Sampling Methods in Quantitative and Qualitative Research

Sampling Sampling in Quantitative Research

Sampling in Quantitative Research Population The entire aggregation of cases that meets a specified set of criteria Eligibility criteria determines the attributes of the target population Sampling The process of selecting a portion of the population to represent the entire population

Sampling in Quantitative Research Accessible population The population of people available for a study Target population The entire population in which the researcher is interested and to which he/she wants to generalize the results

Sampling Plans A sample is a subset of the population A sample should be representative and similar to the population to be studied

Sampling Plans Strata Subdivisions of the population based on specific characteristics

Samples vs. the Population More economical More efficient More practical

Problems Using Samples Sampling bias Over-representation or under-representation of some characteristic of the population Not representative of the population being studied

Sampling Plans Types of sampling plans Nonprobability sample Convenience sampling Purposive sampling Quota sampling Probability sample Random sampling Cluster sampling Systematic sampling

Sampling Plans Nonprobability sample The selection of the sample from a population using non-random procedures Convenience sampling Purposive sampling Quota sampling

Sampling Plans Nonprobability sample Convenience sampling (accidental sampling) Selection of the most readily available people as participants in a study Risk of bias and errors as sample may be atypical of the population Weakest form of sampling Snowball sampling (network sampling) The selection of participants by means of referrals from earlier participants

Sampling Plans Nonprobability sample Quota sampling Researcher pre-specifies characteristics of the sample to increase its representativeness This is used so sample includes an appropriate number of cases from each stratum (subpopulation) Usually use age, gender, ethnicity, socioeconomic status, and medical diagnosis

Sampling Plans Nonprobability sample Purposive sampling (judgmental sampling) Researcher selects study participants on the basis of personal judgement about which ones will be most representative or productive Handpick cases, very subjective

Sampling Plans Nonprobability Sample Problems Are rarely representative of the target population But are convenient and economical

Sampling Plans Probability sample The selection of the sample from a population using random procedures Random selection – each element in the population has an equal, independent chance of being selected Should be representative of the population Random sampling Cluster sampling Systematic sampling

Sampling Plans Probability sample Simple Random sampling Listing the population elements Elements are assigned a number Table of random numbers is used to draw at random a sample

Sampling Plans Probability sample Stratified Random sampling Population divided into homogenous subsets Elements are selected at random Increases representativeness of the final sample

Sampling Plans Probability sample Stratified Random sampling Proportionate sample a sample that results when the researcher samples from different strata of a population in direct proportion to their representation in the population

Sampling Plans Probability sample Stratified Random sampling Disproportionate sample a sample that results when the researcher samples differing proportions of study participants from different strata that are comparatively smaller Used when comparison between strata of unequal membership size are desired

Sampling Plans Probability sample Cluster sampling (multistage sampling) A form of sampling in which large groupings are selected first, with successive subsampling of smaller units Used for large scale sampling where it is impossible to have a listing of all elements

Sampling Plans Probability sample Systematic sampling The selection of study participants such that every Xth person or element in a sampling frame or list is chosen Population is divided by the size of desired sample to obtain a sampling interval Sampling interval is the standard distance between the selected elements

Sampling Plans Sample Size (Quantitative Studies) Sample size The number of participants in a sample Use the largest sample possible The larger the sample, the more representative it is likely to be The larger the sample, the smaller the sampling error Large samples counter balance atypical values

Critiquing the Sampling Plan Did the researcher adequately describe the sampling plan Type of sampling used The population under study Number of participants Main characteristics of participants Number and characteristics of potential subjects Were good sampling decisions made Was the sample representative of the population

Critiquing the Sampling Plan Response rates The number of people participating in a study relative to the number of people sampled Nonresponse bias Differences between participants and those who declined to participate A bias that can result when a nonrandom subset of people invited to participate in a study fail to do so

Sampling in Qualitative Studies

Sampling in Qualitative Studies Uses small samples Non-random samples Sample design is emergent

Sampling in Qualitative Studies Types of Qualitative Sampling Convenience sampling (volunteer sample) Snowball sampling Purposive sampling (theoretical sampling, purposeful sampling) Researcher selects sample based on information needs which emerged from earlier findings

Sampling in Qualitative Studies Sample Size Sample size is based on informational needs Data saturation is sought Sampling to the point at which no new information is obtained and redundancy is achieved

Sampling in Qualitative Studies Evaluating Sampling Plans Based on: Adequacy Sufficiency and quality of the data the sample yielded Appropriateness Using the best informants for the sample, those who will provide the best information

Reference Loiselle, C. G., Profetto-McGrath, J., Polit, D. F., & Beck, C. T. (2011). Canadian essentials of nursing research. (Third Edition). Philadelphia: Lippincott, Williams & Wilkins.