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CRJU 202 005

by: Alexis M.
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Research Methods in Criminology and Criminal Justice
Brian Fuleihan
Class Notes
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This 5 page Class Notes was uploaded by Alexis M. on Sunday October 2, 2016. The Class Notes belongs to CRJU 202 005 at University of South Carolina taught by Brian Fuleihan in Fall 2016. Since its upload, it has received 3 views. For similar materials see Research Methods in Criminology and Criminal Justice in Criminology and Criminal Justice at University of South Carolina.

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Date Created: 10/02/16
9/26/2016 Chapter 5: Sampling  What is a sample & why would we want to use one? Generalizability or External Validity Population   ­ the entire set of elements (individuals or other entities) Sample   ­ a subset of elements from the population  Sample generalizability­ ability to generalize from a subset (sample) of a larger population to  that population itself Cross­population generalizability­ ability to generalize from one population, or setting—to other  populations, or setting Basic Sampling Types  Probability sampling   Known probability of selection into sample Produces the most generalizable sample    Non­probability sampling  Unknown probability of selection into sample Frequently used in qualitative research, which populations is not well defined Sampling Methods  Probability Sampling Methods   Sampling methods that allow us to know in advance… how likely it is that any element of a population will be selected for the sample. The probability is known and not zero (requires a sampling frame)  Allows researchers to select study subjects … to be statistically representative of  population they want to learn about (generalize to) Representative= aggregate characteristics of the sample closely approximate those characteristics in the population    Even when a random selection process is used, two common problems can result in  bias…  (1) Incomplete sampling frame   E.g., In a study of campus drug use, our list of enrolled students may not reflect recent  withdrawals (dropouts) If sampling frame is incomplete, it is not truly a random sample of population   (2) Nonresponse An inability to contact a good number of subjects or having a high refusal rate can result in  biasness. Why? Opinion varies, but a 60% response rate is ‘acceptable’; strive for at least 70% Even so, there is still error associated with every probability sample  taken   Even IF sampling frame and response rate are good, there is still error associated with  every probability sample taken.  Important kinds of probability samples Simple Random Sample  Identifies cases strictly on the basis of chance  True random sample is obtained through the equal probability of selection method (EPSEM) ­ All members of population have equal chance of being sampled  ­ Some methods of selecting a SRS: coin flip, roll of dice, random number table,  random digit dialing, computer generated random numbers. How to obtain a Simple Random Sample (SRS)  Assign a number to all the elements listed in the sampling frame. 1­ John 11­ Tina 21­ Ronald 2­ Kelli 12­ John 22­ Dave 3­ Joan 13­ Bobby 23­ Taylor 4­ Stephen 14­ Jeff 24­ Ashley 5­ Sharon 15­ Katie 25­ Thomas N= 30 students 6­ Robert 16­ Mike 26­ Jack n = 6 students (20% sample) 7­ Mark 17­ Megan 27­ Lisa 8­ Laura 18­ Pamela 28­ Anna 9­ Andrew 19­ Kathy 29­ Martin 10­ Rachel 20­ Lindsey 30­ Lola Consult a random number table (A­E)   74088  65564   39634  62349  16379  19713  39153  69459  17986  24537   40469  27478   14595  35050  44526  67331  93365  54526  22356  93208   83722  79712   30734  71571  25775  65178  07763  82928  31131  30196   91254  24090   64628  89126  25752  03091  39411  73146  06089  15630   43511  42082   42831  95113  15140  34733  68076  18292  69486  80468   41047  26792   80583  70361  78466  03395  17635  09697  82447  31405   99457  72570   00209  90404  42194  49043  24330  14939  09865  45906 HOWEVER, WE NOW HAVE COMPUTER PROGRAMS WHICH GENERATE LISTS OF RANDOM  NUMBERS FOR SAMPLING PURPOSES Systematic Random Sample  Determine the number to sample, for example, a sample of 125 from a population of 700    Calculate sampling interval    population / sample size.  Round to next lowest whole number  700 / 125 = 5.6    5  Select first element randomly (usually from a list), and then select every n   element. Here, select every 5  member of population In almost all sampling situations, systematic random sampling yields what is essentially a simple random sample  Unless this occurs…...  Periodicity –  the sequence of elements of the population varies in some regular, periodic pattern Stratified Random Sample  Strata – layers, levels, groups (singular is stratum) ­Purpose: to ensure that various groups will be included in the sample. Useful when  researcher needs to make sure that small groups are included  1. Distinguish all elements in the population (i.e., in the sampling frame) according to their value on some relevant characteristic (e.g., police officer rank: captains, lieutenants, sergeants, patrol  officers, etc.).  ­That characteristic forms the sampling strata. Each element must belong to one and  only one stratum  2. Sample elements randomly from within each strata: e.g., 25 captains, 25 sergeants, etc. Types of Stratified Sampling Imagine that you plan to draw a sample of 500 from an ethnically diverse neighborhood. The  neighborhood population is 15% black, 10% Hispanic, 5% Asian, and 70% white. A simple random  sample might, for example, end up with no Asians and very few Hispanics.  In proportionate stratified sampling, ­ you create sampling strata based on a characteristic of  interest (e.g., ethnicity) and randomly select cases from each stratum, in exactly the same  proportions.  In disproportionate stratified sampling, ­ the proportion of each stratum that is included in the  sample is intentionally varied from what it is in the population. Here, you might select an equal  proportion from each ethnic group. Cluster Sampling  Cluster ­ naturally occurring, mixed group of elements of the population; each element (person,  for instance) appears in one and only one cluster at one time ­ Prisons are clusters for sampling inmates ­ City blocks are clusters for sampling residents  Useful when sampling frame (a definite list of elements) is not available or too expensive to cover  ­ Larger populations spread out across a wide geographic area  ­ “Hidden” populations   Also called “multi­stage cluster sampling” because cluster sampling is at least a two­stage  procedure.  Draw a random sample of clusters. (A list of clusters should be much easier to obtain than a list of all the individuals in every cluster in the population).  Draw a random sample of elements within each selected cluster. Non­Probability Sampling  Non­probability sample   Kinds of non­probability samples Convenience sampling  a.k.a., sampling of available subjects; selection of cases on the basis of ease of  availability to researcher  Quota sampling    Like stratified sampling, but selection of subjects within strata is nonrandom (left to  interviewer judgment)  Sample unlikely to be representative of the population on other characteristics Purposive sampling  a.k.a., judgmental sampling  Use your expertise to sample units/subjects  Purposive & probability sampling Snowball sampling  Selection of cases on the basis of referrals from sampled individuals   Proceeds in two stages  Useful for sampling cases within a network   Goal of sampling  To generalize to population, sample must be as representative as possible Random sampling error  Refers to the difference between a sample statistic and the true value in the population due solely to chance  Sampling error or bias  Statisticians estimate the probability that a sampling result is representative of the population  using sampling distribution Systematic sampling error  Refers to differences between the sample and population due to some problem with the sampling method Units of Analysis


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