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Chapter 5

Chapter 5

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Department
Psychology
Course
PSYB01H3
Professor
Connie Boudens
Semester
Fall

Description
Chapter 5: Sampling and Survey Research  census : studying the entire population of interest; difficult to conduct if population is too big Sample Planning - Define the population  cross-population generalizability: when findings from one group/population holds true for another group/population. a.k.a external validity; to assess this, you need to compare results of groups. - ex: study found differences in how Eastern and Western cultures perceived identical scenes.  clearly define the population in question, and not give the impression that data came from a different group -Define sample components  elements: individual members of population. ex. students  sampling frame: list of all the elements in population. ex: list of students from the registrar’s office  population: entire set of individuals to which the findings are to be generalized  representative sample: sample that looks like that entire population; it has the same distribution of characteristics and does not over represent or under represent the population  sampling generalizability depends on the amount of sampling error: diff between characteristics of the sample and of the entire population it came from. - larger error = less representative of the sample = less generalizable findings  Estimating Sampling Error - inferential statistics: mathematical tool that estimates how likely a statistical result from a random sample represents the population it came from. - think of our sample as one possible sample out of many that could have been chosen - sampling distribution: a plot of the mean values of all the samples - a normal distribution is symmetric; due to random sampling error: error plainly due to chance and not to systematic errors. - sample statistic: the statistic computed ex: mean; it is an estimate of a population parameter -population parameter: value of statistic ( ie. mean) from the entire population - sample statistic and population parameter should be identical; peak of the bell curve represents the norm for the entire population - in a normal distribution, a predictable proportion will fall within certain ranges Sample Methods  Probability sample methods: method that allows us to know how likely an element will be chosen (probability of selection is known) - relies on chance. ex: flipping a coin = ½ or 50% chance → probability of selection - no systematic bias  Four Most Common Methods of Random Sampling: - Simple Random Sampling → strictly on the basis of chance → probability of selection is equal for each element → can be computer generated ex: random-digit dialing done by organization doing phone surveys - Systematic Random Sampling → variant of simple random sampling → first element is selected randomly from a list and then every nth element is selected. → convenient for drawing samples from sequentially arranged population elements ex: folders in a filing cabinet → periodicity: sequence varies in some regular periodic pattern - Stratified random Sampling → uses information known about the total population prior to sampling to make the process more efficient → first, all elements are distinguished according to some relevant characteristic ex: racial background → makes the sampling strata → second, elements are sampled from within these strata. ex: people are chosen from the racial background → size of each stratum must be known → this method assure appropriate representation of elements across strata → proportionate stratified sampling: distribution of characteristics within a selected sample matches the distribution of entire popu. ex: population ratio of undergrads to graduate students is 2:1 therefore # of students in both strata should replicate the ratio → disproportionate stratified sampling: ex. Eisenberg mental health study where he chose a larger portion of graduate students versus undergrads because graduate students’ mental health were “understudied”; able to calculate separate statistical estimates. - Cluster Sampling → useful when sampling frame is unavailable ie larger populations spread across wide area → ex: schools are clusters for sampling students, blocks are clusters for sampling city residents → list of clusters is easier to obtain than a list of each person in that cluster ex: list of schools is easier than a list of students in all of Toronto → researcher draws random samples from each cluster → cluster and stratified sampling are usually used in a combo  Non probability Sampling Methods: does not use random selection -ex: availability sampling: elements selected because they are easy to find or available. - Quota Sampling → quotas are set to ensure that the sample represents certain characteristics in proportion to the population → ex: in a town, 48% are male. You want a sample of 500 residents, therefore you look for 240 males (48% of 500). The percentages are you quotas. → differs from stratified sampling in that quota does not need a sampling frame and is not random. → problem of this method is the potential of respondents not wanting to participate ex: phone surveys Writing Survey Questions  Avoid Confusing Phrasing - be simple and direct - shorter words and shorter sentences; 20 or less than words - avoid double negatives: → Do you disagree that rich people are not happy? - avoid double-barreled questions: asking two questions in one is confusing → Agree or Disagree: Cars should be faster and safer. → can agree to one but disagree to another - make sure that respondents can relate to questions using filter questions and skip patterns. → Are you employed? If NO, skip to question 16.  Minimize the Risk of Bias - answers can be biased due to the phrasing  Avoid Making Either Disagreement or Agreement Disagreeable - when dealing with disapproved behaviour, respondents tend to be reluctant t
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