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# Sampling

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### Sampling

1. 1. RESEARCHMETHODOLOGY
2. 2. Steps in Research : 1. objectivity 2. Problem formulation 3. Literature study 4. Research design 5. Formulation of Hypothesis 6. Sampling 7. Data collection 8. Processing and analysis of data 9. Interpretation and recommendation 10. Report writing
3. 3. Survey A survey is a process by which certain quantitative/qualitative facts pertaining to certain field of enquiry are collected to throw light on the objectives of a research problem. A descriptive surveys are fact finding surveys An analytical surveys deal with interrelations among different variables of interest and their interaction
4. 4.  A survey is a planned observation of objects that are not controlled by the observer. These objects are not themselves treated but the „Nature‟ is assumed to have applied the treatments and all that analysts can do it to observe the consequences.
5. 5.  A Survey of complete enumeration of population of interest is called Census. A Survey based on a subset of the population which is also called as a sample is termed as sample survey.
6. 6. Sampling or Sampling techniques A sample as the name implies is smaller representative of a larger whole. The method of selecting a portion of the universe for the study is known as sampling. It helps to draw conclusions about the said universe
7. 7.  The entire group from which a sample is chosen is known as the population or universe Census: A complete enumeration of all items in the population is known as census enquiry Sampling frame: It is a list of items from which the sample is to be drawn.
8. 8. Sampling methods or Sampling techniques Sampling Designs: Two generic types:1. Probability or random sampling, and2. Non-probability or Non-random sampling
9. 9. Probability or random samplingA. Simple designs1. Simple random sampling2. Stratified random sampling3. Systematic random samplingB. Complex designs1. Cluster sampling2. Area sampling3. Multi-stage and sub-sampling
10. 10. Non-probability or Non-random samplingA. Simple designs Convenience or accidental sampling Purposive (or Judgement ) samplingB. Complex designs1. Quota sampling2. Snow-ball sampling
11. 11.  Reasons for choosing different sampling designs.1. Nature of population2. Simplicity in adoption3. Availability of frame4. Representativeness5. Nature of sampling unit6. Cost of enumeration7. Precision criterion
12. 12. Probability or random samplingA. Simple designs1. Simple random sampling Simple random sampling is the simplest of all sampling designs Each and every item in the population has an equal and independent chance of inclusion This can be done for a homogenous population. However for heterogeneous population a simple random sampling may not give the desired results.
13. 13. 2. Stratified random sampling This is used for a heterogeneous population. Here the population is stratified (Grouped) into a number of overlapping sub-populations or strata and sample items are selected from each stratum. Ex: In survey of business establishments, one may form large, medium and small establishments. Further the sample selection from each strata is based on simple random selection.
14. 14. 3. Systematic random sampling Only the first unit is selected randomly and the remaining units of the sample are selected at fixed intervals. Ex: To choose every 10th name or 15th item and so on In this method the entire list of the universe is given numbers It is easier and less expensive It is spread more evenly over the entire population The main disadvantage is if there is a hidden periodicity in the population, this may prove inefficient.
15. 15. B. Complex designs1. Cluster sampling : This involves grouping of population and then selecting the groups or clusters rather than individual elements for inclusion in the sample. That is the total population is divided into a number of relatively small subdivisions which are themselves clusters of smaller units. Further some of these clusters are randomly selected for inclusion in the overall selection
16. 16. 2. Area sampling Cluster sampling in the form of grids imposed on maps in certain forms are is termed as Area sampling. It will not be grouped by type of establishments like villages, industries, hospitals etc but based on areas. Ex: National population or well defined political or natural boundaries.
17. 17. Non-probability sampling This sampling does not provide a chance of selection to each population The selection probability is known A non-probability sample may not be true representative Population parameters cannot be estimated from the sample values It suffers from sampling bias which suffers from bias. Hence generally not advisable
18. 18.  When there is no other feasible method for collection of data or non-availability of population for collection of data. When study does not need generalisation of conditions When cost is a consideration When probability sampling needs more time.
19. 19. Non-probability or Non-random samplingA. Simple designs1. Convenience or accidental sampling2. Judgment samplingB. Complex designs1. Quota sampling2. Snow-ball sampling
20. 20. Non-probability or Non-random samplingA. Simple designs1. Convenience or accidental sampling: This method is employed to get information quickly and inexpensively Depends on the convenience of the researcher Keeps in view of the general population
21. 21. 3. Judgment sampling: Judgment sampling is very appropriate when it is necessary to reach small and specialized populations. The researcher uses judgment to identify representative samples A judgmental sampling is likely to be more reliable and representative than a probability sample. However unwelcome bias might creep into results if not honestly judged.
22. 22. Complex designs1. Quota sampling: We observe the responding units non-randomly according to some fixed quota It is to assure that the smaller groups are adequately represented Bias can exist
23. 23. 2. Snow-ball sampling First someone is identified who meets the criteria and further asked to include others. Useful where representatives are inaccessible or hard to find Inherent problem is one who is socially visible are likely to be selected.
24. 24. Data Collection Data are facts, figures and other relevant materials, past and present serving as basis for study and analysis. Types of sources of data1. Primary data2. Secondary Data
25. 25. 1. Primary data are those which are collected afresh and for the first time and thus happens to be original in character2. Secondary data are those which have already been collected by someone else and which have salready been passed through statistical process.
26. 26. Primary data1. Primary data Primary data are those which are collected afresh, for the first time and thus happens to be original in character.2. First formal appearance of results in the print or electronic literature.
27. 27. Secondary data1. Secondary data are those which have already been collected by someone else and which have already been passed through statistical process.2. Secondary sources are works that describe, interpret, analyse primary data3. Comments and discussion of the evidence provided by primary sources
28. 28. Processing of Data Data processing is an intermediary stage of work between data collection and data interpretation The steps involved in processing of data may be stated as: 1. Identifying data structures 2. Editing the data 3. Coding and classifying the data 4. Transcriptions of data 5. Tabulation of data
29. 29.  Editing the data  Data editing at he time of recording the data  Data editing at the time of analysis of data Completeness Accuracy Uniformity
30. 30.  Coding and  Numeric coding  Alphabetic coding  Zero coding Classification
31. 31.  Tabulation  Manual tabulation
32. 32. Graphs/Charts/Diagrams Line Graphs Bar charts Histograms Frequency plygon Ogive Lorenz curve Bar charts  Vertical bar charts  Horizontal bar charts Pie charts pictograms
33. 33.  Line graphs are useful for showing changaes in data relationships. The horizontal line is the x-axis and verical line is the y-axis
34. 34.  A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Bar charts are used for plotting discrete (or discontinuous) data i.e. data which has discrete values and is not continuous.
35. 35.  A histogram is a graphical representation, showing a visual impression of the distribution of data. It is an estimate of the probability distribution of a continuous variable and was first introduced by Karl Pearson. A histogram consists of tabular frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to the frequency of the observations in the interval.
36. 36.  Frequency polygon In laying out a frequency polygon instead of drawing a histogram, the frequency of each class is located at the midpoint of the interval and straight line to connect the plotted points.
37. 37.  An Ogive is a line chart plotted on graph paper from a cumul;ative ferquency distribution
38. 38.  Lorenz Curve is a line chart used to compare the proportionality in two quantities variables.
39. 39.  The circle or pie chart is a component parts bar chart from the segments of the circle. It is usually a percentage chart
40. 40.  A pictogram uses symbols which may be appropriate for the type of data.
41. 41. Statistical analysis of data Purpose Types of statistical analysis  Descriptive analysis  Inferential analysis  Statitiacl estimation  Testing of hypothesis
42. 42.  Types of Statistical analysis  Measures of central tendency  Measures of dispersion  Measures of association/ relations  Analysis of variance  Hypothesis testing  Tests of significance  Time series analysis
43. 43. Methods of collecting Primary data. In many cases the secondary data are inappropriate, inadequate or obsolete, primary data have to be gathered. Primary data are directly collected by the researcher from their original source Method is different from a tool One or more methods can be chosen No method is universal but has its own uniqueness
44. 44. 1. Observation2. Interviewing3. Mail survey4. Experimentation5. Simulation6. Projective technique
45. 45.  Observation: Observation is defined as a systematic viewing of a specific phenomenon in its proper setting for the specific purpose of gathering data for a particular study. Observation includes both seeing and hearing. The main body of knowledge has been developed by observing the nature
46. 46. Observation Participant observationResearcher’sRole Non- participant observationMode of DirectObservation observation Indirect observation ControlledSystem observationAdopted Un-controlled observation
47. 47. Interviewing One of the prominent method of data collection People are generally more willing to talk than to write It is two way systematic conversation between an investigator and an informant initiated for obtaining information relevant to a specific study. It is not only conversation, but also learning from the respondents gestures, expressions, pauses and environment It is carried out in a structured schedule It calls for interviewing skills
48. 48.  Interviewing can be used as a main method or a supplementary method It is the only method for gathering information from illiterate and uneducated method. It can be used for collecting personal and intimate information relating to a person‟s opinions, attitudes, values, future intentions etc.
49. 49. Questionnaire A questionnaire is a series of questions asked to individuals to obtain statistically useful information about a given topic. When properly constructed and responsibly administered, questionnaires become a vital instrument Questionnaires are frequently used in quantitative research. They are a valuable method of collecting a wide range of information from a large number of individuals, often referred to as respondents. Good questionnaire construction is critical to the success of a survey.
50. 50.  Types of questions1. Contingency questions - A question that is answered only if the respondent gives a particular response to a previous question. This avoids asking questions of people that do not apply to them2. Matrix questions - Identical response categories are assigned to multiple questions.3. Closed ended questions - Respondents‟ answers are limited to a fixed set of responses. Most scales are closed ended. Other types of closed ended questions include: 1. Yes/no questions - The respondent answers with a “yes” or a “no”. 2. Multiple choice - The respondent has several option from which to choose. 3. Scaled questions - Responses are graded on a continuum (example : rate the appearance of the product on a scale from 1 to 10, with 10 being the most preferred appearance). Examples of types of scales include the Likert scale, semantic differential scale, etc
51. 51.  Open ended questions - No options or predefined categories are suggested. The respondent supplies their own answer without being constrained by a fixed set of possible responses. Examples of types of open ended questions include:1. Completely unstructured - For example, “What is your opinion of questionnaires?”2. Word association - Words are presented and the respondent mentions the first word that comes to mind.3. Sentence completion - Respondents complete an incomplete sentence. For example, “The most important consideration in my decision to buy a new house is . . .”4. Story completion - Respondents complete an incomplete story.5. Picture completion - Respondents fill in an empty conversation.6. Thematic apperception test - Respondents explain a picture or make up a story about what they think is happening in the picture
52. 52.  Question sequence1. Questions should flow logically from one to the next.2. The researcher must ensure that the answer to a question is not influenced by previous questions.3. Questions should flow from the more general to the more specific.4. Questions should flow from the least sensitive to the most sensitive.5. Questions should flow from factual and behavioral questions to attitudinal and opinion questions.6. Questions should flow from unaided to aided questions.7. The sandwich theory - three stage theory : Initial questions should be screening and rapport questions. Then in the second stage you ask all the product specific questions. In the last stage you ask demographic questions
53. 53.  Research Design - Data collection Observational research Ethnographic group Research Focus group Research Survey research Behavioral data Experimental research( cause & effect relationships)
54. 54.  - Research instrument Questionnaires:Close-endOpen-end Mechanical instruments: like, Galvanometers-emotions Tachistoscopes flashes Eye cameras Audiometer-TV - Sampling plan
55. 55.  Field work - Planning and supervision Data Analysis - Classifying raw data - Summarising data - Analytical methods to analyse and then make an inference
56. 56.  Iceberg principle Observation that in many (if not most) cases only a very small amount (the tip) of information is available or visible about a situation or phenomenon, whereas the real information or bulk of data is either unavailable or hidden. The principle gets its name from the fact that only about 1/10th of an icebergs mass is seen outside while about 9/10th of it is unseen, deep down in water.
57. 57. Formulation of Hypothesis Hypotheses is an imaginary, verifiable statement which is a possible answer to the research question. It is a tentative proposition formulated for empirical testing. It is tentative because its veracity can be tested only after it has been tested empirically They are useful and they guide the research process in the particular direction In exploratory and Descriptive studies hypothese may not be required but it is essential in all analytical and experimental studies
58. 58. Types of HypothesesWith reference to their function:  Discreptive and Relational hypotheses, Casual HypothesesWith ref. to working  Null hypotheses, working hypotheses and Statistical hypothesesLevel of abstraction:  Common sense Hypotheses, Complex Hypotheses and analytical Hypotheses
59. 59. Types of HypothesesWith reference to their function:  Dicretiveand Relational hypotheses, Casual HypothesesWith ref. to working  Null hypotheses, working hypotheses and Statistical hypothesesLevel of abstraction:  Common sense Hypotheses, Complex Hypotheses and analytical Hypotheses
60. 60. Types of HypothesesWith reference to their function:  Dicretiveand Relational hypotheses, Casual HypothesesWith ref. to working  Null hypotheses, working hypotheses and Statistical hypothesesLevel of abstraction:  Common sense Hypotheses, Complex Hypotheses and analytical Hypotheses
61. 61. Types of HypothesesWith reference to their function:  Dicretiveand Relational hypotheses, Casual HypothesesWith ref. to working  Null hypotheses, working hypotheses and Statistical hypothesesLevel of abstraction:  Common sense Hypotheses, Complex Hypotheses and analytical Hypotheses
62. 62.  Six Thinking Hats The de Bono Hats system (also known as "Six Hats" or "Six Thinking Hats") is a thinking tool for group discussion and individual thinking. Combined with the idea of parallel thinking which is associated with it, it provides a means for groups to think together more effectively, and a means to plan thinking processes in a detailed and cohesive way. The method is attributed to Dr. Edward de Bono and is the subject of his book, Six Thinking Hats. The paternity of this method is disputed by the School of Thinking. The method is finding some use in the UK innovation sector, is offered by some facilitation companies and has been trialled within the UK civil service.
63. 63.  Six distinct states are identified and assigned a color:  Information: (White) - considering purely what information is available, what are the facts?  Emotions (Red) - instinctive gut reaction or statements of emotional feeling (but not any justification)  Bad points judgment (Black) - logic applied to identifying flaws or barriers, seeking mismatch  Good points judgment (Yellow) - logic applied to identifying benefits, seeking harmony  Creativity (Green) - statements of provocation and investigation, seeing where a thought goes  Thinking (Blue) - thinking about thinking
64. 64. Data Collection Data are facts, figures and other relevant materials, past and present serving as basis for study and analysis. Types of sources of data1. Primary data2. Secondary Data
65. 65. 1. Primary data are those which are collected afresh and for the first time and thus happens to be original in character2. Secondary data are those which have already been collected by someone else and which have salready been passed through statistical process.
66. 66. Primary data1. Primary data Primary data are those which are collected afresh, for the first time and thus happens to be original in character.2. First formal appearance of results in the print or electronic literature.
67. 67. Secondary data1. Secondary data are those which have already been collected by someone else and which have already been passed through statistical process.2. Secondary sources are works that describe, interpret, analyse primary data3. Comments and discussion of the evidence provided by primary sources
68. 68. Methods of collecting Primary data. In many cases the secondary data are inappropriate, inadequate or obsolete, primary data have to be gathered. Primary data are directly collected by the researcher from their original source Method is different from a tool One or more methods can be chosen No method is universal but has its own uniqueness
69. 69. 1. Observation2. Interviewing3. Mail survey4. Experimentation5. Simulation6. Projective technique
70. 70.  Observation: Observation is defined as a systematic viewing of a specific phenomenon in its proper setting for the specific purpose of gathering data for a particular study. Observation includes both seeing and hearing. The main body of knowledge has been developed by observing the nature
71. 71. Observation Participant observationResearcher’sRole Non- participant observationMode of DirectObservation observation Indirect observation ControlledSystem observationAdopted Un-controlled observation
72. 72. Interviewing One of the prominent method of data collection People are generally more willing to talk than to write It is two way systematic conversation between an investigator and an informant initiated for obtaining information relevant to a specific study. It is not only conversation, but also learning from the respondents gestures, expressions, pauses and environment It is carried out in a structured schedule It calls for interviewing skills
73. 73.  Interviewing can be used as a main method or a supplementary method It is the only method for gathering information from illiterate and uneducated method. It can be used for collecting personal and intimate information relating to a person‟s opinions, attitudes, values, future intentions etc.
74. 74. Questionnaire A questionnaire is a series of questions asked to individuals to obtain statistically useful information about a given topic. When properly constructed and responsibly administered, questionnaires become a vital instrument Questionnaires are frequently used in quantitative research. They are a valuable method of collecting a wide range of information from a large number of individuals, often referred to as respondents. Good questionnaire construction is critical to the success of a survey.
75. 75.  Types of questions1. Contingency questions - A question that is answered only if the respondent gives a particular response to a previous question. This avoids asking questions of people that do not apply to them2. Matrix questions - Identical response categories are assigned to multiple questions.3. Closed ended questions - Respondents‟ answers are limited to a fixed set of responses. Most scales are closed ended. Other types of closed ended questions include: 1. Yes/no questions - The respondent answers with a “yes” or a “no”. 2. Multiple choice - The respondent has several option from which to choose. 3. Scaled questions - Responses are graded on a continuum (example : rate the appearance of the product on a scale from 1 to 10, with 10 being the most preferred appearance). Examples of types of scales include the Likert scale, semantic differential scale, etc
76. 76.  Open ended questions - No options or predefined categories are suggested. The respondent supplies their own answer without being constrained by a fixed set of possible responses. Examples of types of open ended questions include:1. Completely unstructured - For example, “What is your opinion of questionnaires?”2. Word association - Words are presented and the respondent mentions the first word that comes to mind.3. Sentence completion - Respondents complete an incomplete sentence. For example, “The most important consideration in my decision to buy a new house is . . .”4. Story completion - Respondents complete an incomplete story.5. Picture completion - Respondents fill in an empty conversation.6. Thematic apperception test - Respondents explain a picture or make up a story about what they think is happening in the picture
77. 77.  Question sequence1. Questions should flow logically from one to the next.2. The researcher must ensure that the answer to a question is not influenced by previous questions.3. Questions should flow from the more general to the more specific.4. Questions should flow from the least sensitive to the most sensitive.5. Questions should flow from factual and behavioral questions to attitudinal and opinion questions.6. Questions should flow from unaided to aided questions.7. The sandwich theory - three stage theory : Initial questions should be screening and rapport questions. Then in the second stage you ask all the product specific questions. In the last stage you ask demographic questions
78. 78.  Research Design - Data collection Observational research Ethnographic group Research Focus group Research Survey research Behavioral data Experimental research( cause & effect relationships)
79. 79.  - Research instrument Questionnaires:Close-endOpen-end Mechanical instruments: like, Galvanometers-emotions Tachistoscopes flashes Eye cameras Audiometer-TV - Sampling plan
80. 80.  Field work - Planning and supervision Data Analysis - Classifying raw data - Summarising data - Analytical methods to analyse and then make an inference
81. 81.  Application of research : - Sales and market analysis - Product research - Corporate research - Advertising research
82. 82. Barriers to the use of MR A narrow conception of Marketing Research Uneven caliber of Marketing researchers Poor framing of the problem Late and erroneous findings by marketing research Personality and presentational differences.
83. 83.  Forecasting and Demand measurement
84. 84. 3. Decomposition method:The company‟s previous periods sales data is broken into four major components Trend, cycle, seasonal and erratic4. Naive/Ratio method: Time seriesSales forecast for next year=Actual sales of this year x Actual sales of this year Actual sales of last year
85. 85. 6. Regression analysis: Company sale is dependent on many factors such as price, promotional expenditure, population etc. Statistical forecasting - SPSS used- Multiple regression analysis is used7. Econometric analysis : Many regression equations are built to forecast industry sales. A forecast is prepared by solving these equations on computer software.
86. 86. To improve forecasting accuracy:1. Use multiple forecasting methods2. Identify suitable method3. Obtain a range of forecasts4. Use computer hardware and software.
87. 87. Steps in sales forecasting As per the conference board of America report 1978, 10 steps are listed.1. Determine the Purpose for which Forecasts are used2. Divide the company products into homogenous groups3. Determine the factors affecting the sales of each product and their relative importance4. Choose the forecasting methods5. Gather the available data6. Analyse the data7. Check and recheck the deductions8. Make assumptions regarding other factors9. Convert deductions and assumptions into forecasts10. Apply the forecast to company operations
88. 88. Sales Budget A sales budget consists of estimates of expected volume of sales and selling expenses. Sales budget is generally fixed slightly lower than the sales forecast to avoid risk Selling expense budget consists of the selling expense budget and sales department administrative budget The sales budget is the key factor for the successful performance of the sales department
89. 89. Sales Budget Sales departmentSales volume budget Selling expense budget Administrative budget
90. 90. Purposes of the sales budget1. Planning: From total corporate plan marketing and sales budgets are developed considering sales goals, sales strategy, action plan, expense, etc.2. Coordination: Coordinating among various functions3. Control : Evaluation of performance
91. 91. Methods used for deciding sales expenditure budget Sales managers are required to decide expenditure levels for each item of selling expenses.1. Percentage of sales method2. Executive judgment method3. Objective and task method
92. 92. Review SituationSales BudgetProcess Communication Subordinate budgets Approval of budget Other departments
93. 93. STATISTICS The word Statistics means an „organised political state‟ in German Organised numerical data It is a numerical statement of facts in any department of enquiry placed in relation to each other.
94. 94. Interview Guides and Schedules Interview Guides Schedules Types of Interviews  Structured directive Interviews  Unstructured or Non-directive Interview  Focused Interview  Clinical interview  Depth Interview
95. 95. Interviewing process 1. Preparation 2. Introduction 3. Developing rapport 4. Carrying the interview forward 5. Recording the interview 6. Closing the Interview
96. 96. Interview problems Inadequate response Interviewer‟s bias Non-response Non-availability Refusal InaccebilityTelephonic interview