Prepared by: DR. ROZIAH MOHD RASDI Faculty of Educational Studies Universiti Putra Malaysia

Transcription

1 Prepared by: DR. ROZIAH MOHD RASDI Faculty of Educational Studies Universiti Putra Malaysia

2 Topic 11 Sample Selection

3 Introduction Strength of quantitative research method its ability to use smaller group of people to make inferences about larger group (Bartlett, Kotrik & Higgins, 2001). To making generalization findings from sample back to population To do so you need to pick the most accurate smaller group to represent the larger group. This is called the Sampling. Smaller group Larger group = SAMPLE (n) = POPULATION (N)

4 Definition Population (or target population) Element entire group of people, events or things of interest that the researcher wishes to investigate a single member of the population Sampling Frame Sample a listing of all the elements in the population from which the sample is drawn a subset of the population Subject / Respondent a single member of the sample

5 Factors affecting the inferences drawn from a sample The size of the sample the larger the sample size, the more accurate the findings. The extend of variation in the sampling population the greater the variation in the study population with respect to the characteristics under study, for a given sample size, the greater the uncertainty.

6 Aims in selecting a sample To achieve maximum precision in your estimates within a given sample size, and avoid bias in the selection of your sample. Population Sample Elements

7 Types of Sampling Probability Sampling Non-probability sampling Mixed sampling Simple random sampling Stratified random sampling Cluster sampling Quota Purposive Systematic sampling Proportionate stratified sampling Disproportionate stratified sampling Single stage Double stage Multi stage Accidental Snowball Expert sampling Figure: Types of sampling in quantitative research

8 Probability Sampling The elements in the population have some known chance or probability of being selected as sample subjects. Allows to make inference from sample about population (generalization). Example: Sample = Consumer. Rate the prize of Novel Z. 75% said expensive. Inference: 75% of all consumer feels the same. Use of inferential statistics the significant values (pvalue) and confidence interval.

9 Types of Probability Sampling Techniques Simple Random Sampling Every element in the population has a known and equal chance of being selected as a subject. Is the most representative of the population for most purposes. Easy to implement. Disadvantages are: Most cumbersome and tedious The entire listing of elements in population frequently unavailable Very expensive Not the most efficient design Time-consuming

10 Random Sampling using SPSS

11

12

13 Stratified Random Sampling Comprises sampling from populations segregated into a number of mutually exclusive sub-populations or strata. E.g. University students divided into juniors, seniors, etc Employees stratified into clerks, supervisors, managers, etc Homogeneity within stratum and heterogeneity between strata. Statistical efficiency greater in stratified samples. Sub-groups can be analysed. Different methods of analysis can be used for different sub-groups. Stratified Sampling oproportionate sampling odisproportionate sampling Substrata/subsets University Substrata/subsets Faculty Substrata/subsets Students Seniority

14 PROPORTIONATE The number of each elements from each stratum is selected according to its proportion in the population. Example: Total population N = 500 (Male = 300, Female = 200) Proportion of male & female: Male = 60% Female = 40% Sample size (n) = 350. proportion of male & female in the sample: Male = 350 x 60% = 210 Female = 350 x 40% = 140 DISPROPORTIONATE The number of each elements from each stratum is selected without consideration to the size of the stratum. Example: Total population N = 500 comprise of Male and Female. Sample size (n) = 350 Select 50% male & 50% female Male (n) = 350 x 50% = 175 Female (n) = 350 x 50% = 175

15 Advantages: Control of sample size in strata Increased statistical efficiency Provides data to represent and analyze subgroups Disadvantages: Increased error if subgroups are selected at different rates Expensive if strata on population must be created High cost Enable use of different methods in strata

16 Cluster Sampling Take clusters or chunks of elements for study. E.g., sample all students in DCE5900 and DCE5131 to study the characteristics of Management Science majors. Divide the population into discrete groups. The complete lists of the clusters will serve as the sampling frame. Select a few cluster using Simple Random Sampling. Statistically it is less efficient than other probability sampling procedures discussed so far. Area Sampling: Cluster sampling confined to a particular area. E.g., sampling residents of a particular locality, county, etc

17 Provide an unbiased estimate of population parameters of properly done. Economically more efficient than simple random Lower cost per sample Easy to do without list ADVANTAGES DISADVANTAGES Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous. Moderate cost.

18 Stage Cluster Sampling 1. Choose the cluster grouping in your sampling frame. 2. Number each of the cluster with a unique number. 1, 2, 3, 4, 5,. 3. Select the cluster using Simple Random Sampling. 4. Then randomly select certain proportion from the cluster as your sample.

19 THINGS TO CONSIDER IN SAMPLE SELECTION USING PROBABILITY SAMPLING Identify correct population and sampling frame ( complete list of all cases in the population) Ensure representativeness of the sample (avoid sampling error) Avoid sampling bias Adequate sample size High response rate Total response rate = total number of responses total number in sample (ineligible + unreachable)

20 Sample representativeness not well represented Sample representativeness well represented

21 Estimating Sample Size

22 Formula for calculating sample size depending on type of statistical analysis Regression/Correlation analysis (Tabachnik & Fidell, 2001) n m (independent variable) Simple linear regression n 50 + m (independent variable) Multiple linear regression

23 Non-Probability Sampling Used when the number of elements in a population is either unknown/cannot be identified. Cannot make inference from the sample about the population. Most often used in qualitative studies. in some quantitative studies it may not be possible to use probability sampling.

24 Types of Non-Probability Sampling Techniques Quota Sampling The researcher non-randomly select subjects from indentified strata until the planned number of subjects is reached Quotas for numbers or proportion of people to be sampled Examples: 1) survey for research on dual career families: 50% working men and 50% working women surveyed. 2) Women in management survey: 70% women surveyed and 30% men surveyed.

25 Accidental Sampling Similar as Quota sampling, but will stop collecting data when you reach the required number of respondents you decide to have in your sample. Common among market research and newspaper reporters. If you are not guided by any obvious characteristics, some people contacted may not have the required information. Judgemental / purposive Sampling Involves the choice of subjects who are in the best position to provide the information required. Researcher deliberately selects the subjects against one/more trait to be a representative sample. Experts opinions could be sought. E.g. Doctors surveyed for cancer causes.

26 Expert Sampling Your respondent must be known experts in the field of interest to you. When use it in qualitative research: - the number of people is dependent upon the data saturation point. When use it in quantitative research: - you decide on the number of experts to be contacted without considering the saturation point.

27 Snowball Sampling Used when elements in population have specific characteristics or knowledge, but are very difficult to locate and contact. Researcher identifies a small number of subjects who in turn identifies others in the population. Initial sample group can be selected by probability or nonprobability methods, but new subjects are selected based on information provided by initial subjects. E.g. Used to locate members of different stakeholder groups regarding their opinions of a new public works project.

28 Mixed Sampling Systematic Sampling Every n th element in the population starting with a randomly chosen element. Select sample at regular intervals from sampling frame. Example: To sample 35 households from a total of 260 houses - sample every 7 th house starting from a randomly chosen number from 1 to 10. If that random number is 7, sample 35 houses starting with 7 th house (14 th house, 21 st house, etc) Possible problem is that there could be systematic bias. e.g. every 7th house could be a corner house, with different characteristics of both house and dwellers.

29 Disadvantages Systematic Sampling Periodicity within population may skew sample and results. Trends in list may bias results. Moderate cost. Advantages Simple to design. Easier than simple random.

30 Choice Points in Sampling Design

31 Step 1: Define the population Step 2: Identify the sampling frame listing of all units in the population from which the sample will be selected Procedure for Drawing a Sample Step 3: Select a sampling procedure Step 4: Determine the sample units Step 5: Select the sample units Step 6: Collect data from the sampled units