1 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Chapter 9 Examining Populations and Samples in Research

2 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling Concepts  Sampling: Selecting a group of people, events, behaviors, or other elements with which to conduct a study  Sampling plan: Sampling method; defines the selection process  Sample: Defines the selected group of people or elements from which data are collected for a study  Members of the sample can be called the subjects or participants.

3 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Populations and Elements  Population: A particular group of individuals or elements who are the focus of the research  Target population: An entire set of individuals or elements who meet the sampling criteria  Accessible population: The portion of the target population to which the researcher has reasonable access  Elements: Individual units of the population and sample

4 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Generalization  Extending the findings from the sample under study to the larger population  The extent is influenced by the quality of the study and consistency of the study’s findings.

5 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling Criteria: Inclusion  Characteristics that the subject or element must possess to be part of the target population  Examples:  Between the ages of 18 and 45  Ability to speak English  Admitted for gallbladder surgery  Diagnosed with diabetes within past month

6 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling Criteria: Exclusion  Characteristics that can cause a person or element to be excluded from the target population  Examples:  Diagnosis of mental illness  Less than 18 years of age  Diagnosis of cognitive dysfunction  Unable to read or speak English

7 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Defining Sampling Criteria  Homogeneous sample: As similar as possible so as to control for extraneous variables  Heterogeneous sample: Represents a broad range of values  Used when a narrow focus is not desirable

8 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Inappropriate Generalizations  Samples cannot be generalized beyond their sampling criteria.  This may lead to inappropriate generalizations:  Because of language or reading ability  To other types of illnesses or injuries

9 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Representativeness  The sample, the accessible population, and the target population are alike in as many ways as possible.  Need to evaluate:  Setting  Characteristics of subjects (age, gender, ethnicity, income, education)  Distribution of values on variables measured in the study

10 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling Error  Difference between the population mean and the mean of the sample  Random variation  The expected difference in values that occurs when different subjects from the same sample are examined  Difference is random because some values will be higher and others lower than the average population values

11 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling error Population Sample Population mean Sample mean Sampling Error (cont’d)

12 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling Error (cont’d)  Systematic variation (bias)  Consequence of selecting subjects whose measurement values differ in some specific way from those of the population  These values do not vary randomly around the population mean.

13 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Random vs. Systematic Variation in Sampling  Random variation: Expected difference in values that occurs when different subjects from same sample are examined  Difference is random because some values will be higher or lower than the mean population value.  As sample size increases, random variation decreases.  Systematic variation (or systematic bias): Consequence of selecting subjects whose measurement values differ in some way from those of the population

14 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Refusal Rate vs. Acceptance Rate  Refusal rate: Percentage of subjects who declined to participate in the study  80 subjects approached and 4 refused  4  80 = 0.05 = 5% refusal rate  Acceptance rate: Percentage of subjects who consented to be in the study  80 subjects approached and 76 accepted  76  80 = 0.95 = 95% acceptance rate

15 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sample Attrition and Retention  Sample attrition: Withdrawal or loss of subjects from a study  Attrition rate = number of subjects withdrawing ÷ number of study subjects × 100  Sample retention: Number of subjects who remain in and complete a study.

16 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Random Sampling  Increases the representativeness of the sample based on the target population  Control group: Used in studies with random sampling  Comparison group: Not randomly determined

17 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sampling Frame and Sampling Plan  Sampling frame: A listing of every member of the population, using the sampling criteria to define membership in the population  Subjects are selected from the sampling frame  Sampling plan: Outlines strategies used to obtain a sample for a study  Probability sampling plans  Nonprobability sampling plans

18 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Types of Probability Sampling  Simple random sampling  Stratified random sampling  Cluster sampling  Systematic sampling

19 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Simple Random Sampling  Randomly choosing the sample  Can use a table of random numbers  Can draw names out of a hat

20 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Stratified Random Sampling  Ensures all levels of identified variables are adequately represented in the sample  Needs a large population with which to start  Variables often stratified  Age, gender, socioeconomic status  Types of nurses, sites of care

21 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Cluster Sampling  All areas with the elements of the identified population are linked.  A randomized sample of these areas is then chosen.  Used to get a geographically diverse sample  Also used when developing a sampling frame is difficult because of a lack of knowledge of the variables

22 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Systematic Sampling  Selecting every kth individual on the list, starting randomly  Researcher must know number of elements in the population and the sample size desired

23 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Nonprobability Sampling  Quantitative research  Convenience (accidental) sampling  Quota sampling

24 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Convenience Sampling  Also called accidental sampling  Weak approach to sampling because it is hard to control for bias  The sample includes whomever is available and willing to give consent.  Representativeness is a concern.

25 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Quota Sampling  Uses convenience sampling, but with a strategy to ensure inclusion of subject types who are likely to be underrepresented in the convenience sample  Goal is to replicate the proportions of subgroups present in the population  Works better than convenience sampling to reduce bias

26 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sample Size in Quantitative Studies  Affect size  Type of quantitative study conducted  Number of variables  Measurement sensitivity  Data analysis techniques

27 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Power Analysis  Ability to detect differences in the population or capacity to correctly reject a null hypothesis  Standard power of 0.8  Level of significance  Alpha = 0.05, 0.01, 0.001

28 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Effect Size  The effect is the presence of the phenomenon being studied.  The effect size is the extent to which the null hypothesis is false.  When the effect size is large (large variation between groups), only a small sample is needed.  Increasing the sample size increases the effect size.

29 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Number of Variables  As the number of variables increases, the sample size may increase.  The inclusion of multiple dependent variables also increases the sample size needed.

30 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Measurement Sensitivity  Was the tool used a reliable and valid measure of the variable?  As the variance in the instrument scores increases, the sample size needed to obtain significance increases.

31 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Data Analysis Techniques  ANOVA and t-test require equal group sizes, which will increase power because the effect size is maximized.  Chi-square is the weakest of the tests and requires a large sample size to achieve acceptable levels of power.  As the number of categories increases, the sample size must increase as well.

32 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Critiquing the Sample  Identify  Elements  Accessible population  Target population  Evaluate  Appropriateness of generalization in quantitative studies

33 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Critiquing the Sample (cont’d)  Identify the sample criteria.  Judge appropriateness of the sampling criteria.  Identify the sampling method.

34 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Nonprobability Sampling  Qualitative research  Purposive sampling  Network or snowball sampling  Theoretical sampling

35 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Purposeful or Purposive Sampling  Also called judgmental or selective sampling  Efforts are made to include typical or atypical subjects.  Sampling is based on the researcher’s judgment.

36 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Network Sampling  Also called snowball sampling  Takes advantage of social networks to get the sample  One person in the sample asks another to join the sample, and so on.

37 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Theoretical Sampling  Used in grounded theory research  Data are gathered from any individual or group that can provide relevant data for theory generation.  The sample is saturated when the data collection is complete based on the researchers’ expectations.  Diversity in the sample is encouraged.

38 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Sample Size in Qualitative Research  Scope of the study  Nature of the topic  Quality of the data  Study design

39 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Scope of the Study  Broad studies require larger samples than narrow studies.  The sample size must be adequate for the scope.

40 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Nature of the Topic  If the study topic is clear, fewer subjects are needed.  If the topic is difficult to define, then a larger sample is needed.

41 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Quality of the Data  How rich are the data?  Were data collected from the best sources?

42 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Study Design  How many interviews were carried out?  Was the design adequate for the variables?

43 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Adequacy of the Sample in Qualitative Studies  Are the sampling inclusion and exclusion criteria appropriate?  Is the sampling plan adequate to address the purpose of the study?  Is the sample size adequate?  What are the refusal and mortality rates?

44 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Adequacy of the Sample in Qualitative Studies (cont’d)  Are sample characteristics and quality described?  Is there saturation of the data?  Is the setting defined?

45 Copyright © 2011 by Saunders, an imprint of Elsevier Inc. Research Settings  Natural or field setting: uncontrolled in real life  Seen in descriptive or correlational studies  Partially controlled setting: manipulated or modified by the researcher  Seen in correlational, quasi-, or experimental studies  Highly controlled setting: artificially constructed by researcher (i.e., lab setting)  Seen in experimental studies