Ch 11- Populations and Samples

Probability Sampling

Uses random sampling procedures Selects sample from elements or members of population Types - Simple - Stratified - Cluster - Systematic

Nursing Research Studies

Usually limited to small convenience samples Generalizations to total population difficult Small sample sizes warrant replication studies. Similar results from replication help with generalization

Random Sampling or Random Assignment

Random sampling: Each subject has equal probability of being included. Random assignment: Procedure to ensure that each subject has equal chance

Sample Selection

Representation of the population Method for getting the sample Sample size for the study

Simple Random Sampling

- Type of probability sampling - Importance of this sampling: Equal chance of selection and Independent chance of selection

Disadvantages of Stratified Random Sampling

Requires accurate knowledge of population May be costly to prepare stratified lists Statistics are more complicated

Approaches for Stratified Random Sampling

Proportional stratified sampling: Determine sampling fraction for each stratum Ensure that this stratum is equal Proportion in total population Disproportional stratified sampling: Determine stratum is represented Used when strata are very unequal Note the key word disproportional

Nonprobability Sampling Procedures

Advantages: Time Money Disadvantages: Nonrandom Not able to generalize findings

Research Data

Based on voluntary responses from subjects Biased sample occurs if subjects do not participate

Sampling Bias

Bias when samples are not carefully selected All nonprobability sampling methods have it. May occur in probability sampling methods - Subjects decide not to participate when chosen. - Final sample is now not representative of population

Disadvantages of Cluster Random Sampling

Causes a larger sampling error Requires each member assignment of population to cluster Uses a more complicated statistic analysis

Convenience Sampling

Chooses the most readily available subject or object Does not guarantee that the subject or object is typical of the population

Disadvantages of Simple Random Sampling

Complete listing of population is necessary It is time consuming to use.

Population

Complete set of persons or objects Common characteristic Of interest to the researcher Goal of sampling in quantitative research is to be able to make generalizations about the population from which the sample came from

Target Population

Definition: Entire group of people or objects People or objects meet designated set of criteria. Generalization of the findings

Accessible Population

Definition: Group of people or objects Researcher has access to them Must meet the desginated criteria of interest to the researcher

Advantages of Systematic Random Sampling

Easy to draw sample Economical Time-saving technique

Sample Terms

Element - Single member of a population Sampling frame - Listing of all elements of a population - Elements or members of a population are selected from a sampling frame

Tell whether the following statement is true or false: Researchers usually sample from the target population.

False Researchers usually sample from the accessible population but should identify the target population to which they want to generalize their results.

Tell whether the following statement is true or false: The aggregate of cases in which a researcher is interested is called a sample.

False The aggregate of cases in which a researcher is interested is called a population. A sample is selection of a portion of the population to represent the entire population.

Power Analysis

Helps to determine sample size May prevent type II error Helps to detect statistical significance Low power; type II error high External funding sources require it. Helps determine the optimum sample size

Steps for Simple Random Sampling

Identify the accessible population or list of elements Choose the method for getting the sample Note how easy it is through this example - Names of elements on slips of paper - Papers are placed into a hat. - Individual draws a slip of paper. - Individual continues until sample number is met

Sampling Error

Random fluctuations in data Not under the control of the researcher Chance variations occur when sample is chosen

Advantages of Stratified Random Sampling

Increases probability of being representative Ensures adequate number of cases for strata

Random Selection

Key word in sample selection Every subject has an equal chance

Cluster Random Stratified Sampling

Large groups or clusters, not people, are selected from population. Simple, stratified or systematic random sampling may be used during each phase of sampling

Advantages of Simple Random Sampling

Little knowledge of population is needed. Most unbiased of probability method Easy to analyze data and compute errors

Classification of Research Studies

Longitudinal Cross-sectional

Longitudinal Versus Cross-Sectional Studies

Longitudinal studies: Accurate means of studying changes over time Studies take a long time to perform. Cross-sectional studies: Less expensive Take less time Easier to conduct

Larger Sample Sizes

Many uncontrolled variables are present. Small differences are expected in members. Population must be divided into subgroups. Dropout rate among subjects is expected to be high. Statistical tests are used that require a minimum sample size

Sample Size

No simple rules Qualitative studies use much smaller samples than quantitative studies. Factors to consider for sample sizes in quantitative studies: - Homogeneity of population - Degree of precision desired by the researcher - Type of sampling procedure that is used Central limit theorem Sampling distribution of the mean

Probability Sampling

Researcher hopes that the variables of interest in the population will be present in the sample in the same proportion as would be found in the total population No guarantee Helps with inferential statistics with greater confidence Probability sampling allows researcher to estimate the chance that any given population element will be included in the sample Gives the ability to generalize the findings

Nonprobability Sampling

Sample elements are chosen nonrandomly. Produces biased sample Each element of the population may not be included in the sample. Restricts generalizations made about study findings

Volunteers As Subjects

Sample selection varies. - Subjects volunteer for a study. - Researcher approaches subjects

Disadvantages of Systematic Random Sampling

Samples may be biased. After first sample is chosen, no longer equal chance

Advantages of Cluster Random Sampling

Saves time and money Arrangements made with small number sampling units Characteristics of clusters or population can be estimated

Longitudinal Research Study

Subjects are followed over time. A cohort study is one example. Subjects are studied based on: - Similar age group - Similar background Data are gathered: - Same subjects - Several times - Tells influence of time

Cross-Sectional Study

Subjects checked at one point in time Data collected from groups of people Data may represent differences in: - Ages - Time periods - Developmental states - Important considerations

Sample

Subset of a population Sample represents the population If selected properly, the researcher is able to make claims about the population based on data from the sample alone

Tell whether the following statement is true or false: Probability sampling involves random selection of elements.

True Probability sampling involves random selection of elements.

Snowball Sampling

Type of convenience sampling method Study subjects recruit other potential subjects. May be called network sampling May find people reluctant to volunteer

Purposive Sampling

Type of nonprobability sampling Researcher uses personal judgment in subject selection. Each subject chosen is considered representative of population. Many qualitative studies use this technique.

Quota Sampling

Type of nonprobability sampling Researcher selects sample to reflect characteristics. - Examples of stratum Age Gender Educational background Number of elements in each stratum Number is in proportion to size of total population

Systematic Random Sampling

Type of probability sampling Every kth element is selected. Process: - Obtain a listing of population - Determine the sample size - Determine the sampling interval (k = N/n) - Select random starting point - Select every kth element

Stratified Random Sampling

Type of probability sampling Population is divided into subgroups or strata Simple random sample taken from each strata

Populations and Samples

Unable to study whole populations So...we sample from the population Quantitative research prefers populations Qualitative research focuses on individuals

Research Studies

Use voluntary subjects Follow the ethics of research - Subjects must voluntarily agree - Subjects may refuse to participate

Threefold Randomization Process

Used for experimental studies Helps represent the ideal study procedure Steps to ensure the process: - Subjects randomly selected from population - Subjects randomly assigned to groups - Experimental treatments randomly assigned to groups

Nonprobability Sampling

Uses nonrandom sampling procedures Selects sample from elements or members of population Types - Convenience - Quota - Purposive