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Chapter 5

PSYB01 Chapter 5.doc

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University of Toronto Scarborough
David Nussbaum

Chapter 5: Sampling and Survey Research ← Substantive Theme: Happiness ← Ed Diener’s Satisfaction With Life Style, a widely used measure of happiness ← Sample – a group of units selected from a larger group that is known as the population ← Survey Research – research in which information is obtained from a sample of individuals through their responses to questions about themselves or others ← Selecting Research Participants ← When studying happiness, we should be concerned with who we study and when we study them • Sampling populations and Census – study the entire population of interest BUT it’s difficult to account for all people ← Sample Planning ← To plan a sample or assess a sample we must answer 2 questions: • From what population will you select cases? • What method will you use to select cases from this population? ← Define the Population ← US population studies found: • Students, disabled persons, elderly and adults samples report similar levels of happiness, as do men and women, African Americans and whites. • Happiness varies in different countries around the world • Satisfaction with different life domains varies across cultures • We CAN’T generalize happiness findings from one population in a country to another ← Cross Population Generalizability – extent to which result of a population can be applied to another country; tested by comparing results from samples of the different populations. ← Westerners perceive scenes by focusing on distinctive objects and Asian cultures view scenes holistically. ← Define Sample Components ← Population – the entire set of individuals of other entities to with study findings are to be generalized ← Elements – the individual members of the population whose characteristics are to be measured ← Sampling Frame – a list of all elements in a population ← Representative Sample – a sample that looks like the population from which it was selected in all respects potentially relevant to the study. • The distribution of characteristics among the elements of a representative sample is the same as the distribution of those characteristics among the total population. • In an unrepresentative sample, some characteristics are overrepresented or underrepresented. • Random selection of elements maximizes sample representativeness. ← Sampling generalizability depends on Sampling Error – difference between characteristics of sample and that of the population • The larger the errorless representative of the population ← Estimating Sampling Error: • Inferential Statistics – tool for calculating sampling error; a mathematical tool for estimating how likely it is that a statistical result based on data from a random sample is representative of the population from which the sample is assumed to have been selected. • Sampling Distribution – represent all possible samples that we could have drawn; many are ‘normal’ or in the mean, while others are deviant creating the normal shape; most frequent value from sample statistic – statistic from the sample (mean) or population estimate – Is identical to corresponding population parameter – statistic from the population o Normal shape created by Random Sampling Error – variation owing purely to chance; differences between the population and the sample that are due to chance factors not to systematic sampling error. It may or may not result in an unrepresentative sample. The magnitude of sampling error due to these factors can be estimated statistically ← Sampling Methods ← Probability Sampling Methods – allow us to know in advance the likelihood of selecting each element; use random selection and has no systematic bias = Probability of selection is known. ← Non-Probability Sampling Methods – sampling methods that don’t let us know in advance the likelihood of selecting each element ← Probability of Selection – the likelihood that an element will be selected from the population for inclusion in the sample. In a census of all elements of a population, the probability that any particular element will be selected is 1 (by tossing a coin), the probability of selection for each element is .5. As the size of the sample decreases as a proportion of the population, so does the probability of selection. ← Random Sampling – cases are selected only on the basis of chance; BUT there is much control in this sampling to ensure it’s only chance that is working on their sampling. ← Probability Sampling Methods ← Probability sampling methods are those in which the probability of selection is known and is not zero (so there is some chance of selecting each element). ← Systematic Bias – having NONE means nothing but chance determines which elements are included in the sample. • Good for generalization whereas non-probability samples aren’t good for it. ← Sampling representativeness (or randomness) is more important than the size of the sample. ← There are 4 methods for drawing random samples: 1. Simple Random Sampling – every sample element is selected only on the basis of chance, through a random process a. Generates numbers or identifies cases on the basis of chance b. Random-Digit Dialing – a procedure in which a machine dials random numbers within the phone prefixes corresponding to the area in which the survey is to be conducted c. The probability of selection in a true simple random sample is equal for each element d. Eg. flipping a coin or rolling a dice 2. Systematic Random Sampling – sample elements are selected from a list or from sequential files, with every nth element being selected after the first element is selected randomly within the first interval a. Periodicity – sequence varies in some regular, periodic pattern; this is problem to watch out for Eg. house on corner of every block different b. Sampling Interval 3. Stratified Random Sampling – sample elements are selected separately from population strata that are identified in advance by the researcher a. All elements are distinguished according to value on relevant characteristic which forms sampling strata Eg. race b. Then elements are sampled randomly from within strata; ensures right representation Eg. randomly sampling within a race c. Stratifies sampling can be either: i. Proportionate Stratified Sampling – ensures sample is selected so that the distribution of characteristic in the sample matches the population ii. Disproportionate Stratified Sampling – elements are selected from strata in different proportions from those that appear in population to study them 4. Cluster Sampling – useful when sampling frame is not available or for large populations spread out across a wide area or among many organizations; elements are selected in two or more stages, with the first stage being the random selection of naturally occurring clusters and the last stage being the random selection of elements within clusters a. Cluster – a naturally occurring, mixed aggregate of elements of the popula
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