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Chapter 8 data collection

  2. What is Data Collection? • It is the process by which the researcher collects the information needed to answer the research problem • The task of data collection begins after a research problem has been defined and research design chalked out.
  3. In collecting the data, the researcher must decide Which data to collect? How to collect the Data? Who will collect the Data? When to collect the Data?
  4. Criteria To Select Method Resources available Credibility Analysis and reporting And the skill of the evaluator
  5. Methods of Data collection Primary Secondary
  6. Methods Of Primary Data Collection 1. Interviews 2. Surveys & Questionnaires 3. Observation 4. Focus Groups 5. Experiments
  7. 1. Interviews Personal Interview Telephone Interview
  8. Characteristics It can be Direct Or Indirect, Structured Or Unstructured, Focused Or Unfocused. Includes a notepad or recording device to record conversation. A cell phone, Laptop, Tablet, or desktop computer with an internet connection is required .
  9. Classifications • Structured Interviews : In this case, a set of predecided questions are there. • Unstructured Interviews : In this case, we don’t follow a system of pre- determined questions. • Focused Interviews : Attention is focused on the given experience of the respondent and its possible effects. • Clinical Interviews : Concerned with broad underlying feelings or motivations or with the course of an individual’s life experience. • Group Interviews : a group of 6 to 8 individuals is interviewed. • Qualitative and quantitative Interviews : divided on the basis of subject matter i.e., whether qualitative or quantitative. • Individual Interviews : Interviewer meets a single person and interviews him. • Selection Interviews : Done for selection of people for certain Jobs.
  10. Steps for an effective Interview Analyze and interpret Analyze and interpret data collected from the interviewer Conduct Conduct the Interview Select Select subject/ Key Responded Prepare Prepare interview Schedule
  11. Advantages More information at greater depth can be obtained Resistance may be overcome by a skilled interviewer Personal information can be obtained Better communication Samples can be collected effectively Questionnaire can be restructured based on the need
  12. Disadvantages It is an expensive Method Interviewer bias Respondent bias Time consuming
  13. 2. Questionnaires A Questionnaire is sent ( by post or by mail ) to the persons concerned with a request to answer the questions and return the Questionnaire. A Questionnaire consists of a number of questions printed in a definite order on a form. Questionnaire – list of questions framed, relating to the study. Ex; Business survey, Economics Survey
  14. Types Of Questions Open ended questions Close ended questions
  15. Characteristics Of Good Questionnaire Should be short and simple Follow a sequence of questions from easy to difficult one Technical terms should be avoided Should provide adequate space for answers in questionnaire Directions regarding the filling of questionnaire should be given Physical Appearance – Quality of paper, Color Sequence must be clear
  16. Questionnaire A set of printed or written questions with a choice of answers, devised for the purposes of a survey or statistical study. Example: question sheet, set of questions, survey form.
  17. Queries to be decided while designing a questionnaire  What type of information is to collected ?  What types of questions are to be formulated ?  What should be the wording of each question ?  What should be their sequence ?  What should be the layout of the questionnaire ?  How to undertake pretesting of the questionnaire?  How to finalize the QUESTIONNAIRE ?
  18. Steps involved in Questionnaire 1. Decide the information required. 2. Define the target respondents. 3. Choose the method(s) of reaching your target respondents. 4. Decide on question content. 5. Develop the question wording. 6. Put questions into a meaningful order and format. 7. Check the length of the questionnaire. 8. Pre-test the questionnaire. 9. Develop the final survey form.
  19. 1. Deciding on the information required • It should be noted that one does not start by writing questions. The first step is to decide 'what are the things one needs to know from the respondent in order to meet the survey's objectives? • These, as has been indicated in the opening chapter of this textbook, should appear in the research brief and the research proposal.
  20. 2. Define the target respondents • in designing the questionnaire, we must take into account factors such as the age, education, etc. of the target respondents.
  21. 3.Choose The Method(s) Of Reaching Target Respondents • Personal Interviews • Group Or Focus Interviews • Mailed Questionnaires • Telephone Interviews.
  22. 4. Decide on question content • Opening questions that are easy to answer and which are not perceived as being "threatening", and/or are perceived as being interesting, can greatly assist in gaining the respondent's involvement in the survey and help to establish a rapport. • Dummy" questions can disguise the purpose of the survey and/or the sponsorship of a study
  23. 5. Develop the question wording • Survey questions can be classified into three forms, i.e. closed, open-ended and open response-option questions. So far only the first of these, i.e., closed questions has been discussed. This type of questioning has several important advantages.
  24. Advantages of close ended • It provides the respondent with an easy method of indicating his answer - he does not have to think about how to articulate his answer.· It 'prompts' the respondent so that the respondent has to rely less on memory in answering a question. • Responses can be easily classified, making analysis very straightforward. • It permits the respondent to specify the answer categories most suitable for their purposes.
  25. Disadvantages when using such questions • They do not allow the respondent the opportunity to give a different response to those suggested. • They 'suggest' answers that respondents may not have considered before
  26. Open ended Question Advantages • They allow the respondent to answer in his own words, with no influence by any specific alternatives suggested by the interviewer. • They often reveal the issues which are most important to the respondent, and this may reveal findings which were not originally anticipated when the survey was initiated. • Respondents can 'qualify' their answers or emphasize the strength of their opinions.
  27. Open Ended Question Disadvantages • Respondents may find it difficult to 'articulate' their responses i.e., to properly and fully explain their attitudes or motivations. • Respondents may not give a full answer simply because they may forget to mention important points. Some respondents need prompting or reminding of the types of answer they could give. • Data collected is in the form of verbatim comments - it has to be coded and reduced to manageable categories. This can be time consuming for analysis and there are numerous opportunities for error in recording and interpreting the answers given on the part of interviewers. • Respondents will tend to answer open questions in different 'dimensions'.
  28. What features of this implement do you like? · Performance · Quality · Price · Weight · Others mentioned:
  29. 6. Putting questions into a meaningful order and format Opening questions Questions flow Question variety Closing questions
  30. 7. Check the length of the questionnaire. Keep short as possible 30- 45minutes
  31. 8.Piloting the questionnaires whether the questions as they are worded will achieve the desired results· whether the questions have been placed in the best order whether the questions are understood by all classes of respondent whether additional or specifying questions are needed or whether some questions should be eliminated whether the instructions to interviewers are adequate.
  32. 9.Develop the final survey form. All that remains to be done is the mechanical process of laying out and setting up the questionnaire in its final form. This will involve grouping and sequencing questions into an appropriate order, numbering questions, and inserting interviewer instructions.
  33. Pros of Questionnaire 1. Inexpensive : • Self administered questionnaire • Website's questionnaire • E-mail Questionnaire
  34. 2. Questionnaires are practical • Practical way to gather data • Targeted Groups • Pick and choose questions • For example, KBC Group learned just how practical surveys are. They were able to spread their quizzes, polls, and questionnaires during a three-day event. This made collecting real-time feedback almost effortlessly.
  35. 3. Quick Results • Online and mobile tools • You don’t need another agency to deliver you results • Example:Dajo Associates needed quality feedback fast. The South African consulting firm needed a way to make informed decisions quickly. An online questionnaire allowed them to collect the data they needed in the shortest time frame possible.
  36. 4. Scalability • Gathers information from a large audience • Link based Sharing option • Automated E-mail • This means that for a relatively low cost, you can target a city or a country. • Geography Boundaries are not the limitation but be aware of culture and language.
  37. 5. Comparability •Qualified data can be used to compare each other research. •Measures changes
  38. 6. Easy Analysis and visualization • Most survey- and questionnaire providers are quantitative in nature and allow easy analysis of results. • With built-in tools, it’s easy to analyze your results without a background in statistics or scientific research. • Tools like Survey Anyplace offer easy to interpret reports and visualizations, meaning that you’ll quickly be turning your data into results. These results can be put in a wide variety of charts and tables to present them to your boss, colleagues, clients or customers.
  39. 7.Questionnaires Don't have time contraints When using mail-in, online or email questionnaires, there’s no time limit and there is no one on the other end waiting for an answer. Respondents can take their time to complete the questionnaire at their own leisure. As a bonus, they will often answer more truthfully, as research has shown that having a researcher present can lead to less honest and more social desirable answers.
  40. 8. Questionnaires can cover every aspect of a topic Ask As Many Questions As You Like. 10 Questions For Online Surveys Since They Are Efficient, Cost-effective In Nature And Have An Easy Mode Of Delivery, There Is No Harm In Creating Multiple Questionnaires, Each Covering A Subtopic Of The Main Subject, That Build Upon One Another.
  41. Cons of Questionnaire 1. Dishonest answers • While there are many positives to questionnaires, dishonesty can be an issue. • Respondents may not be 100% truthful with their answers. • This can happen for a variety of reasons, including social desirability bias and attempting to protect privacy.
  42. 2. Unanswered questions Some Questions Will Be Ignored Or Left Unanswered. Mark Questions As Required Make Your Questionnaire Short And Uncomplicated
  43. 3. Differences in understanding and interpretation • Without someone to explain the questionnaire fully and ensure everyone has the same understanding, results can be subjective. • Trouble grasping the questions sometimes • This miscommunication can lead to skewed results. The best way to combat this situation is to create simple questions that are easy to answer.
  44. 4. Hard to convey feelings and emotions • A survey or questionnaire cannot fully capture emotional responses or feelings of respondents. Without administering the questionnaire face-to-face, there is no way to observe facial expression, reactions or body language. • Solution use Likert scale and rating scale
  45. 5. Accessibility issues For Users With A Visual Or Hearing Impairment, Or Other Impediments Such As Illiteracy. Always Choose A Questionnaire Platform That Has Accessibility Options Built In.
  46. 6. Questionnaire or survey fatigue Survey Response Fatigue: Frequent survey forms Survey Taking Fatigue: Too long questionnaire or irrelevant content to respondent.
  47. BASIS FOR COMPARISON QUESTIONNAIRE INTERVIEW Meaning Questionnaire implies a form consisting of a series of written or printed multiple choice questions, to be marked by the informants. Interview is a formal conversation between the interviewer and respondent wherein the two participates in the question answer session. Form Written Oral Nature Objective Subjective Questions Closed Ended Open Ended Information provided Factual Analytical Order of questions Cannot be changed, as they are written in an appropriate sequence. Can be changed as per need and preference. Cost Economical Expensive Time Informant's own time Real time Communication One to many One to one Non-response High Low Identity of respondent Unknown Known
  48. Observation Observation is a method that uses vision/eyes as its main element for collecting the data. Observation is watching behavior of persons who are under observation as it actually happens without controlling it. It includes recording information without asking any questions.
  49. For example • A researcher can use the observation method in an organization and record the behavior of the employee during working hours with his colleagues as well as with his clients. Are they comfortable with the working environment and the available resources, will make a good study for the researcher.
  50. Advantages of the Observation Method: 1. Directness: The main advantage of observation is its directness. We can collect data at the time they occur. 2. Natural environment: Data collected in an observation study describe the observed phenomena as they occur in their natural settings. 3. Longitudinal analysis: Since the observation is possible to be conducted in a natural setting, the observer can conduct his or her study over a much longer period. 4. Non-verbal behavior: Observation is decidedly superior for collecting data on nonverbal behavior than survey research, experimentation, or document study.
  51. Disadvantages of the Observation Method: 1. Lack of control: Despite the advantage as achieved from the natural environment, the observation study, however, has little control over extraneous variables that may affect the data. 2. Difficulties in quantification: Measurement in observational studies generally takes the form of observer’s un-quantified perceptions rather than the quantitative measures often used in the survey and experimental studies. 3. Smallness in sample size: Because observational studies are generally conducted in-depth, with data that are often subjective and difficult to quantify, the sample size is usually kept at a minimum. This feature tends to limit the size of the sample. 4. No opportunity to learn past: In an observational study, there is no way to know the past. It is also difficult to gather information on such topics as intentions, opinions, attitudes, or preferences.
  52. Focus Groups A focus group is a group interview of approximately six to twelve people who share similar characteristics or common interests. Focus groups are useful for gathering in-depth information on perceptions, insights, attitudes, experiences, or beliefs. Focus groups are a qualitative data collection method, meaning that the data is descriptive and cannot be measured numerically. The main methods of data collection during a focus group discussion include audio and tape recording, note-taking and participant observation
  53. Advantages of focus groups Quick and relatively easy to set up The group dynamic can provide useful information that individual data collection does not provide. Is useful in gaining insight into a topic that may be more difficult to gather through other data collection methods.
  54. Disadvantages of focus groups Susceptible to facilitator bias. The discussion can be dominated or sidetracked by a few individuals. Data analysis is time consuming and needs to be well planned in advance. Does not provide valid information at the individual level. The information is not representative of other groups.
  55. Experiments An experiment is a data collection method where a researcher change some variables and observe their effect on other variables. The variables that manipulate are referred to as independent while the variables that change as a result of manipulation are dependent variables. Experimental research can be adapted to different fields like medical research, agriculture, sociology, and psychology.
  56. Advantages of Experimental Research 1. It gives researchers a high level of control. 2. It allows researchers to utilize many variations. 3. It can lead to excellent results. 4. It can be used in different fields.
  57. Disadvantages of Experimental Research 1. It can lead to artificial situations 2. It can take a lot of time and money 3. It can be affected by errors 4. It might not be feasible in some situations
  58. SECONDARY DATA Data gathered and recorded by someone else prior to and for a purpose other than the current project Secondary data is data that has been collected for another purpose. It involves less cost, time and effort Secondary data is data that is being reused. Usually in a different context. For example: data from a book.
  59. SOURCES INTERNAL SOURCES • Internal sources of secondary data are usually for marketing application- • Sales Records • Marketing Activity • Cost Information • Distributor reports and feedback • Customer feedback
  60. EXTERNAL SOURCES • External sources of secondary data are usually for Financial application- • Journals • Books • Magazines • Newspaper • Libraries • The Internet SOURCES
  61. Advantages of Secondary Data Ease Of Access Low Cost To Acquire Clarification Of Research Question May Answer Research Question
  62. Disadvantages of Secondary Data Quality of Research Not Specific to Researcher’s Needs Incomplete Information Not Timely
  63. Basis For Comparison Primary Data Secondary Data Meaning Primary Data Refers To The Firsthand Data Gathered By The Researcher Himself. Secondary Data Means Data Collected By Someone Else Earlier. Data Real Time Data Past Data Process Very Involved Quick And Easy Source Survey, Observations, Expérimentes, Questionnaire, Personale Interview, Etc. Government Publications, Websites, Books, Journal Articles, Internal Records Etc. Cost Effectiveness Expensive Economical Collection Time Long Short Specific Always Specific To The Researcher's Needs. May Or May Not Be Specific To The Researcher's Need. Available In Crude Form Refined Form Accuracy And Reliability More Relatively Less
  64. Classification of data • Classification is the process of arranging things(either normally or notionally) in groups or classes according to their resemblances and affinities and give expressions of the unity attributes that may subsist amongst a diversity individuals”. – Conner
  65. Functions of Data: •Bulk of the data •Simplifies of the data •Facilitates comparison of characteristics •Renders the data for statistical analysis
  66. Characteristics of classification Unambiguous Stable Flexible Exhaustiveness Mutually exclusive
  67. Objectives of classification To condense the mass of data To prepare the data for tabulation To study the relationships To facilitate comparison
  68. Types of Classification Geographical (or spatial) classification Chronological classification Qualitative classification Quantitative classification Alphabetical classification
  69. Geographical (or spatial) classification • When the data classified according to geographical location or region (like states, cities, regions, zones , areas etc.) It is called a geographical classification. For example, the production of food grains in INDIA may be presented state- wise in following manner.
  70. Chronological classification • When data are observed over a period of time the type of classification is known as chronological classification ( on the basis of its time of occurrence ). Various the serious such as National income figures , annual output of wheat monthly expenditure of a house hold , daily consumptions of milk, etc. Are some examples of chronological classification . For examples we may present the figures of population (or production , sales,etc.) as follows……
  71. Qualitative classification • We may first divide the population in to males and females on the basis of the attribute ‘sex’, each of this class may be further subdivide into ‘literate’ and ‘illiterate’ on the basis of attribute ‘literacy’ further classification can be made on the basis of same other attribute ,say , employment.
  72. Quantitative classification • Quantitative classification is refers to the classification of data according to some characteristics that can be measured, such as height, weight ,income, sales profit, production,etc. For example, the student of a college may be classified according to weight as follows:
  73. Alphabetical classification •When the data are arranged according to alphabetical order, it is called alphabetical classification. For example state-wise density of population in India is depicted in an alphabetical order below;
  74. Scale of measurement • Nominal, Ordinal, Interval, and Ratio scales can be defined as the 4 measurement scales used to capture and analyze data from survey, questionnaire, and similar research instruments. • All of the scales use multiple-choice questions. • Psychologist Stanley Smith Stevens created these 4 levels of measurement in 1946. • Data • Nominal & Ordinal – Qualitative/Categorical • Interval & Ratio – Quantitative/Numerical
  75. Scale of measurement
  76. Nominal Scale • A nominal scale is the 1st level of measurement scale in which the numbers serve as “tags” or “labels” to classify or identify the objects. A nominal scale usually deals with the non-numeric variables or the numbers that do not have any value
  77. Example: • An example of a nominal scale measurement is given below: • What is your gender? • M- Male • F- Female • Here, the variables are used as tags, and the answer to this question should be either M or F. • Male may be assigned a number 1, female may be assigned a number 2. The assignment of number is only for the purpose of identification.
  78. Ordinal Scale • The ordinal scale is the 2nd level of measurement that reports the ordering and ranking of data without establishing the degree of variation between them. Ordinal represents the “order.” Ordinal data is known as qualitative data or categorical data. It can be grouped, named and also ranked.
  79. Example: • Ratings in restaurants • Rank the following attributes, while choosing a restaurant for dinner. The most important attribute may be ranked one, the next important may be assigned a rank of 2 and so on. Attribute Rank Food quality Price Menu variety Ambience
  80. Scale • Volume of production • Interval Scale • The interval scale is the 3rd level of measurement scale. It is defined as a quantitative measurement scale in which the difference between the two variables is meaningful. In other words, the variables are measured in an exact manner, not as in a relative way in which the presence of zero is arbitrary.
  81. Example: Likert Scale • How do you rate the work environment of your organization VERY GOOD GOOD NEUTRAL BAD VERY BAD 5 4 3 2 1
  82. Ratio Scale • The ratio scale is the 4th level of measurement scale, which is quantitative. • It is a type of variable measurement scale. • It allows researchers to compare the differences or intervals. • The ratio scale has a unique feature. It possesses the character of the origin or zero points.
  83. Example: • What is your weight in Kgs? • Less than 55 kgs • 55 – 75 kgs • 76 – 85 kgs • 86 – 95 kgs • More than 95 kgs
  84. SCALE CHARACTERISTICS EXAMPLE PERMISSIBLE STATISTICS Nominal Numbers are used to label and classify objects Players of Team, Caste, Religion, Gender, Martial Status, Brands, Types, etc., Percentages', Mode, Chi-Square test, Contingency coefficient, Binominal test Ordinal Numbers indicate the relative position of the objects Preference ranking, Image ranking, Social class etc., Percentile, Quartiles, Median, Rank order correlation, Friedman, ANOVA Interval Numbers indicate the relative position of the objects Attitude, opinion, index number Product moment, correlation coefficient test, Z-test, ANOVA, Regression analysis, Factor analysis Ratio Numbers indicate the relative position of the objects Age, Income, market share, Sales, cost etc., Geometric Mean, Harmonic Mean and coefficient of variation.
  85. Preparing the data for analysis • Data Preparation • The data collected from the respondents is generally not in the form to be analyzed directly. After the responses are recorded or received, the next stage is that of preparation of data i.e. to make the data amenable for appropriate analysis. • Data preparation includes editing, coding, and data entry and is the activity that ensures the accuracy of the data and their conversion from raw form to reduced and classified forms that are more appropriate for analysis. Preparing a descriptive statistical summary is another preliminary step leading to an understanding of the collected data
  86. Data Preparation: Editing Coding Validation of data Data entry Classification Tabulation
  87. EDITING • The customary first step in analysis is to edit the raw data. Editing detects errors and omissions, corrects them when possible, and certifies that maximum data quality standards are achieved. The editor's purpose is to guarantee that data are: • 1. Accurate. • 2. Consistent with the intent of the question and other information in the survey. • 3. Uniformly entered. • 4. Complete. • 5. Arranged to simplify coding and tabulation
  88. Editing • Field Editing • Central Editing • In large projects, field editing review is a responsibility of the field supervisor. • It, should be done soon after the data have been gathered. During the stress of data collection in a personal interview and paper- and-pencil recording in an observation, the researcher often uses ad hoc abbreviations special symbols. • Soon after the interview, experiment, or observation, the investigator should review the reporting forms
  89. Central Editing • It should take place when all forms or schedules have been completed and returned to the office. • This type of editing implies that all forms should get a thorough editing by a single editor in a small study and by a team of editors in case of a large inquiry. • Editor(s) may correct the obvious errors such as an entry in the wrong place, entry recorded in months when it should have been recorded in weeks, and the like. In case of inappropriate on missing replies, the editor can sometimes determine the proper answer by reviewing the other information in the schedule. At times, the respondent can be contacted for clarification.
  90. Be familiar with instructions given to interviewers and coders. • Do not destroy, erase, or make illegible the original entry by the interviewer; • Original entries should remain legible. • Make all editing entries on an instrument in some distinctive color and in a standardized form. • Initial all answers changed or supplied. • Place initials and date of editing on each instrument completed.
  91. ERROR DETECTION • First step in error detection is to determine whether the software used for data entry and tabulation will allow the researcher to perform “error edit routines” which identifies the wrong type of data. • Example – Say that for a particular field on a given data record, only the codes of 1 or 2 should appear. An error edit routine can display an error message on the data output if any number other than 1 or 2 has been entered • Another approach to error detection is for the researcher to review a printed representation of entered data • The final approach to error detection is to produce a data/column list for the entered data. Quick view of this data/column list procedure can indicate to the analyst whether inappropriate codes were entered into data fields
  92. Data tabulation • INTRODUCTION • The classification of data leads to the problem of presentation of data. The presentation of data means exhibition of the data in such a clear and attractive manner that these are easily understood and analyzed. • There are many forms of presentation of data of which the following three are well known: • (i) Textual Presentation, • (ii) Tabular Presentation, • (iii) Diagrammatic Presentation. Here, we discuses in detail Tabular method of data presentation.
  93. What is a Table • A table is a symmetric arrangement of statistical data in rows and columns. • DEFINITIONS • “Table involves the orderly and systematic presentation of numerical data in a form designed to elucidate the problem under consideration.” ---According Prof. L.R.Connor,”
  94. Features of a good Table • Title as compatible with the objective of the study • To facilitate comparison. • Ideal Size • Stubs • Use of Zero • Heading • Abbreviation • Footnote • Total • Source of data • Size of Columns • Simple, Economical and Attractive
  95. Objectives of Tabulation • To carry out investigation • To do comparison • To locate omissions and errors in the data. • To use space economically • To simplify data • To use it as future reference
  96. Parts of a Table •Table number •Title of the table •Caption and stubs •Body •Prefatory or head note •Footnotes
  97. Types of Tables •There are three basis of classifying tables. •I. Purpose of a table •II. Originality of a table •III. Construction of a table
  98. I. According to Purpose • General Purpose Table: General purpose table is that table which is of general use. It is does not serve any specific purpose or specific problem under consideration. • Special Purpose Table: Special Purpose table is that table which is prepared with some specific purpose in mind.
  99. II. According to Originality • Original Table: An original table is that in which data are presented in the same form and way they are collected. • Derived Table: A derived table is that in which data are not presented in the form or way these are collected. Instead, the data are first converted into ratios or percentage and then presented.
  100. III. According to Construction •Simple Table •Complex Tables •a. Double or Two-Way Table •b. Three-Way Table •c. Manifold (or Higher Order) Table
  101. Simple Table • In a simple table (also known as one-way table), data are presented based on only one characteristic
  102. Complex Tables • In a complex table (also known as a manifold table) data are presented according to two or more characteristics simultaneously. The complex tables are two-way or three- way tables according to whether two or three characteristics are presented simultaneously. • a. Double or Two-Way Table • b. Three-Way Table • c. Manifold (or Higher Order) Table
  103. Double or Two-Way Table • In such a table, the variable under study is further subdivided into two groups according to two inter-related characteristics. The two-way table is shown in Table 1.2.
  104. Three-Way Table • In such a table, the variable under study is divided according to three interrelated characteristics. The Three- Way Table is shown in Table 1.3.
  105. Manifold (or Higher Order) Table • Such tables provide information about a large no of interrelated characteristics in the data set. Manifold (or Higher Order) Table is shown in Table 1.4. • CONCLUSION • With the help of above discussion we can say that table are help us to represent the data in the form of rows and columns and make it useful for the purposes.
  106. Reliability and validity test • Reliability (Cronbach’s Alpha) • Reliability refers to whether your data collection techniques and analytic procedures would reproduce consistent findings if they were repeated on another occasion or if they were replicated by another researcher. • Don't mix Positive and Negative question • LIKERT scale variables (only)
  107. Analysis using SPSS A Commonly accepted rule
  108. Reliability (Cronbach’s Alpha) – Interpretation Reliability and Scale Statistics S.No Benefits No. of items Cronbach’s Alpha 1 Monetary 5 Value from spss 2 Non Monetary 5 Value from spss The Cronbach’s Alpha Value of the monetary benefit is **** which is more than ***. Hence the reliability of the question is proved. The Cronbach’s Alpha Value of the non monetary benefit is *** which is more than the value of ***. Hence the reliability of the question is proved ie., the questionnaire is reliable for the purpose of data collection.
  109. Validity (Pearson Correlation) • Validity is about the accuracy of a measure • Validity is a judgement based on various type of evidence
  110. Go to SPSS • Transform – Compute Variable – m1 + m2+ m3 + m4+ m5 • Analyse – Corrolate – Bivariate (Move needed variable from left to right – m1, m2, m3, m4, m5, mtotal) – click ok • Mtotal values must be > 0.159 is acceptable – Valid • And also do for nm
  111. DATA ANALYSIS • Once the data have been collected and prepared for analysis, there are some basic statistical analysis procedures that researcher will want to perform • An obvious need for these statistics comes from the fact that almost all data sets are disaggregated • Graphics should be used whenever practical availing information user to quickly grasp the essence of the information developed in research project • Charts also can be an effective visual aid to enhance the communication process and add clarity and impact to research reports • i.e Bar Charts, • Line charts, • pie or round chart
  112. • Data must be accurately scored and systematically organized to facilitate data analysis vide • descriptive analysis, • univariate , • bivariate analysis and • multivariate analysis • Descriptive statistics : permit the researcher to describe many pieces of data with a few indices • Statistics : indices calculated by the researcher for a sample drawn from a population • Parameter : indices calculated by the researcher for an entire population • (Adults in bangalore city - % that are married, average age)
  113. Types of descriptive statistics • 1) Graphs • 2) Measures of Central Tendency • 3) Measures of central variability
  114. Graphs : • . Representations of data enabling the researcher to see what the distribution of scores look like Bar graph, line graph and Pie or Round chart
  115. Measure of Central Tendency • Indices enabling the researcher to determine the typical or average score of a group of scores. • They are : • a)Mean – • The arithmetic average of the sample • All values of a distribution of responses are summed and divided by the number of valid responses • b) Median – • The middle value of rank ordered distribution • Exactly half of the responses are above and half are below the median value • C) Mode – • The most common value in the set of responses to a question i.e the response most often given to a question
  116. Measure of Variability • Indices enabling the researcher to indicate how spread out a group of scores are They are : • a)Range • b) Variance • c)Standard Deviation
  117. a) Range – The difference between the highest and lowest score in a distribution b) Variance – A summary statistic indicating the degree of variability among participants for a given variable The average squared deviation about the mean of distribution of values c) Standard deviation – The square root of variance providing an index of variability in the distribution of scores. It describes the average distance of distribution values from the mean
  118. How to determine the sample size? • Sample Size is determined in two steps: • 1. Calculate the sample size for infinite populations. • S = Z2 * p * (1-p) /M2 • S = Sample Size for infinite population • Z = Z score • P = Population proportion (assumed to be 50% = 0.5) • M = margin of error • Z score is determined based on confidence level • Confidence level: The probability that the value of a parameter falls within a specified range of values Confidence Level Z- Value 90% 1.645 95% 1.960 99% 2.576
  119. • If we consider 95% confidence level then Z – score value is 1.96 • Margin of error is a small amount that is allowed for in case of miscalculation or change of circumstance • Generally we take margin of error as 5 % i.e m = 0.05 • Z – score = 1.96 • P = 0.5 • M = 0.05 • S = Z2*P*(1-p)/m2 • S = (1.96)2*0.5*(1-0.5)/(0.05)2 • S = 384.16 • So, sample size for the infinite population is ***
  120. • 2. Adjust the sample size to required Population. • For example, If we want to adjust the sample size to 1,00,000 population • Then use the following formula for adjusted sample size • Adjusted sample size = (S) /1 + ((S-1)/Population) • Adjusted S = 384.16/1+((384.16-1)/100000) • Adjusted S = 382.69 • Adjusted S = 382.69 =>383 • Finally, we have determined the sample size for 1,00,000 population as 383