Sampling Techniques, Scaling Techniques and Questionnaire Frame
- 1. Sampling Method Scaling Techniques Questionnaire frame - discussion Dr. Sridhar L S Faculty SJCC 1
- 2. What is research? • Research is an organized and systematic way of finding answers to questions 2
- 3. Important Components of Empirical Research • Problem statement, research questions, purposes, benefits • Theory, assumptions, background literature • Variables and hypotheses • Operational definitions and measurement • Research design and methodology • Instrumentation, sampling • Data analysis • Conclusions, interpretations, recommendations 3
- 4. SAMPLING •A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005) •Why sample? •Resources (time, money) and workload •Gives results with known accuracy that can be calculated mathematically •The sampling frame is the list from which the potential respondents are drawn •Registrar’s office •Class rosters •Must assess sampling frame errors 4
- 5. SAMPLING…… •What is your population of interest? •To whom do you want to generalize your results? •Can you sample the entire population? 5
- 6. SAMPLING……. • 3 factors that influence sample representative-ness • Sampling procedure • Sample size • Participation (response) • When might you sample the entire population? • When your population is very small • When you have extensive resources • When you don’t expect a very high response 6
- 8. Process •The sampling process comprises several stages: •Defining the population of concern •Specifying a sampling frame, a set of items or events possible to measure •Specifying a sampling method for selecting items or events from the frame •Determining the sample size •Implementing the sampling plan •Sampling and data collecting •Reviewing the sampling process 8
- 9. Types of Samples • Probability (Random) Samples • Simple random sample • Systematic random sample • Stratified random sample • Multistage sample • Multiphase sample • Cluster sample • Non-Probability Samples • Convenience sample • Purposive sample • Quota 9
- 10. Population definition •A population can be defined as including all people or items with the characteristic one wishes to understand. • Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population. 10
- 11. PROBABILITY SAMPLING • A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. • . When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight. 11
- 12. PROBABILITY SAMPLING……. • Probability sampling includes: • Simple Random Sampling, • Systematic Sampling, • Stratified Random Sampling, • Cluster Sampling • Multistage Sampling. • Multiphase sampling 12
- 13. NON PROBABILITY SAMPLING • Any sampling method where some elements of population have no chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection can't be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling not allows the estimation of sampling errors.. • Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities. 13
- 14. NONPROBABILITY SAMPLING……. • Nonprobability Sampling includes: Accidental Sampling, Quota Sampling and Purposive Sampling. In addition, nonresponse effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled. 14
- 15. SIMPLE RANDOM SAMPLING • Applicable when population is small, homogeneous & readily available • All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. • It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame. • A table of random number or lottery system is used to determine which units are to be selected. 15
- 16. SYSTEMATIC SAMPLING…… As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is not 'simple random sampling' because different subsets of the same size have different selection probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten probability of selection, but the set {4,13,24,34,...} has zero probability of selection. 16
- 17. STRATIFIED SAMPLING Where population embraces a number of distinct categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. • Every unit in a stratum has same chance of being selected. • Using same sampling fraction for all strata ensures proportionate representation in the sample. • Adequate representation of minority subgroups of interest can be ensured by stratification & varying sampling fraction between strata as required. 17
- 18. STRATIFIED SAMPLING……. 18 Draw a sample from each stratum
- 19. CLUSTER SAMPLING •Cluster sampling is an example of 'two-stage sampling' . • First stage a sample of areas is chosen; • Second stage a sample of respondents within those areas is selected. • Population divided into clusters of homogeneous units, usually based on geographical contiguity. •Sampling units are groups rather than individuals. •A sample of such clusters is then selected. •All units from the selected clusters are studied. 19
- 20. CLUSTER SAMPLING……. • Identification of clusters – List all cities, towns, villages & wards of cities with their population falling in target area under study. – Calculate cumulative population & divide by 30, this gives sampling interval. – Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster. – Random no.+ sampling interval = population of 2nd cluster. – Second cluster + sampling interval = 4th cluster. – Last or 30th cluster = 29th cluster + sampling interval 20
- 21. CLUSTER SAMPLING……. • Freq c f cluster • I 2000 2000 1 • II 3000 5000 2 • III 1500 6500 • IV 4000 10500 3 • V 5000 15500 4, 5 • VI 2500 18000 6 • VII 2000 20000 7 • VIII 3000 23000 8 • IX 3500 26500 9 • X 4500 31000 10 • XI 4000 35000 11, 12 • XII 4000 39000 13 • XIII 3500 44000 14,15 • XIV 2000 46000 • XV 3000 49000 16 • XVI 3500 52500 17 • XVII 4000 56500 18,19 • XVIII 4500 61000 20 • XIX 4000 65000 21,22 • XX 4000 69000 23 • XXI 2000 71000 24 • XXII 2000 73000 • XXIII 3000 76000 25 • XXIV 3000 79000 26 • XXV 5000 84000 27,28 • XXVI 2000 86000 29 • XXVII 1000 87000 • XXVIII 1000 88000 • XXIX 1000 89000 30 • XXX 1000 90000 • 90000/30 = 3000 sampling interval 21
- 22. Difference Between Strata and Clusters • Although strata and clusters are both non-overlapping subsets of the population, they differ in several ways. • All strata are represented in the sample; but only a subset of clusters are in the sample. • With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous 22
- 23. MULTISTAGE SAMPLING • Complex form of cluster sampling in which two or more levels of units are embedded one in the other. • First stage, random number of districts chosen in all states. • Followed by random number of talukas, villages. • Then third stage units will be houses. • All ultimate units (houses, for instance) selected at last step are surveyed. 23
- 24. MULTI PHASE SAMPLING • Part of the information collected from whole sample & part from subsample. • In Tb survey MT in all cases – Phase I • X –Ray chest in MT +ve cases – Phase II • Sputum examination in X – Ray +ve cases - Phase III • Survey by such procedure is less costly, less laborious & more purposeful 24
- 25. QUOTA SAMPLING • The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. • Then judgment used to select subjects or units from each segment based on a specified proportion. • For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. • It is this second step which makes the technique one of non-probability sampling. • In quota sampling the selection of the sample is non- random. • For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years 25
- 26. 26 CONVENIENCE SAMPLING……. • Use results that are easy to get 26
- 27. Judgmental sampling or Purposive sampling • - The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched 27
- 28. PANEL SAMPLING • Method of first selecting a group of participants through a random sampling method and then asking that group for the same information again several times over a period of time. • Therefore, each participant is given same survey or interview at two or more time points; each period of data collection called a "wave". • This sampling methodology often chosen for large scale or nation- wide studies in order to gauge changes in the population with regard to any number of variables from chronic illness to job stress to weekly food expenditures. • Panel sampling can also be used to inform researchers about within-person health changes due to age or help explain changes in continuous dependent variables such as spousal interaction. • There have been several proposed methods of analyzing panel sample data, including growth curves. 28
- 29. Measurement and Scaling Measurement means assigning numbers or other symbols to characteristics of objects according to certain pre-specified rules. • One-to-one correspondence between the numbers and the characteristics being measured. • The rules for assigning numbers should be standardized and applied uniformly. • Rules must not change over objects or time.
- 30. Scale Characteristics Description By description, we mean the unique labels or descriptors that are used to designate each value of the scale. All scales possess description. Order By order, we mean the relative sizes or positions of the descriptors. Order is denoted by descriptors such as greater than, less than, and equal to.
- 31. Scale Characteristics Distance The characteristic of distance means that absolute differences between the scale descriptors are known and may be expressed in units. Origin The origin characteristic means that the scale has a unique or fixed beginning or true zero point.
- 32. Primary Scales of Measurement 7 38 Scale Nominal Numbers Assigned to Runners Ordinal Rank Order of Winners Interval Performance Rating on a 0 to 10 Scale Ratio Time to Finish in Seconds Third place Second place First place Finish Finish 8.2 9.1 9.6 15.2 14.1 13.4
- 33. A Classification of Scaling Techniques SCALING TECHNINQUES Likert Semantic Differential Stapel Noncomparative Scales Comparative Scales Paired Comparison Rank Order Constant Sum Q-Sort and Other Procedures Continuous Rating Scales Itemized Rating Scales
- 34. A Comparison of Scaling Techniques • Comparative scales involve the direct comparison of stimulus objects. Comparative scale data must be interpreted in relative terms and have only ordinal or rank order properties. • In noncomparative scales, each object is scaled independently of the others in the stimulus set. The resulting data are generally assumed to be interval or ratio scaled.
- 36. Preference for Toothpaste Brands Using Rank Order Scaling Fig. 8.4 cont. Form Brand Rank Order 1. Crest 2. Colgate 3. Aim 4. Gleem 5. Sensodyne 6. Ultra Brite 7. Close Up 8. Pepsodent 9. Plus White 10. Stripe
- 37. Comparative Scaling Techniques Constant Sum Scaling •Respondents allocate a constant sum of units, such as 100 points to attributes of a product to reflect their importance. •If an attribute is unimportant, the respondent assigns it zero points. •If an attribute is twice as important as some other attribute, it receives twice as many points. •The sum of all the points is 100. Hence, the name of the scale.
- 38. Continuous Rating Scale • Respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. • The form of the continuous scale may vary considerably. • How would you rate Wal Mart as a department store? Version 1 • Probably the worst - - - - - - -I - - - - - - - - - - - - - - - - - - - - - - Probably the best • Version 2 • Probably the worst - - - - - - -I - - - - - - - - - - - - - - - - - - - - - --Probably the best 0 10 20 30 40 50 60 70 80 90 100 • Version 3 Very bad Neither good Very good nor bad Probably the worst - - - - - - -I - - - - - - - - - - - - - - - - - - - - ---Probably the best 0 10 20 30 40 50 60 70 80 90 100
- 39. Itemized Rating Scales • The respondents are provided with a scale that has a number or brief description associated with each category. • The categories are ordered in terms of scale position, and the respondents are required to select the specified category that best describes the object being rated. • The commonly used itemized rating scales are the Likert, semantic differential, and Stapel scales.
- 40. Likert Scale The Likert scale requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects. • The analysis can be conducted on an item-by-item basis (profile analysis), or a total (summated) score can be calculated. • When arriving at a total score, the categories assigned to the negative statements by the respondents should be scored by reversing the scale. Strongly Disagree Neither Agree Strongly disagree agree nor agree 1. Sears sells high-quality merchandise. 1 2X disagree 3 4 5 2. Sears has poor in-store service. 1 2X 3 4 5 3. I like to shop at Sears. 1 2 3X 4 5
- 41. Semantic Differential Scale The semantic differential is a seven-point rating scale with end points associated with bipolar labels that have semantic meaning. SEARS IS: Powerful --:--:--:--:-X-:--:--: Weak Unreliable --:--:--:--:--:-X-:--: Reliable Modern --:--:--:--:--:--:-X-: Old-fashioned • The negative adjective or phrase sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. Individual items on a semantic differential scale may be scored on either a -3 to +3 or a 1 to 7 scale. • •
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