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SOC 300 Midterm 2
| Term | Definition |
|---|---|
| longitudinal research | data collected over time, time must be central part of the research, capture data over extended period to observe/analyze changes over time; provide insights into development of individuals & effects of various factors on their lives |
| population | entire set of individuals to which study findings are to be generalized, target population is tied to a specific research question |
| quantitative vs qualitative research | less about methodological issues and more about ontological assumptions, quantitative are committed to objective while qualitative includes subjective |
| Induction | create knowledge claim, tied to patterns observed; leads to conclusions that are probable based on evidence but not absolutely certain |
| Deduction | prove/test knowledge claim, if conclusion doesn't hold true then you have evidence that the premise is false; leads to conclusions that are logically certain if premise is true |
| reductionist fallacy | make incorrect conclusion about group-level data based on individual-level data |
| ecological fallacy | erroneous use of group level data to make inferences about individual-level data |
| association | whether there's a relationship/correlation |
| time order | use logic/observation, theory to determine chronological order |
| Spuriousness | where two variables appear related because there is a third variable |
| mechanism | process that creates a causal connection between two variables |
| context | larger circumstances in which an outcome should be understood |
| pre-experimental design: one-shot case study | did something, see how doing later |
| pre-experimental design: static group comparison | do something, see how react but also see how people didn't do something to are doing |
| pre-experimental design: one group, pre-test, post-test design | see how doing, do something, see how doing now |
| classic experimental design | treatment group and control group, treatment-see how doing, do something, then see how doing later; control-see how doing, then see how doing later |
| Trend study | take different samples at different times, comes from same population |
| Cohort study | special type of trend study, study group of people who share a similar event |
| Panel Study | take measurement from same sample over time |
| Follow-up study | special type of panel study, start from pre-existing records, less generalizable |
| JIF | Journal Impact Factor, calculated by how many times articles from that journal are cited by other scholars divided by the number of articles published in the journal |
| reductionist fallacy | reduce group level variation to individual level data |
| challenges of panel study | people die, don't want to participate anymore, researcher fails to keep good records, researcher fails to interview respondent, researcher fails to locate respondent |
| operationalization | process of connecting abstract concepts to variables that can be measured or observed, involves assigning specific definitions, dimensions or characteristics to a concept so that it can be scientifically evaluated |
| Sample | subset of a population used to study the population as a whole |
| sampling frame | list of all elements or other units containing the elements in a population, want as large as possible, ex. list of all BYU students |
| target population | population of interest in which researcher wants to generalize results of study |
| standard error | the larger the sample then the more confidence we can have in the representativeness of the sample; the more homogenous the population the smaller the sample size needs to be |
| sample size depends on... | the nature of population (how diverse), purposes of the study, precision needed, resources available |
| more homogenous population | greater confidence in sample's representativeness |
| larger sample | greater confidence in sample's representativeness |
| measurement | process of associating numbers or symbols to observation obtained in research study, observations can be qualitative or quantitative, somewhat difficult to measure abstract characteristics, somewhat easier to measure concrete characteristics, |
| Science as objective | must have representative, unbiased sample, wants to be neutral and based on data |
| science as systematic | methods must be unbiased, must follow procedure that can be explained to and replicated by others |
| science as deterministic | goal is to explain relationship between data, large enough sample to be able to accurately see and understand patterns |
| coverage error | when sampling frame is inaccurate in how much of population is covered |
| qualitative research | depth, subjects are relevant to research project, nonprobability sample, gonna ask the people who know/have experience |
| quantitative research | breadth, want subjects that are representative of population, generalizability, probability sampling |
| Simple Random Sampling | probability sampling, every element on sampling frame is selected on basis of chance through random process, everyone has same chance, ex. random digit dialing, random number table |
| nonresponse error | when people just don't respond, can account for by oversampling group that you know is less likely to respond |
| systematic random sampling | probability sampling, sample elements are selected from list/sequential files w/ every nth element selected after first element is selected randomly |
| periodicity | error where sample frame occurs in regular or periodic pattern, can account for by randomizing the sample frame |
| cluster sampling | probability sampling, used when sampling frame is inaccurate/unavailable, randomly select clusters of people then randomly select elements within clusters |
| cluster | naturally occurring mixed aggregate of elements of the population |
| stratified random sampling | probability sampling, ensures various groups will be included, characteristic determines sampling strata, must be proportionate to the size of each stratum in the population |
| Availability sampling | nonprobability sampling, elements are selected on basis of convenience, also called convenience sampling, not generalizable and not needing to be |
| quota sampling | nonprobability sampling, talk to certain number of people from certain groups, set number to ensure sample represents certain characteristics in proportion to their prevalence in the population, relies on availability sampling methods |
| snowball sampling | nonprobability sampling, sample elements are selected as successive informants or interviewees identify them, get cases using referrals from one or few cases and then referrals from those cases and so forth, good way to get access to marginalized pop. |
| purposive sampling | nonprobability sampling, each sample element is selected for a purpose, usually because of the unique position of the sample elements |
| nominal | assign numbers to objects where different numbers indicated different objects, have no real meaning other than to differentiate. equivalent categories |
| ordinal | assign numbers to objects, have meaningful order, number indicates placement or order. equivalent, ordered categories |
| interval | numbers have order with equal intervals between adjacent categories. equivalent, ordered categories, standard unit of measurement |
| ratio | differences are meaningful plus ratios are meaningful and there is a true zero point. equivalent, ordered categories, standard unit of measurement, meaningful zero point |
| Hawthorne Effect | people change their behavior when they know they're being watched |
| Sampling Weight | statistical adjustment when sample is disproportionate to population, numbers researchers use to correct/rebalance data results when some groups are overrrepresented or underrepresented compared to the target population |
| unweighted data | some groups count more or less than they should |
| weighted data | results are readjusted so each group represents its true share of population, doesn't change actual responses just changes how much influence each response has |
| Nominal statistics | frequency, mode, percentages |
| Ordinal statistics | frequency, mode, order/rank, median, percentiles, percentages |
| Interval statistics | frequency, mode, percentages, median, percentiles, mean and standard deviation, variance, correlation coefficients |
| Ratio statistics | frequency, mode, median, percentiles, percentages, mean and standard deviation, variance, correlation coefficients, ratios, twice/half as much |
| conceptualization | process of defining of specifying concepts, involves defining/specifying what we mean when we using certain terms, purpose to refine/specify abstract concepts, first step in measurement process, rely on theory |
| operationalization | process by which researcher precisely specifies how concept will be measured, developing specific research definitions that will bring about empirical observations representing concept in real world, purpose: remove vagueness/make sure concepts measurable |
| concept | mental image that summarizes a set of similar observations, feelings or ideas; multifaceted, made up of constructs; ex. religiosity |
| construct | concept which has been deliberately invented for a special purpose; aimed at specific aspects of the larger concepts; ex. religious beliefs, religious commitments, religious behaviors |
| indicators | variables, measurement via a symbol to which numerals or values are assigned; measures that capture variation across our sample; ex. do you believe? how often do you attend...? |
Created by:
pworthen0723