Donna B. Konradi, DNS, RN, CNE GERO 586 Populations and Samples

Donna B. Konradi, DNS, RN, CNE Sampling Definition: Selecting a group of people, events, behaviors, or other elements with which to conduct a study.

Donna B. Konradi, DNS, RN, CNE Sample Definition: the selected group of people (or elements) from which data are collected for a study

Donna B. Konradi, DNS, RN, CNE Population The entire set of individuals (or elements) who (that) met the sampling criteria. Also called the target population. Accessible population is the portion of the target population to which the researcher has reasonable access.

Donna B. Konradi, DNS, RN, CNE Quantitative Sampling Terminology PopulationSample

Donna B. Konradi, DNS, RN, CNE Sampling Adequacy To what extent are the characteristics of the study sample representative of the population? Very representative  can make generalizations to the population Minimally representative  can not make generalizations to the population

Donna B. Konradi, DNS, RN, CNE Eligibility Criteria Characteristics essential for inclusion in the target population Examples of sampling criteria: Between the ages of 65 and 80 Ability to speak English Diagnosed with high blood pressure Medically able to participate in a low impact exercise routine

Donna B. Konradi, DNS, RN, CNE Quantitative Sampling Terminology Nonprobability sampling Selection is not random Probability sampling Some form of random selection Strata Mutually exclusive segment of the population Based on one or more characteristics (gender, age, race, income, education level, etc)

Donna B. Konradi, DNS, RN, CNE Quantitative Sampling Terminology Sampling bias Systematic over or under representation of some segment of the population Based on a characteristic relevant to the study Example: Research on the effects of various CV drugs and procedures that excluded or under represented women. Attempts were made to generalize findings to women.

Donna B. Konradi, DNS, RN, CNE Sampling Plan Outlines strategies used to obtain a sample for a study

Donna B. Konradi, DNS, RN, CNE Nonprobability Sampling Convenience Snowball or network Quota 40 adults years 40 adults years 40 adults years 40 adults older than 80 years

Donna B. Konradi, DNS, RN, CNE Purposive Sampling Based on the assumption that the researcher’s knowledge about the population can be used to handpick the cases to be included in the sample Lacks external, objective methodology for assessing the “typical-ness” of the sample

Donna B. Konradi, DNS, RN, CNE Purposive Sampling Can be used effectively to pilot test an instrument Can be used effectively when the researcher needs to obtain expert feedback Use of purposive sampling to receive “expert feedback” on a newly developed measurement instrument

Donna B. Konradi, DNS, RN, CNE Probability Sampling Random Stratified random Cluster Systematic

Donna B. Konradi, DNS, RN, CNE Probability Sampling Stratified random sample Population is divided into two or more strata Subjects are selected at random from each strata Proportionate sampling Example: males in nursing = 8% of the population; Hence 8% of the study sample will be male (N = 1000; N = 80) Disproportionate sample Example: it may be inappropriate to make generalizations to all male nurses based on small male sample (n = 80), hence the number of males included in the study will be increased (n = 200)

Donna B. Konradi, DNS, RN, CNE Representativeness Need to evaluate Setting Characteristics of the subjects (age, gender, ethnicity, income, education) Distribution of values on variables measured in the study

Donna B. Konradi, DNS, RN, CNE Sampling Error Definition: difference between the population mean and the mean of the sample

Donna B. Konradi, DNS, RN, CNE Sampling error Population Sample Population mean Sample mean Sampling Error

Donna B. Konradi, DNS, RN, CNE Cluster Sampling Researcher desires a sample of all licensed nurses in the United States Contacts all the State Boards of Nursing for lists of licensed nurses Selects at random X nurses from each state Possible errors Over or under representation (nurses in states with fewer licensed RNs will have a greater chance of being selected for the study than nurses in states with more licensed RNs)

Donna B. Konradi, DNS, RN, CNE Systematic Sampling Selection of every x th case for inclusion in the study The first case is selected at random and from that point on every x th case is included If a standard list is used, the interval can be determined by dividing the total number on the list by the number of desired study participants

Donna B. Konradi, DNS, RN, CNE Systematic Sampling Example  The phone book has 225,000 listings (N)  The researcher needs a sample of 500 (n)  The sampling interval k is determined  N  n = k  225,000  500 = 450  k = 450

Donna B. Konradi, DNS, RN, CNE Systematic Sampling Example  The researcher selects a number between 1 and 450 at random (227)  The first participant will be the 227 th listing, the 2 nd participant will be listing 677 (227 + k); 3 rd participant will be listing 1127 (677 + K)  Which listing will be the 4 th participant?

Donna B. Konradi, DNS, RN, CNE Advantage of Probability Sampling A reliable method of obtaining a representative sample in a quantitative study Avoids conscious or unconscious bias in sampling All in the population have an equal chance of being included in the study Allows the researcher to estimate the degree of sampling error

Donna B. Konradi, DNS, RN, CNE Disadvantage of Probability Sampling Expensive Inconvenient

Donna B. Konradi, DNS, RN, CNE Sample Size Power analysis Effect size (very subtle to very obvious) Number of participants Error Assess Sample size Method of sample selection

Donna B. Konradi, DNS, RN, CNE Power Analysis Standard power of 0.8 Level of significance Alpha =.05,.01,.001 Effect size.2 small,.5 medium,.8 large Sample size

Donna B. Konradi, DNS, RN, CNE Determining Sample Size in Qualitative Research Consider the following elements: Nature of the topic Quality of the data Scope of the study Study design

Donna B. Konradi, DNS, RN, CNE Critiquing the Sample Identify the sample criteria. Judge the appropriateness of the sampling criteria. Identify the sampling method. Was the sample heterogeneous or homogeneous?

Donna B. Konradi, DNS, RN, CNE Critiquing the Sample Was the sample size identified? Was the percent of subjects consenting to participate addressed? Was the sample mortality identified? Was the sample size adequate?

Donna B. Konradi, DNS, RN, CNE Critiquing the Sample Identify Accessible population Target population Evaluate Appropriateness of generalization in quantitative studies

Donna B. Konradi, DNS, RN, CNE Critiquing the Sample Did the researcher successfully implement the sampling plan? How effective was the sampling plan in achieving representativeness? In what ways was the sample not representative? Was power analysis data reported?