Section V

Section V - Section V Studying Your Interests Data...

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Section V. Studying Your Interests: Data Gathering 1. Selecting Units to Observe: Subjects and Sampling Survey research is very concerned about representativeness (external validity), so sampling procedures are very important. Experimental research is more concerned about demonstrating causal relationships so control of extraneous variables (external validity) is of primary importance; usually subjects are obtained using non-probability techniques. A. Probabilistic Sampling Involves random sampling techniques Allows generalizations from a sample to a population with a known amount of error at a certain level of certainty (we are 95% certain that the sample estimate is off by no more than ± 3 percentage points) The most effective means for selecting representative subjects Avoids researcher biases. Population units are selected by chance and not by the researcher. Permits for estimates of sampling error Requires a sampling frame -- a listing of everyone (every object) in the population. Sample units are selected from this list such that all members of the list (sampling frame) have an equal and independent chance of being selected, i.e., units are selected randomly -- chance determines which population units end up in the sample Non-response bias (non-sampling error) is always a problem. If only a portion of the random sample responds, is the non-response random, or do the non-respondents constitute a particular segment of the intended population? Non-response can bias the results. Always compare sample characteristics (demographics, etc.) to known population characteristics to check for non-response bias Probabilistic sampling techniques: Simple random sampling Assign a single number to each element, not skipping any number in the process Use random number table to select elements for the sample Systematic sampling
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Every nth element in the total population list is selected for inclusion into the sample with a random starting point Stratified sampling Grouping members of a population into homogeneous strata before sampling Reduces degree of sampling error Multistage cluster sampling Used when population list does not exist Sample of members (clusters) is selected Members of the selected cluster are listed List of clusters is subsampled B. Nonprobabilistic Sampling Does not rely on random sampling techniques Should not be used to make generalizations to a larger population; while a purposive sample may be generalizable, you cannot be as sure as with random sampling When it is either impossible or unfeasible to select a probabilistic sample i.e., when there is no sampling frame of the population (e.g., drug abusers) Less valid than probabilistic sampling Practical Nonprobabilistic sampling techniques: Purposive Sampling: researcher uses own judgment in selection of members Chunk Sampling: a convenient group of people (a class of students etc.) are used as a
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Section V - Section V Studying Your Interests Data...

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