and above) in the organization with the problem of high employee turnover. If the sample size is 35 employees. Use lottery and random number table. to select the sample elements.
  • 85. 85 Simple random sampling Procedures: 1. Identify the population: All employees with BA degree and above in the organization 2. The sampling frame: The list of employees with BA degree and above, names are sequentially numbered from 001 to 150 3. Prepare numbered balls equal to the number of the population 4. Thoroughly mix up the balls and then 5. Draw 35 balls from the 150 balls.
  • 86. 86 II. Systematic Random sampling  It requires the complete list of population  It reduces the amount of effort required to draw a sample and provides adequate results. But it does not result in a truly random sample  Applicable when the researcher wants to pick households in the sample from the population of consecutive households found along a street/road. Procedures:  Population has N units. Plan to sample n units and then  The sampling interval/skip= N/n------K  Line-up all N units and Randomly select a number between 1 and K  Select the randomly selected unit and every kth unit after that Example: the list contains 10,000 element and you want a sample of 1,000:  Sampling interval = Population size/Sample size=10  Randomly select a number between 1 and 10. Assume the first element in the sample is number 7, then the selection of elements continue as 7, 17, 27 …, 9987, 9997
  • 87. 87 III. Stratified sampling  Involves a process of stratification or segregation, followed by random/purposive/sample from each stratum. Procedures 1st: divide or classify the population into strata, or groups, on the basis of some common characteristics such as sex, race, or institutional affiliation, level of management, or income, etc. -Mutually exclusive groups: the classification should be done so that every member of the population is found in one and only one stratum. 2nd Determine the size to be taken from each stratum (proportionately or disproportionately) rd
  • 88.  It ensures homogeneity within each stratum, but heterogeneity between strata  Stratified sampling can be further classified as: A). Proportional:- take different sample from each stratum based on the proportionality of the stratum size B). Disproportional:- not consider the size of prospective respondents, but will take the same sample from each stratum Stratified sampling
  • 89. 89 IV. Cluster Sampling  It involves division of elements of a population into geographical groups-the groups are termed clusters Recommended when:  it is necessary to study a large geographical area and  It is difficult to identify the sampling frame  The geographical distribution of the members is scattered Stages in cluster sampling 1. The sampling frame is the complete list of clusters rather than individuals 2. Select a few clusters, normally using simple random sampling technique. 3. Collect data from the selected clusters either using census or by taking sample.
  • 90.  It is different from stratified sampling, because Every cluster is not sampled; where as every stratum is sampled in the case of stratified sampling.  It saves time and money  it may requires larger sample than other methods for the same level of accuracy  It may losses key information as a result of random selection and re-selection process of groups. Cluster sampling
  • 91. V. Multi-stage Sampling  This method is used in large-scale surveys. A sample of first—stage sampling units is chosen, each of the selected units is divided into second- stage units, samples of second- stage units are selected, and so on  Different methods (simple random, stratified, systematic or cluster sampling) may be used at any stage  The first-stage units may be Country → regions → Woredas → kebeles → 91
  • 92. 92 B. Non probability/ None random sampling  Four types 1. Convenient sampling 2. Purposive sampling (Expert sampling) 3. Quota sampling 4. Snow ball (referral Sampling) Non-probability sampling designs  Can work well for exploratory studies  Useful if it is not important to obtain accurate estimates of population characteristics  The units are selected at the discretion of the researcher  Cheaper and easier to carry out than probability designs  one cannot estimate parameters from sample statistics
  • 93. 93 I. Convenience sampling  Also called haphazard or Accidental sampling  It involves collecting information from members of the population who are conveniently available to provide it.  For example: collecting information from Volunteers It is get and talk approach  Criteria: The availability/ the ease of obtaining/ and willingness to respond  Convenient and Economical to sample employees in a nearby area  During election times TV channels often present man- on-the-street interviews to reflect public opinion.
  • 94. 94 II. Quota sampling  It classifies the population into group and then select a quota of individual units with defined characteristics in given the population  It is not a representative of the population  It is a type of stratified sample in which selection of cases within strata is entirely non-random.  Is called Dimensional sampling If all dimensions of the population are considered in quota sample  It can also be administered proportionally or disproportionally  Example: A researcher is interested to assess the attitudes of employees towards working condition. male are 60 percent and female are 40% in the organizations: If Sample size is 30 employees, then 18 conveniently available male and 12 female workers will be sampled
  • 95. 95 III. Purposive sampling  Is judgmental/ deliberate sampling  Identify and target individuals who are believed to be typical of the population being studied.  The researcher uses his own judgment about which respondents to choose, and  Picks only those best meet the purposes of the study. Expert sampling: involves selecting persons with known experience or expertise in an area.  With purposive sampling the sample is ‘hand picked’ for the research
  • 96. 96 IV. Snowball /referral sampling  Snowball: Individuals are discovered initially, and then each individual is used to locate others (the names & addresses) who possess similar characteristics and who, in turn, identify others.  Used when members of a population cannot be located easily by other methods and where the members of a population know each other.  Example: we may want to sample very small populations who are not easily distinguishable from the general population or who do not want to be identified, example drug users, homeless people
  • 99. 1. What are the Types of Data? 2. What are the ethical issues in Data collection? 3. What are the methods of primary data collection? 4. Questionnaire: a. Closed ended: Advantages and disadvantages b. Open ended Advantages and disadvantages c. What are the do’s and don'ts in developing questionnaires 5. Interview a. Types of interview b. Advantages and disadvantages 6. Observation a. Types of observations b. Advantages and disadvantages 7. Focus group discussion a. Moderator 8. Secondary data a. Sources b. Advantages and disadvantages Discussion Pints
  • 100.  Ethical issues: protection from harm, informed consent, right to privacy and honesty with professional colleagues  Data can be primary or secondary 1.1. The primary data:  are data collected for the first time and hence they are original  Major sources of primary data are diaries of eyewitness, tape- records, films, letters and autobiographies 1.2. The secondary data:  are those which have already been gathered by someone else and which have already been passed through the statistical process  These include historical studies based on the actual data, statistical research based on census data, newspaper reports of an event, company records, government publications 1. Data collection methods
  • 102.  The questionnaire is usually mailed, administered personally or electronically  Preparation of questionnaire can be tedious, involving several drafts and more than one pretests Questions can be asked to gather information on:  Facts: help to get objective information from respondents. Examples are gender, age, marital status, education, income, etc.  Behaviour: behaviour questions help to get information about what people do. Examples: “Have you ever attended any public lecture” 1.1.1. Questionnaire
  • 103.  Opinions: asking people what they think about specific issue or event  Attitudes: help to get information on the underlying belief of the respondent or the way people look at things.  Motives: asking people why people behave in a particular manner or hold certain opinions or attitudes.  Knowledge: It helps to obtain information about the extent of knowledge an individual has and to what extent the information is accurate. Questionnaire
  • 104. General Merits of Questionnaire 1. There is low cost even when the universe is large and is widely spread geographically. 2. It is free from the bias of the interviewer; answers are in respondents’ own words. 3. Respondents have adequate time to give well thought out answers. 4. Respondents, who are not easily approachable, can also be reached conveniently. 5. Large samples can be made use of and thus the results can be made more dependable and reliable.
  • 105. General Demerits of questionnaire: 1. Low rate of return; bias due to no-response is often indeterminate. 2. It can be used only when respondents are educated and cooperating. 3. The control over questionnaire may be lost once it is sent. 4. There is inbuilt inflexibility because of the difficulty of amending the approach once questionnaires have been dispatched. 5. There is also the possibility of ambiguous replies or omission of replies altogether to certain questions; interpretation of omissions is difficult. 6. It is difficult to know whether willing respondents are truly representative. 7. This method is likely to be the slowest of all.
  • 106. Types of Questionnaires: open-ended or closed-ended Closed ended questions allow only answers which fit into categories that have been established in advance by the researcher.  Open-ended - unrestricted type of questionnaire, free response in the respondent's own words; leave the respondent to decide the wording and the length of the answer and the kind of matters to be raised in the answer. Advantages of closed- ended questions:  The answers are standards, and can be compared from person to person.  The answers are much easier to code and analyze  They are easier for a respondent to answer as he or she merely choose a category, Conti...
  • 107. Disadvantages of closed-ended questions:  guesses or randomly answers if a respondent does not know the answer or has no opinion  There is no opportunity for the respondent to clarify or qualify his or her answer  Differences in interpretation of what was meant by the question may go undetected  Variations in answers among the different respondents may be eliminated artificially by forced- choice responses  A likelihood of a clerical error as the respondent circles Conti...
  • 108. The advantages of open-ended questions:  They can be used when not all of the possible answer categories are known and to see what the respondent views as appropriate answer categories  To allow the respondent to answer adequately, in all detail he or she likes, and to clarify and qualify his or her answer  They can be used when there are too many potential answer categories to list on the questionnaire – you can not list too many questions in a given questionnaire  They allow the respondent to have more opportunity for creativity or self-expression Conti…
  • 109. Disadvantages of open-ended questions  The possibility of collecting worthless and irrelevant information  Data are often not standardized from person to person, making comparison or statistical analysis difficult  Coding is often very difficult and subjective  require a lot of time for analysis  may require more of the respondent's time and effort, and may engender a high refusal rate /reduce respondents’ willingness to take part in the research.  possibly discouraging some respondents who do not wish to answer a lengthy questionnaire. Conti…
  • 110.  make each question brief and the wording clear and concise with minimal use of jargon  keep the length of the questionnaire to a minimum: a maximum of around 20 questions is probably a good guide for most surveys.  Make all definitions, assumptions, and qualifiers clearly understood  Avoid making significant memory demands  make the questions simple to answer  Keep it interesting – don’t make it monotonous, do have a logical sequence to the question  Avoid biased, loaded, leading, or sensitive questions. ◦ Example: ‘are you a heavy smoker?’ Instead use ranges:. Please indicate your smoking habit: less than 10 cigarettes a day, between 10 and 20, over 20 ◦ Leading: what are your views on the level of VAT in Ethiopia? Is better than ‘ do you agree that the level of VAT in Ethiopia is too Questionnaire: a list of some dos and don’ts
  • 111.  start with simple questions such as gender, leaving more complicated questions to be answered late in the questionnaire.  avoid asking personal questions  avoid asking the same questions in a different fashion ◦ Abortion should be legalized - agree / disagree) at one point and Abortion should not be legalized (agree/disagree)  Don’t ask two questions in one/ double barrelled questions- with two purposes or interpretations/ ◦ Example: Is your job interesting and well paid? Is unlikely to be answered with a simple yes or no  Don’t ask hypothetical question such as winning the National Lottery). Question; a list of some dos and don’ts
  • 112. I. Introductory statement of a Questionnaire  state the purpose of the study and its significance;  explain who the data collector is, the basis of its authority/the sponsor-under whose auspices is the research being undertaken?, and why it is conducting the study;  tell how and why the respondents were selected;  explain why their answers are important;  tell how to complete the format and list the person to call if help is needed to complete the form;  provide assurance of confidentiality and anonymity when appropriate;  explain how the data will be used;  explain who will have access to the information; and  present the response efforts as a favor and thank the respondents for their cooperation. Questionnaire: Important points to note
  • 113. II. Format of the questionnaire  Cleanly format your questionnaire: margins, spacing, font, etc.  Proofread your questionnaire carefully for grammar and spelling errors  conduct a pilot survey on a small sample so that to test the reliability and validity of your measure. Schedule vs questionnaire method  Schedule is a device consisting of a set of questions, which are asked and filled in by the enumerators who are specially trained for the purpose Questionnaire: Important…
  • 114. An interview is a process of interaction in which the interviewee gives the needed information verbally in a face-to-face situation or through telephone (koal 1996).  Involves presentation of oral-verbal stimuli and reply in terms of oral- verbal responses. 114 1.1.2. Interview
  • 115. • Answers are recorded by: – Writing the response – Tape recording or – A combination of writing and tape recording • Interview can be conducted through: – personal / face-to-face (individually or in group) or – telephone – through internet/online 115
  • 116. Purpose of interview  It is the principal means of collecting information about what an interviewee:  Knows (i.e., getting knowledge or information)  likes or dislikes (i.e., values & preferences) and  Thinks (i.e., attitudes and beliefs)  It may be used to test hypotheses or to suggest new  It could be used in conjunction with other methods in a research undertaking 116
  • 117.  Types of Interview: structured , unstructured and semi- structured A) Structured interview  Set of predetermined questions and of highly standardized recording technique (audio or digital recorders)  The same types of questions are presented in the same order to each interviewee  The interviewer has no freedom to rephrase or change the order of questions to be presented  quite often used in the case of descriptive studies Interview
  • 118. B. Unstructured interview  The interviewer has a general plan of inquiry but not a specific set of questions that must be asked in particular words and in a particular order  characterized by flexibility  The interviewer is largely free to arrange, rephrase, modify, and add some new questions  Very important for exploratory research studies C. Semi-structured interview  Shares the nature of both structured and unstructured interview Interview tech…
  • 119.  Prepare for interview, self-presentation: objective of the study, its background, how the respondent was selected, the confidential nature of the interview, the beneficial values of the research findings  Conduct the interview – use your communication skills here (attentive, non-judgmental), ask the questions properly, probe meaningfully  Recording of the interview; record the responses accurately, record response as they occur; use some shorthand system (abbreviating words, key words) Interview: Techniques of Interviewing
  • 120. Advantages:  More and in-depth information can be obtained  There is greater flexibility  Personal information can be obtained easily  high response rate  The language of the interview can be adapted to the ability the person interviewed Disadvantages  Expensive including Cost of selecting, training and supervising the field-staff  Bias of interviewer and the respondent - presence of the interviewer on the spot may over-stimulate the respondent - may give imaginary information  Important officials or executives may not be easily approachable  More-time-consuming, when calling the respondents Interview…
  • 121.  observing what is occurring in some real - life situation , without asking questions of respondents  It is valuable instrument in a wide range of research studies. ◦ Examples: Cultural study, traffic counts, direction of traffic flows Planning and execution of observation  Selecting an appropriate group of subjects to observe  Identifying the specific activities or units of behavior to be observed and focusing attention on same at the time of implementation  Proper arrangement of specific conditions for the subject(s) to be observed  Assuming the proper role or physical positions for observing  Handling well the recording instruments to be used 1.1.3. Observation Methods
  • 122. A) Direct versus Indirect observation Direct observation: the observer is physically present and personally monitors what takes place  Very flexible - the observer can react to events  The observer is free to shift places, change the focus of observation, or concentrate on unexpected events  weakness - the observers' perception may become overloaded as events move quickly; they must later try to reconstruct what they are not be able to record Classification of observation methods
  • 123. Indirect observation  The recording is done by mechanical/adjusted instruments ◦ Example: a special camera that takes one frame every second is mounted in a department of a large store to study customer and employee movement  Less flexible but much less biasing, less unpredictable or erratic in accuracy  The permanent record can be analyzed to include any different aspects of an event Observation
  • 124. B. ) Disguised (Covert) Vs undisguised (overt) observation  The role of the observers should be disguised in situations where people behave differently if they know they were being observed  Often technical means are used such as one-way mirrors, hidden cameras, or microphones  Reduce the risk of observer bias but bring up a question of ethics ◦ Hidden observation is a form of intelligence work  A modified approach - the presence of the observer is not concealed, but the observer´s real purpose and subject of interest are hidden Observation…
  • 125. c) Participant Vs non-participant observation  Participant observation: The observer becomes one of the groups under observation  Non-participant observation: Observer takes position where his presence is not disturbing the group. d) Structured Vs. unstructured observation  Structured observation is systematic and has a high level of predetermined steps  Objective: To quantify behavior (your focus is to determine how often things happen rather than why they happen. Ex: Time and motion study  Unstructured observation: The observer has no definite ideas of the particular aspects that need focus.  Observing events that are happening may also be a part of the plan as in many qualitative studies. Observation…
  • 126.  the observer must take utmost care to minimize the influence of his biases, attitudes and values on the observation report Advantages:  Useful in locating data about non-readers, young children, people with mental disorders, and laboratory animals  The data obtained through observation of events as they normally occur are generally more reliable and free from respondent bias. Disadvantages:  It is time consuming  It is costly to collect data.  The data may reflect observers’ bias Observation: Recording and interpreting the observation
  • 127.  a special type of interview that offers opportunity to interview a number of people at the same time.  Made by a panel of 8 to 12 respondents led by a trained moderator  The moderator uses group dynamics principles to focus or guide the group in an exchange of ideas, feelings, and experiences on a clearly understood topic  Good for exploratory research  Qualities of a moderator: (Kindness with firmness, Tolerance, Involvement, understanding, Encouragement, Flexibility, Sensitivity / emotional response)  Benefits of FGD : (Synergism , Snowballing, Stimulation, Security, Spontaneity—/natural behavior/, Serendipity/discovery of something fortunate, Specialization, Scientific Scrutiny, Structure, Speed) 1.1.4. Focus group discussion
  • 128. Collection of Secondary Data Secondary data means data that are already available i.e., the data which have already been collected and analyzed by someone else. 128
  • 129.  Secondary data may either be published data or unpublished data.  Usually published data are available in: various publications of the central, state or local governments; various publications of foreign governments or of international organizations; technical and trade journals. 129
  • 130. books, magazines and newspapers; reports and publications of various associations connected with business and industry, banks, stock exchanges, etc.; reports prepared by research scholars, universities, economists, etc. in different fields; and public records and statistics, historical documents, and other sources of published information. 130
  • 131. The sources of unpublished data are many; they may be found in diaries, letters, unpublished biographies and autobiographies and also may be available with scholars and research workers, trade associations, labor bureaus and other public/ private individuals and organizations. 131
  • 132.  Researcher must be very careful in using secondary data. He must see that they possess following characteristics:  Reliability of data  Suitability of data  Adequacy of data  From all this we can say that it is very risky to use the already available data. The already available data should be used by the researcher only when he finds them reliable, suitable and adequate. 132
  • 133. Strengths  Enable researchers to study past events or issues  Usually most secondary documents are readily available  It is more economical  Give an easy way of obtaining other peoples perception 133
  • 134. Limitations  Some secondary sources may be unreliable and inaccurate  Some sources could be confidential/secret/private  Some documents may not be up to date and complete  Documents may be biased to some extent since they represent the views of the authors 134
  • 137. Contents  Data Processing: Editing, Coding, Classification, Tabulation, and presentation  Employing Statistical Tools for Data Analysis  Overview of descriptive and inferential statistics  Parametric and non-parametric tests  Interpretation of Data  Utilizing Computers for Data Processing (using STATA or SPSS): An Overview 137
  • 138. Plan for processing and analysis: Quantitative data • Data Processing: Editing, Coding, Classification, Tabulation, and presentation • Level of measurements [Nominal, Ordinal, Interval and Ratio] • Employing Statistical Tools for Data Analysis  Descriptive Vs inferential statistics  Parametric and non-parametric tests • Interpretation of Data • Utilizing Computers for Data Processing (using SPSS, STATA, etc) 138
  • 140. Qualitative Data Analysis  Best used when for in-depth understanding of the intervention  Used for any non-numerical data collected: – unstructured observations – open-ended interviews – analysis of written documents – focus groups transcripts – diaries, observations  Analysis challenging  Take care for accuracy (validity concern) 140
  • 141. Computer help for qualitative data analysis  Software packages to help you organize data [example, Qualpro, Hyperqual, Anthropax, Atlas-ti, Envivo, etc]  Search, organize, categorize, and annotate textual and visual data  Help you visualize the relationships among data 141
  • 142. Data Processing:  Once the data have been collected, the next step is data processing, generally consisting of:  Editing,  Coding/recoding  Classification and  Tabulations including producing tables, graphs, coefficients etc.  Data processing requires careful attention and understandings. Else it results in what is known as GIGO: Garbage in Garbage out. 142
  • 143. Questionnaire Editing  Editing of data is a process of examining the collected raw data (especially in surveys) to detect errors and omissions and to correct these.  It involves a careful scrutiny of the completed questionnaires and /or schedules. It is done to assure that information received are complete as much as possible and have been well arranged to facilitate coding and tabulation. 143
  • 144.  Editing requires checking for the following: a. Completeness: Whether every questions has answers or not. Incomplete questions can be imputed (if possible). b. Accuracy: Check if every questions has an appropriate answer. Inaccuracy often arises out of carelessness on the part of enumerator, deliberate misleading, and ticking wrong boxes or circling wrong codes. c. Uniformity: Failure to give explicit instructions or clear understanding of the questions could lead to recording the same answer in different ways. A check on uniformity is believed to eradicate this source of error. 144
  • 145. Data Coding  Coding is the process of converting answers to numbers and classifying answers accordingly so that responses can be put into a limited number of categories or classes. .  Coding is the primary task in reduction of qualitative data.  Coding decision should usually be taken at the designing stage of the questionnaire. 145
  • 146. Six main steps in Coding and Classifying quantitative data: a. Classifying responses b. Allocating codes to each variable c. Allocating column numbers to each variables d. Producing a codebook e. Checking from coding errors f. Entering data into computer 146
  • 147. Data Entry Requirements for Data Entry 1. Definition of Data Dictionary – Giving names and explanations for each of the variables to be entered into the database. 2. Defining range : In order to regulate the magnitude of answers to be entered for each of the questions on the questionnaires, the researcher needs to limit the scope of answers and their flow patterns. 147
  • 148. Data editing and cleaning after data entry  Data editing and cleaning after data entry is tantamount to drying and ironing washed clothes before putting on.  Wrong entries either in the field or during data coding and entry need to be checked and removed before the commencement of data analysis.  Cleaning can be done by looking at patterns of the data via identification of outliers and unexpected responses through running frequencies and cross tabulating related variables. 148
  • 149. Four broad considerations of data analysis  Identification of Level of measurement of each variable  Number of variables that each of the particular pieces of analysis requires.  Types of analysis required: descriptive vs analytic  Application of ethical principles of full, fair, appropriate and challenging analysis to the selection of data to be analyzed and reported. 149
  • 150. Tabulation and data analysis  Tabulation starts with production of simple frequency and contingency tables to construction of complex and multi-dimensional tables  Tabulation is often known as a skeleton form of the survey research.  A researcher shall assume some knowledge of quantitative data analysis procedures to assume the sense of skeleton.  Even if the researcher does not have sufficient knowledge of data analysis, he/she can consult someone who has sufficient knowledge of data processing. 150
  • 155. Graphical methods of displaying data  Pie Charts Categories represented as percentages of total  Bar Graphs Heights of rectangles represent group frequencies Bars do not touch each other  Frequency Polygons Height of line represents frequency  Histogram  A histogram is a chart made of bars of different heights but interconnected.  Time Plots Represents values over time 155
  • 156. Pie Chart 33.0% 23.0% 19.0% 19.0% 6.0% Category Happy with career Don't like my job but it is on my career path Job is OK, but it is not on my career path Enjoy job, but it is not on my career path My job just pays the bills Figure 1-1: Extent of job satisfication My job just pays the bills Happy with career Enjoy job, but it is not on my career path Job OK, but it is not on my career path Do not like my job, but it is on my career path NB: Use different colors for each of the slices to distinguish between categories 156
  • 157. Bar Chart C4 1Q 4Q 3Q 2Q 1Q 1.5 1.2 0.9 0.6 0.3 0.0 Figure 1-2 2003 2004 Quartely net income for General Motors (in billions) Bar chart is advantageous to make presentations for those who are not familiar with statistical materials 157
  • 159. Relative Frequency Polygon Frequency Polygon 5 0 4 0 3 0 2 0 1 0 0 0 . 3 0 . 2 0 . 1 0 . 0 Sales It visualizes gradual shifts in frequency from one category to another 159
  • 160. O S A J J M A M F J D N O S A J J M A M F J D N O S A J J M A M F J 8 .5 7 .5 6 .5 5 .5 M o n th Milli ons o f Tons M o n th ly S te e l P r o d u c tio n Time Plot/Line Graph 160
  • 164. What are the determinants to say qualitative and/or quantitative analysis approach? Data nature Analysis techniques  if not, Mixed approach
  • 174. Measurement of Shape of Distribution: Skewness and Kurtosis  Skewness  Measure of asymmetry of a frequency distribution  Skewed to left  Symmetric or unskewed  Skewed to right  Kurtosis  Measure of flatness or peakedness of a frequency distribution  Platykurtic (relatively flat)  Mesokurtic (normal)  Leptokurtic (relatively peaked) 174
  • 178. Kurtosis Platykurtic - flat distribution 178
  • 179. Kurtosis Mesokurtic – neither too flat nor too peaked 179
  • 180. Kurtosis Leptokurtic - peaked distribution 180
  • 194. Chapter Six What statistical analysis should We use?
  • 195. 6.1. introduction 6.1.1. Levels of Measurement of Data  There are four levels of measurement: Nominal, Ordinal, Interval, and Ratio. A. Nominal Data: are categorical or qualitative data that are converted into numerical data by coding the various categories. These are numerical in name only; because the numbers assigned are more symbols and hence cannot have any numerical meaning in the real sense. There is no any mathematical difference between categories. Examples:  Sex , Ethnic group, and Marital status
  • 196. Conti… B. Ordinal Data: are nominal data, which have order and consensus. Measurements with ordinal scales are ordered in the sense that higher numbers represent higher values, i.e., they can have meaningful inequalities (< or >). In such kind of data, only counting and ranking are possible but it is not likely to find exact differences. Examples:  Military ranks, Graduates , likert scal
  • 197. Conti… C. Interval Data: are ordinal data in which the differences between units have meaning. These data do not have a’ true’ zero point and therefore it is not possible to make statements about how many times higher one score is than other. In other words, the ratios of different values are meaningless. Examples:  Number of votes in election.  Exam scores of students.  Data on shoe size of individuals.
  • 198. Conti… D. Ratio Data: are interval data, which also have true zero point. With these data, one can perform addition, subtraction, division and multiplication. Examples:  1. Income is a ratio data because zero dollars is truly “no income”  2. Measurement data like height, weight, volume and area.  N.B. Both Nominal Data and Ordinal Data are categorical data
  • 199. Type of Tests  Parametric tests are statistical tests which make certain assumptions about the parameters of the full population from which the sample is taken.  These tests normally involve data expressed in absolute numbers (interval or ratio) rather than ranks and categories (nominal or ordinal).  Such tests include analysis of variance (ANOVA), t- tests, Z-test, etc. 199
  • 200. Non parametric test • Non-parametric tests are used to test hypotheses with nominal and ordinal data. • The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences; in terms of levels of measurement, for data on an ordinal scale. • Such tests are like Chi-Square (X2), Mann- Whitney Test, kruskal wallis, etc 200
  • 201. 6.1.2. Types of Variable A. Independent Variable B. Dependent Variable C. Mediator variable
  • 202. Conti… C. Mediator variable  A mediator variable influences the strength and/or direction of the relation between the independent and dependent variables; mediators are often called intervening (Baron & Kenny, 1986). It is used to explain the causal relation between dependent and independent variables (Hair et al, 2006).
  • 203. Conti… A. Qualitative variable: variables in which the characteristic or variable being studied is non-numeric. A qualitative variable is a variable that can be described only in words.  Example: gender, color, religion, ethnic group etc. B. Quantitative variable: variables that can be expressed numerically or are variables that are numeric in nature.
  • 204. Conti… i. Discrete variables: A Variable that assumes a finite or countable number of possible values is called a discrete variable. There are finite or countable numbers of choices available with discrete data. You cannot have 2.63 people in the room. Discrete variable is usually obtained by counting. -E.g., number of children’s in a family, number of cars at a traffic light is usually obtained by counting. ii. Continuous variables: A variable that can
  • 205. Latent variable Vs observed variables I. Latent variable- are a central concept and abstract phenomena which are of hidden or unobserved and theoretical (Bowen and Guo, 2012), and typically hypothetically existing constructs of interests in a study (Raykov and Marcoulides, 2006). -Latent variables are measured indirectly by their respective
  • 206. II. observed variables -are variables that can be directly measured and are indicators of a latent variables (Wang and Wang , 2012). . Thus, the observed variables can be categorical, ordinary, and continuous, but all latent variables are continuous (Kline, 2011).  A latent variable with three indictors are considered as acceptable,  Four or more is recommendable, but, a latent variable with 5 to 7 indicators are considered as to be maximum (Hair et al., 2006). -When a latent variable has only one observed variables,
  • 207. Practical examples Customer Loyalty is a latent variable can be measured though a rapid loyalty approach. It is measured indirectly by their respective indicators (observed variables) such as Customer attraction Customer retention Customers’ advocacy Customer’s repeat purchase Customer’s bulk purchase
  • 209. 6.2. Part one: Categorical Variables/data What is categorical variable? - A variable that can be studied in providing categorized alternatives, or can be answered or described only in categories. What is categorical data?  The data (whether it is expressed numerically or in word, Discrete variables or Continuous variables) that can be offered in terms of categories.
  • 210. Likert-Type Versus Likert Scales Likert-type items  Likert-type items- as single questions that use some aspect of the original Likert response alternatives.  Likert items- are used to measure respondents' attitudes to a particular question or statement. A Likert Scales  Likert Scales on the other hand, is composed of a series of four or more Likert-type items that are combined into a single composite score/variable during the data analysis process.  Combined, the items are used to provide a quantitative measure of a character
  • 211. Conti… With likert type data we cannot use the mean as a measure of central tendency Likert-type items fall into the ordinal measurement scale For Likert-type items - mode or median for central tendency, frequencies for variability, chi-square measure of association, Kendall Tau B, and Kendall Tau C
  • 212. Conti… Likert scale data, on the other hand, are analyzed at the interval measurement scale. B/C it is created by calculating a composite score (sum or mean)  Descriptive statistics recommended for interval scale items include the ‘mean’ for central tendency and standard deviations for variability.  Additional data analysis procedures appropriate for interval scale items would include the Pearson's r, t-test, ANOVA, and
  • 213. Nature of Likert-Type Items I. Items measuring degree of acceptance -this type of Likert items are used to measure respondents' attitudes towards to accepting a particular statement. it is usually coded as follows.  1 = Strongly disagree  2 = Disagree  3 = Neutral  4 = Agree
  • 214. Conti… II. Items measuring degree of extent -Likert items are used to measure respondents' believe towards the extent of a particular question. it is usually coded as follows.  1 = very low extent  2 = low extent  3 = medium extent  4 = high extent  5 = very high extent
  • 215. I. How to analysis Categorical data? A. Univariate Variables With two outcomes- Binomial Probability Test Variables With more than two outcomes- Chi- square Goodness of fit test
  • 216. B. Bivariate-when there are two categorical variables
  • 217. i. when there are one categorical dependant variable and one predictor variable - Simple logistic regression (Simple Binary outcome logistic regression & Simple Ordered logistic regression) - Spearman’s rank correlation
  • 218. C. Multivariate- when there are one categorical dependant variable but more than one predictor variable -Multiple logistic regression- (Multiple binary outcome logistic regression & Multiple ordered logistic regression) -Multinomial (polytomous) logistic regression -Correlation- (Partial and semi-partial correlations, and Multiple correlation)
  • 220. How to analysis interval variables A. Univariate -one-sample t-test B. Bivariate i. when there are two interval variables -Paired t test (Two sample, paired) ii. Group Difference test-when there are one Continuous dependant variable and one categorical independent variable ANOVA- analysis of variance and ANCOVA-analysis of covariance iii. Causality test-(Simple linear regression) -when there are one interval dependant variable and one predictor variable
  • 221. C. Multivariate- i. Group Difference test- when there are two or more dependent continuous and independent categorical variable/s MANOVA- Multiple analysis of variance MANCOVA-Multiple analysis of covariance ii. Multiple linear regression- when there are one Continuous dependant variable and two or more predictors
  • 222. Other statistical tests i. Multivariate regression -It is a type of regression in which there are two or more dependent and two or more predictor (independent) variables It can serve to compute the coefficient of regression when you have; A. Two or more categorical dependent variables B. Two or more continuous dependent variables C. The combination of categorical and continuous dependent variables
  • 223. ii. Factor analysis Steps i. Exploratory factor analysis (EFA) or principal component analysis (PCA) ii. Correlation matrix and then merging and rejection variables iii. Confirmatory factor analysis - It is developing or building model for each latent variable, determine path coefficient and then model fit Index iii. Path analysis  To indicate the direct and indirect effects of predictor variables on the DV?  It needs one or more independent/predictor variables, one or more intermediate variables, and one or more DV. iv. Structural equation modeling (SEM): Add covariance, connect latent variables and then estimate the coefficient to indicate the direct and indirect
  • 224. Advancing Professional Construction and Program Management Worldwide Any Questions?