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1、Ch. 7 Selecting samples,The need to sample Overview of Sampling techniques Probability sampling Non-probability sampling,Definition of terms,Census Collect and analyse data from every possible case or group member Sampling A range of methods that enable researcher to reduce the amount of data by onl
2、y data from a subgroup rather than all possible cases or elements Population The full set of cases from which a sample is taken,Figure 7.1 Population, sample and individual cases,1. The need to sample,Budget constraints prevent you from surveying the entire population Time constraints prevent you fr
3、om surveying the entire population Impracticable to survey the entire population You have collected all the data but need the results quickly,2. Overview of sampling techniques,Probability or representative sampling - Each case from population is known and usually is equal for all cases - (survey an
4、d experimental research strategies) Non-probability or judgemental sampling - Probability of each case from the population is unknown - Impossible to answer research questions or to address objectives that require you to make statistical inferences about the characteristics of the population - (case
5、 study strategy),非随机抽样和随机抽样的比较,Figure 7.2 Sampling techniques,随机抽样,非随机抽样,简单随机 抽样,系统抽样,分层抽样,分群抽样,多步抽样,配额抽样,雪球抽样,便利抽样,自选抽样,目的抽样,极端抽样,同质抽样,不均匀抽样,典型抽样,关键抽样,3. Probability sampling,Process of probability sampling,(2) decide on a suitable sample size,(3) select the most appropriate sampling technique and
6、select the sample,(4) check the sample is representative of the population,(1) identify a suitable sampling frame based on your research questions or objectives,Sample frame A complete list of all the cases in the population from which your sample will be drawn,Sample size the number of cases used f
7、or the research analysis,Statistical inference a probable conclusion about a population on the basis of data of sample,Law of large number Larger sample size can better represent the population than Smaller sample size,How to choose the sample size?,The confidence you need to have in your data: the
8、level of certainty that the sample can represent the total population (confidencesample size) The margin of error that you can tolerate: the accuracy you require for any estimates made from your sample (accuracy sample size) The types of analysis you will undertake: (Categoriessample size; minimum t
9、hreshold of each technique) The size of total population from which your sample is being drawn,Response rate,Reasons of non-response: Unreachable; ineligible; inability；refusal;,total number of responses Total Response rate = - total number in sample - ineligible,total number of responses Active Res
10、ponse rate = - total number in sample (ineligible + unreachable),Population, sampling frame, samples,Select appropriate sampling technique,Five main sampling techniques (1) simple random (2) systematic (3) stratified random (4) cluster (5) multi-stage,sampling technique,(1)Simple random sampling (a)
11、 Number each case in your sampling frame with a unique number (b) Select cases using random numbers until your actual sample size is reached (pp218;587 for “Random number tables”).,sampling technique,(2)Systematic sampling (a) Number each case in your sampling frame at regular intervals (b) Select t
12、he first case using a random number (c) calculate the sampling fraction (抽样比) (d) select subsequent cases systematically using the sampling fraction to determine the frequency of selection,actual sample size Sampling fraction = - total population Sampling fraction: The proportion of the total popula
13、tion that you need to select.,1. Decide on sample size: n 2. Divide frame of N individuals into n groups of k individuals: sampling fraction k=n/N 3. Randomly select one individual from the 1st group 4. Select every 1/k-th individual thereafter,Systematic Sample,sampling technique,(3) Stratified ran
14、dom sampling strtifaid (a) choose the stratification variable(s) (b) divide the sampling frame into the discrete strata (c) number each of the cases within each stratum with a unique number (d) select your sample using either simple random or systematic sampling,Stratified Sample,1. Divide Populatio
15、n into Subgroups Mutually Exclusive Exhaustive At Least 1 Common Characteristic of Interest 2. Select Simple Random Samples from Subgroups,sampling technique,(4)Cluster sampling (a) Choose the cluster grouping for your sampling frame (b) number each of the clusters with a unique number. The first cl
16、uster is numbered 0, the second 1, and so on (c) select your sample using some form of random sampling,Cluster Sample,1. Divide Population into Clusters If Managers are Elements then Companies are Clusters 2. Randomly Select Clusters 3. Survey All or a Random Sample of Elements in Cluster,Companies (Clusters),Sample,sampling technique,(5) Multi-stage sampling,Overview of probability
