Populations and Samples Nursing 200W Chapter 9

Objectives Define population, sample and sampling. Define population, sample and sampling. Distinguish between nonprobability and probability samples and compare their advantages and disadvantages. Compare sampling for quantitative and qualitative research methods. Compare sampling for quantitative and qualitative research methods. Evaluate the appropriateness of the sampling method and sample size used in studies.

Introduction Sampling - process of selecting a group of elements (people, events, behaviors) that represent a population. Sampling - process of selecting a group of elements (people, events, behaviors) that represent a population. What is the purpose of sampling? What is the purpose of sampling? Selecting a portion of the population to represent the population. A sample is a subset of population elements.Selecting a portion of the population to represent the population. A sample is a subset of population elements.

Definitions Population – the entire set of individuals (or elements) who meet the sampling criteria. This is the “P” in the PICO question. Population – the entire set of individuals (or elements) who meet the sampling criteria. This is the “P” in the PICO question. Target populationTarget population Accessible population – portion of the target population to which the researcher has reasonable access Accessible population – portion of the target population to which the researcher has reasonable access

Definitions Sample – the selected group of people (or elements) from whom data are collected for a study. Sample – the selected group of people (or elements) from whom data are collected for a study. Element – one individual unit of a population Element – one individual unit of a population Person (adolescent males) – in nursing research humans are the most common elements.Person (adolescent males) – in nursing research humans are the most common elements. Event (hospital admission)Event (hospital admission) Behavior (walking 30 minutes per day)Behavior (walking 30 minutes per day)

Accessible Population (target) Sample Element

Sampling Criteria Criteria that specify characteristics of the target population. Criteria that specify characteristics of the target population. Inclusion criteria: desirable characteristics Inclusion criteria: desirable characteristics Able to speak/read EnglishAble to speak/read English Diagnosed with diabetes within last monthDiagnosed with diabetes within last month 18 to 45 years old18 to 45 years old Exclusion criteria: undesirable characteristics; will not be included Exclusion criteria: undesirable characteristics; will not be included Mental illness or cognitive dysfunctionMental illness or cognitive dysfunction Age <18 yearsAge <18 years Unable to read or speak EnglishUnable to read or speak English

Sampling Goal: representativeness of population Goal: representativeness of population Target population – the entire pop. In which a researcher is interested.Target population – the entire pop. In which a researcher is interested. Accessible population – the portion of the target pop. that is accessible to the researcher.Accessible population – the portion of the target pop. that is accessible to the researcher. Representative Sample – one that closely approximates the population; this improves generalizability of finding to the target population. Representative Sample – one that closely approximates the population; this improves generalizability of finding to the target population. Sampling bias – overrepresentation or underrepresentation of some segment of the population; decreases generalizability Sampling bias – overrepresentation or underrepresentation of some segment of the population; decreases generalizability

Variations in Sampling Random variation Random variation Expected differences in values of a variable within a sampleExpected differences in values of a variable within a sample Variation around a meanVariation around a mean Humans are unique and different!Humans are unique and different! Systematic variation (bias) Systematic variation (bias) Result of sampling errorResult of sampling error Occurs when sample is not representative of populationOccurs when sample is not representative of population

Sampling Error Measured difference between the population mean and the sample mean Measured difference between the population mean and the sample mean Representativeness of sample (the closer it is to the population) minimizes sampling error Representativeness of sample (the closer it is to the population) minimizes sampling error ERROR PopulationSample Pop. meanSample mean

Types of Systematic Variation Refusal rate: percentage of subjects that declined to participate in the study Refusal rate: percentage of subjects that declined to participate in the study 80 subjects approached and 4 refused80 subjects approached and 4 refused 4  80 = 0.05 (5% refusal rate)4  80 = 0.05 (5% refusal rate) Acceptance rate: percentage of subjects that consented to be in the study Acceptance rate: percentage of subjects that consented to be in the study 80 subjects approached and 76 accepted80 subjects approached and 76 accepted 76  80 = 0.95 (95% acceptance rate)76  80 = 0.95 (95% acceptance rate)

Sampling Mortality Loss of subjects from the study Loss of subjects from the study Can be from one group (experimental or control)Can be from one group (experimental or control) One group may be more likely to withdraw than the other.One group may be more likely to withdraw than the other. Can result in bias/systematic variation Can result in bias/systematic variation Mortality should be reported by researchers Mortality should be reported by researchers

Sampling Plan – Quantitative Studies Strategies used to obtain a sample for a study Strategies used to obtain a sample for a study Types of sampling plans Types of sampling plans 1. Probability 1. Probability 2. Non-Probability 2. Non-Probability

1. Probability Sampling Random selection of elements from a population Random selection of elements from a population Selection is up to chance Selection is up to chance Types Types Simple randomSimple random Stratified randomStratified random Cluster randomCluster random SystematicSystematic

Simple Random Sampling Most basic method Most basic method Each element in target population has equal chance of being in the sample Each element in target population has equal chance of being in the sample Improves representativeness of sampleImproves representativeness of sample Examples Examples Draw namesDraw names Random number tableRandom number table Computer programComputer program

Stratified Random Sampling Ensures all levels of identified variables are adequately represented in the sample Ensures all levels of identified variables are adequately represented in the sample Need a large population to start with Need a large population to start with Variables often stratified Variables often stratified Age, gender, socioeconomic statusAge, gender, socioeconomic status Types of nurses, sites of careTypes of nurses, sites of care

Systematic Sampling Select every K th individual from a list, using a randomly selected starting point Select every K th individual from a list, using a randomly selected starting point e.g. every 10 th person from a liste.g. every 10 th person from a list Researcher must know number of elements in the population and the sample size desired Researcher must know number of elements in the population and the sample size desired

Point of Clarification Random sampling is NOT the same as random assignment Random sampling is NOT the same as random assignment In random sampling, each element in the population has an equal independent chance of being selected. In random sampling, each element in the population has an equal independent chance of being selected. Random assignment – assigning members of sample to experimental or control group as seen in a randomized control trial. Random assignment – assigning members of sample to experimental or control group as seen in a randomized control trial.

Sampling – Key Points Probability sampling is the only method for obtaining a highly representative sample Probability sampling is the only method for obtaining a highly representative sample Reduces biasReduces bias Improves ability to generalize results of quantitative studiesImproves ability to generalize results of quantitative studies

2. Non-probability Sampling Not every element of population has opportunity for selection Not every element of population has opportunity for selection Decreases representativenessDecreases representativeness Reasons commonly used? Reasons commonly used? Cost or time savingsCost or time savings Types Types ConvenienceConvenience QuotaQuota

Convenience Sampling “Accidental” sampling “Accidental” sampling Whomever is available and willing to give consent Whomever is available and willing to give consent Weakest approach – high risk of bias! Weakest approach – high risk of bias! Quasi-experimental studies Quasi-experimental studies use convenience sampleuse convenience sample subjects are randomly assigned to groupssubjects are randomly assigned to groups

Quota Sampling Similar to stratified random sampling Similar to stratified random sampling Deliberately attempt to include subjects from sub-groups who may be under-represented in the convenience sample Deliberately attempt to include subjects from sub-groups who may be under-represented in the convenience sample Goal: replicate the proportions of subgroups present in the population Goal: replicate the proportions of subgroups present in the population Better than convenience sampling (less bias) Better than convenience sampling (less bias)

Sampling Methods: Qualitative Studies Representativeness of sample less important Representativeness of sample less important Goal is to uncover meanings, not to generalize findings Goal is to uncover meanings, not to generalize findings Methods Methods 1. Purposive 2. Networking 3. Theoretical

1. Purposive Conscious selection of sample by researcher Conscious selection of sample by researcher Selection based on specific characteristics Selection based on specific characteristics Weakness – no way to evaluate bias on part of the researcher Weakness – no way to evaluate bias on part of the researcher

2. Networking (Snowballing) Use social networks to connect with poorly accessible populations Use social networks to connect with poorly accessible populations Examples: Examples: AlcoholicsAlcoholics Homeless personsHomeless persons Breastfeeding momsBreastfeeding moms Ethical concerns Ethical concerns

3. Theoretical Sampling Used in grounded theory studies Used in grounded theory studies Selection of participants based upon their knowledge of or experience with the phenomenon of interest Selection of participants based upon their knowledge of or experience with the phenomenon of interest Sample not pre-determined Sample not pre-determined Characteristics may change as data collection evolvesCharacteristics may change as data collection evolves

Sample Size In general – a larger sample is more representative of the population In general – a larger sample is more representative of the population Does size really matter? Does size really matter? Inadequate sample size may influence ability to detect statistically significant differences between groups – this is important in quantitative studies.Inadequate sample size may influence ability to detect statistically significant differences between groups – this is important in quantitative studies.

Power Analysis Definition: statistical procedure used to estimate sample size needed to correctly reject a null hypothesis Definition: statistical procedure used to estimate sample size needed to correctly reject a null hypothesis Usually done prior to sampling Usually done prior to sampling May be done after data analysis, if unable to reject null hypothesisMay be done after data analysis, if unable to reject null hypothesis

Factors That Influence Adequacy of Sample Size 1. Effect size 2. Type of research design 3. Number of variables 4. Sensitivity of measurement tools 5. Data analysis technique

Effect Size Strength of relationship between variables or magnitude of difference between groups Strength of relationship between variables or magnitude of difference between groups Detecting a large effect size (strong relationship or big difference) Detecting a large effect size (strong relationship or big difference) EasierEasier Smaller sample neededSmaller sample needed Detecting a small effect size Detecting a small effect size More difficultMore difficult Larger sample neededLarger sample needed

Effect Size Increasing the sample size also increases the effect size Increasing the sample size also increases the effect size More likely to detect differences between groups with larger sample More likely to detect differences between groups with larger sample Small effect <0.3 Small effect <0.3 Medium effect 0.5 Medium effect 0.5 Large effect >0.6 Large effect >0.6

Sample Types in Research Designs Descriptive Descriptive QuestionnairesQuestionnaires Large samples neededLarge samples needed Correlational Correlational Studies with more variables need larger samplesStudies with more variables need larger samples Experimental/Quasi-experimental Experimental/Quasi-experimental More controlMore control Smaller samples (groups)Smaller samples (groups)

Two Issues to Consider when Critiquing a Sampling Plan 1. Consider whether the researcher has adequately described the sampling strategy. 1. Consider whether the researcher has adequately described the sampling strategy. 2. Consider whether the researcher made good sampling decisions. 2. Consider whether the researcher made good sampling decisions. This can be critiqued by considering the following…

Critiquing Sampling (Quantitative) Type of sampling Type of sampling Sample size Sample size Rationale for sample size (power analysis)Rationale for sample size (power analysis) Inclusion & exclusion criteria Inclusion & exclusion criteria Method of recruiting Method of recruiting Description of sample characteristics Description of sample characteristics Refusal & sample mortality Refusal & sample mortality

Critiquing Sampling (Qualitative) Inclusion & exclusion criteria Inclusion & exclusion criteria Method of recruiting Method of recruiting Refusal and sample mortality Refusal and sample mortality Characteristics of sample Characteristics of sample “Quality” of participants – articulate, well-informed, willing to share “Quality” of participants – articulate, well-informed, willing to share Sample size determined by saturation Sample size determined by saturation