Textbook Notes (367,969)
Psychology (9,695)
PSYB01H3 (581)
Chapter 5

# Chapt 5 - psyb01.docx

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Department
Psychology
Course
PSYB01H3
Professor
David Nussbaum
Semester
Fall

Description
Chapt 5 census: studying the entire population of interest cross-population generalizability: findings from one popular in one country to one population in another country - need to compare results obtained from samples of different populations population: the entire set of individuals or other entities to which 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 error: the difference between the characteristics of a sample and the characteristics of the population from which it was selected - the larger the sampling error, the less representative the sample, the less generalizable the findings obtained from that sample inferential statistics (the 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 random sampling error (chance sampling error): differences between the population and the sample that are due only to chance factors (random error), not to systematic sampling error. Random sampling error may or may not result in an unrepresentative sample. The magnitude of sampling error due to chance factors can be estimated statistically sample statistic: the statistic computed from sample data (i.e mean) is identical to the population parameter: the statistic computed for the entire population - the value at the peak of the bell curve represents the norm for the entire population - population parameter also may be termed true value for the statistic in that population - a sample statistic is an estimate of a population parameter Probability of selection: the likelihood that an element will be selected from the population for inclusion in the sample. As the size of the sample decreases as a proportion of the population, so does the probability of selection - rely on a random, or chance, selection procedure -> flipping a coin to decide which of the two people wins and which one loses. Heads and tails are equally likely to turn up in a coin toss -> equal chance of winning. That chance is their probability of selection PROBABILITY SAMPLING METHODS: lets us know in advance how likely it is that any element of population will be selected for the sample 1. Simple random sampling: requires some procedure that generates numbers of otherwise identifies cases strictly on the basis of chance - random-digit dialing: a machine dials random numbers within the phone prefixes corresponding to the area in which the survey is to be conducted 2. Systematic random sampling: a variant of simple random sampling - the first element is selected randomly from a list or from sequential files, and then every nth element is selected - one problem to watch out for is periodicity: the sequence varies in some regular periodic pattern 3. Stratified random sampling: uses info known about the total population prior to sampling to make the sampling process more efficient proportionate stratified random sampling: ensures that the sample is selected so that the distribution of characteristics in the sample matches the distribution of the corresponding characteristics in the population disproportionate stratified random sampling: calculates separate statistical estimates 4. Cluster sampling: used when a sampling frame of elements is not available as often is the case for large populations spread out across a wide geographic area or among many different organizations cluster: naturally occurring, mixed aggregate of elements of the population ex. schools could serve as clusters for sampling students, blocks could serve as clusters for sampling city residents, countries could serve as clusters for sampling the general population and businesses could serve as clusters for sampling employees NON PROBABILITY SAMPLING METHODS: methods that do not let us know in advance the likelihood of selecting each element - does not use random selection procedure - elements are selected for availability sampling b/c they’re available or easy to find (also known as haphazard, accidental or convenience sampling) ex. inviting interested students to do a questionnaire, including a survey inside a magazine (with the plea to subscribers to complete and return it) 1. Quota sampling - is intended to overcome the most obvious flaw of availability sampling that the sample will just consist of whoever and whatever is available without any concern for its similarity to the population of interest - are set to ensure that the sample represents certain characteristics in proportion to their prevalence in the population Differences between stratified and quota sampling methods stratified: - unbiased (random) selection of cases - sampling frame required - ensures representation of key strata quota: - biased selection of cases - does not require sampling frame - ensures representation of key strata WRITING SURVEY QUESTIONS When writing survey questions, avoid using double negatives ex. “Do you disagree that rich people are not happy?” - avoid using negative words like “don’t” and “not” - double barreled questions also guarantee uninterpretable results b/c they actually ask two questions but allow one answer (only allowing “yes” and “no” answers when some respondents may want to agree with both answers) - filter questions creates skip
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