Chapter 9

1. Chapter 9 Examining Populations and Samples in Research

2. 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. 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: Theportion of the target population to which the researcher has reasonable access Elements: Individual units of the population and sample

4. 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. 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. 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. 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. 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. 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. 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. Sampling Error (cont’d) Sampling error Sample Population Population mean Sample mean

12. 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. 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. 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. 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. 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. 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. Types of Probability Sampling Simple random sampling Stratified random sampling Cluster sampling Systematic sampling

19. Simple Random Sampling Randomly choosing the sample Can use a table of random numbers Can draw names out of a hat

20. 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. 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. 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. Nonprobability Sampling Quantitative research Convenience (accidental) sampling Quota sampling

24. 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. 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. Sample Size in Quantitative Studies Affect size Type of quantitative study conducted Number of variables Measurement sensitivity Data analysis techniques

27. 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. 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. 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. 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. 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. Critiquing the Sample Identify Elements Accessible population Target population Evaluate Appropriateness of generalization in quantitative studies

33. Critiquing the Sample (cont’d) Identify the sample criteria. Judge appropriateness of the sampling criteria. Identify the sampling method.

34. Nonprobability Sampling Qualitative research Purposive sampling Network or snowball sampling Theoretical sampling

35. Purposeful or Purposive Sampling Also called judgmental or selectivesampling Efforts are made to include typical or atypical subjects. Sampling is based on the researcher’s judgment.

36. 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. 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. Sample Size in Qualitative Research Scope of the study Nature of the topic Quality of the data Study design

39. Scope of the Study Broad studies require larger samples than narrow studies. The sample size must be adequate for the scope.

40. 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. Quality of the Data How rich are the data? Were data collected from the best sources?

42. Study Design How many interviews were carried out? Was the design adequate for the variables?

43. 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. 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. 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