SAMPLING TECHNIQUES THE SMART STUDENT com LEARNING OBJECTIVES

SAMPLING TECHNIQUES THE SMART STUDENT. com

LEARNING OBJECTIVES � To identify the need for sampling in research � To appreciate the importance of technique of sampling in affecting the quality of research � to understand the different types of sampling techniques � To appreciate the possible issues in sampling

Why Sample? � Why � not study everyone? Debate about Census vs. sampling

Problems in Sampling? � What problems do you know about? � What issues are you aware of? � What questions do you have?

Key Sampling Concepts � What we want to generalise? � What population can we get access to? ◦ The theoretical population ◦ The study population � How can we get access to them ? � Who is in your study? ◦ The sampling frame ◦ The sample

Sampling Process � Units � � � of analysis(people) Target population Population of interest Actual population to which generalizati ons are made Target sample Sample (people actually studied) list or procedure Sampling frame m List /rule defining the e population t h o List of d target sample

Sampling Frame � The list or procedure defining the POPULATION. (From which the sample will be drawn. ) � Distinguish sampling frame from sample. � Examples: – Telephone book – Voter list – Random digit dialing � Essential for probability sampling, but can be defined for non=probability sampling

Types of Samples Simple random systematic random PROBABILITY Stratified cluster Complex multi stage random Random cluster NON PROBABILITY qouta Convenienc e purposive stratified random

Simple Random Sampling � Each element in the population has an equal probability of selection AND each combination of elements has an equal probability of selection � Names drawn out of a hat � Random numbers to select elements from an ordered list

Stratified Random Sampling-1 � Divide population into groups that differ in important ways � Basis for grouping must be known before sampling � Select random sample � from within each group

Random Sampling Procedures Sample points are proportionally represented within population subgroups. The subgroups are chosen with the needs of the study in mind. e. g. , N = 8000 and n = 200 Male = 43% and Female = 57%. 43 x n =. 43 x 200 = 86 males +. 57 x n =. 57 x 200 = 114 females 11

Stratified Random Sampling-2 reduces error compared to simple random sampling � Tradeoff between the cost of doing the stratification and smaller sample size � Probabilities of selection may be different for different groups � comparisons �

Systematic Random Sampling Systematic random sampling is a method of probability sampling in which the defined target population is ordered and the sample is selected according to position using a skip interval

Steps in Drawing a Systematic Random Sample 1: Obtain a list of units that contains an acceptable frame of the target population 2: Determine the number of units in the list and the desired sample size 3: Compute the skip interval 4: Determine a random start point 5: Beginning at the start point, select the units by choosing each unit that corresponds to the skip interval

SYSTEMATIC Random Sampling Sample points are spread over entire sampling frame. 1) Determine the sampling interval N/n where N = population size; n = sample size e. g. , N = 8000 and n = 200 N/n = 8000/200 = 40 2) Determine one random number (k) in the first interval. e. g. , k = 12 3) The sample contains the kth element in each sampling interval. i. e. , 1 st interval 12 th element 2 nd interval 12 + 40 = 52 nd element 3 rd interval 12 + 80 = 92 nd element 4 th interval 12 + 120 = 132 nd element … 200 th interval 12 + 7960 = 7072 nd 15

CLUSTER Random Sampling Sample points are all the members of a naturally occurring unit (cluster). 1) The target population is divided into natural occurring subgroups (clusters). e. g. , sports clubs 2) Subgroups are randomly chosen. e. g. , AM COLLEGE HOCKEY CLUB, CYCLING CLUB, PARA –GLIDING CLUB, TABLE TENNIS CLUB (WOMEN'S), VOLLEYBALL CLUB (MEN'S) 3) Sample points are all elements in chosen clusters. 16

Nonprobability Sampling Methods Convenience sampling relies upon convenience and access Judgment sampling relies upon belief that participants fit characteristics Quota sampling emphasizes representation of specific characteristics Snowball sampling relies upon respondent referrals of others with like characteristics

Sampling Frame is Crucial in Probability Sampling � If the sampling frame is a poor fit to the population of interest, random sampling from that frame cannot fix the problem � Elements not in the sampling frame have zero probability of selection. Generalizations can be made ONLY to the actual population defined by the sampling frame

BIAS IN SAMPLING-5 sources � Any deviation from rulesself selection volunteers � Omission of hard to identify peolple missing persistant absentees � Replacement of previously selected individuals ◦ Difficult to trace after being included in frame/uncooperative � Large scale refusal � List/sampling frame goes out of date

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