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SOC 380 Exam 2 Study Guide

by: Maddie Butkus

SOC 380 Exam 2 Study Guide SOC 380 001

Marketplace > Ball State University > Sociology > SOC 380 001 > SOC 380 Exam 2 Study Guide
Maddie Butkus
GPA 3.7
Introduction to Research Methods
Dr. Rachel Kraus

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About this Document

This study guide is extremely detailed and covers both book material and lecture material. It includes examples from class as well as examples from the book.
Introduction to Research Methods
Dr. Rachel Kraus
Study Guide
research methods, SOC 380, Exam 2, Study Guide, KRAUS
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This 0 page Study Guide was uploaded by Maddie Butkus on Thursday February 25, 2016. The Study Guide belongs to SOC 380 001 at Ball State University taught by Dr. Rachel Kraus in Winter 2016. Since its upload, it has received 63 views. For similar materials see Introduction to Research Methods in Sociology at Ball State University.

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Date Created: 02/25/16
Exam 2 Study Guide Conceptualization 0 Process of specifying what we mean by a term Helps translate portions of an abstract theory into testable hypotheses involving speci c variables In inductive research conceptualization is an important part of the process used to make sense of related observations EX workshop from class de ning simple words with partners Operationalization o Occurs after conceptualization Process of specifying the way that you will measure each concept Operational concepts in one or more variables IV 8 DV 0 Variable Criteria Categories must cover all possible answers Answers cannot t into more than one response category 0 Categorical Variables Nominal Variables Place case into categories with out natural ordering Assigned numerical value number means NOTHING EX race seX region no more or less no bigger or smaller than each other Ordinal Variables Allow us to determine order of categories but there is no quanti able distance between the categories EX agreement scales strongly agree agree neutral disagree strongly disagree distance between categories is not measureable Ratio Variables Variable we have xed absolute zero point We can rank cases we can say how far apart cases are and we can say that one value time as large as another value EX age height weight length of residency of clubs belong to income Example of Religion for all three types of variables 0 Nominal Religious Affiliation catholic Methodist Baptist Jewish etc Ordinal Religiosity how often you attend service never occasionally sometimes etc 0 Ratio how many times have you attended service in the last month 0 Levels of measurement 0 Validity extent to which a measure actually measures what we think it does Face Validity does the measurement make common sense Criterion Predictive Validity does our measure of a concept agree with a more direct or already validated measure of the same concept EX Standardized test scores amp college success Construct Validity the degree to which a measure relates to other variable as expected within a system of theoretical relationship EX if measure of marital satisfaction relates to measure of marital in delity as 1 goes up the other decreases the way you think they should construct validity Content Validity degree to which a measure covers the range of meaningsdimension with a concept 0 Does measuring someone s intelligence based only on standardized test scores pass content validity ls only addition a good measure of someone s math ability 0 Reliability extent to which a measure yields consistent responses on different occasions 4 Empirical Ways of Assessing Measurement amp Reliability 1 Test Retake method a Take the same measurement of two or more points in time 2 SplitHalf Method a Make more than one measurement of a concept 3 InterItem Method a Using a series of questions to measure the same concept 4 InterObserver Method a Using 2 different observers to measure the same concept 5 AlternateForms Reliability FROM THE BOOK a Procedure for testing the reliability of responses to survey questions in which subjects answers are compared after the subjects have been asked slightly different versions of the questions or when randomly selected halves of the sample have been administered slightly different versions of the question 0 The use of multiple methods to study one research question 0 Whatever group you want to learn something about 0 The entire set of individuals or other entities to which study ndings are to be generalized EX ALL the countries in the world 0 A subset of a population used to study it EX a subset of countries 0 It is impossible to study every country this narrows down a sample to testable measures 0 A list of all elements or other units containing the elements in a population EX a list of all countries 0 Each country is an element on the list of countries in the population 0 Means someone who has more or less of a Change to be Chosen for a sample 0 Occurs when some population Characteristics are over or underrepresented in the sample because of particular features of the method of selecting the sample Research in which information is obtained through responses from or information about all available members of an entire population 0 Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample 0 Probability of selection is known and is not zero so there I some change of selecting each element 0 These methods randomly select elements 8 therefore have no systematic bias nothing but chance determines which elements are included in the sample 0 This feature of probability samples makes them much more desirable than nonprobability samples when the goal is to generalize to a larger population Random samples simple strati ed 2 types cluster 0 Simple Random Sampling Identi es cases strictly on the basis of change Flipping a coin and rolling a die both can be used to identify cases strictly on the basis of change but these procedures are not very efficient tools for drawing a sample 0 Strati ed Random Sampling Ensures that various groups will be included First all elements in the population that is in the same sampling frame are distinguished according to their value on some relevant characteristics 0 EX army rank for instance generals captains privates o Proportionate Strati ed Sampling You plan to draw a sample of 500 from an ethnically diverse neighborhood Neighborhood population is 15 black 10 Hispanic 5 Asian 8 70 white If you drew a random sample you might end up with somewhat different percentages of each group BUT if you created sampling strat based on race and ethnicity you could randomly select cases from each stratum in exactly the same proportions as in the neighborhood population This is termed propitiate strati ed sampling because the percentages are proportional to the population 0 Disproportionate Strati ed Sampling The proportion of each stratum that is included in the sample is intentionally varied from what is in the population In the case of the sample strati ed by O ethnicity you might select equal numbers of cases from each racial or ethnic group 0 NOT proportional to percentages in population Cluster Sampling Broad to more narrow Creating clusters then sampling within the clusters Naturally occurring mixed aggregated of elements of the population 0 Sampling error is an error that occurs when using samples to make inferences about the populations from which they are drawn There are two kinds of sampling error random error and bias Random error is a pattern of errors that tend to cancel one another out so that the overall result still accurately re ects the true value Every sample design will generate a certain amount of random error Bias on the other hand is more serious because the pattern of errors is loaded in one direction or another and therefore do not balance each other out producing a true distortion Nonprobability samples snowball convenience quota purposive O O Often used in qualitative studies when researchers are unable to use probability selection methods Sometimes a probability sample is not feasible or generalizability isn t possible such as EX Internet data no way you can get a list of all FaceBook users or NonMainstream groups hard to track down Availability Sampling convenience sample Elements are selected for availability sampling because they re available or easy to nd Thus this sampling method is also known as haphazard accidental or convenience sample EX People on the street internet posts magazine surveys all at your convenience Quota Sampling Intended to overcome aw of availability sampling whoever of whatever is available without any concern for its similarity to the population of interest Quotas are set to ensure that the sample represents certain characteristics in proportion to their prevalence in the population Similar to strati ed sampling but quota sampling is not representative on any other characteristic 8 people chosen by rst come rst serve basis NOT random sample EX 2 men 2 women but 1 black man 8 1 white man and 1 black woman 8 1 white woman 0 Purposive Sampling Each sample element is selected for a purpose because of the unique position of the sample elements EX Studying the entire population of some limited group directors of shelters for homeless adults or a subset of a population sociology majors at ball state Purposive sample can also be quotkey informant survey which targets individuals are who knowledgeable about issue in research 0 Snowball Sampling Hard to reach or identify populations not sampling frame but the members of which are somewhat interconnected EX belly dancers know other belly dancers 0 Underground populations Critique of Snowball Sampling relying on your one person to give you names of others and they re all most likely going to have similar answers 0 Mailed Self Administered survey conducted by mailing a questionnaire to respondents who then take the survey by themselves Limitations Central problem is maximizing the response rate probably only a 30 return rate women often respond more Bene ts low cost available population 0 Group Administered Survey completed by an individual respondent who are assembled in a group Limitations seldom feasible because it requires captive audience feel coerced to participate less honestly Bene ts high response rate Telephone Survey Interviewers question respondents over the phone and record their answers 0 Limitations not being able to reach proper sampling units not getting enough successfully completed responses 0 Bene ts Available population basically everyone has a phone In Person Surveys interviewer questions respondents face to face and records their answers Limitations should always conduct the survey exactly the same with each participant presence of interviewer can make it hard for participant to answer questions Bene ts high response rates survey control solid responses Electronic Surveys sent and answered through email or on the web Limitations coverage issue cannot reach people without computersinternet Bene ts cover large population more comfortable to disclose information get more honest answers easy to conduct 0 A test under controlled conditions in which the IV is manipulated by the experimenter in order to study the effects of that variable on the dependent variable 0 True Experiment subjects are assigned randomly to an O 0 experimental group that receives a treatment or other manipulation of the independent variable and a comparison group that does not receive the treatment or receives some other manipulation Outcomes are measure in a posttest Comparison Group groups that have been exposed to different treatments or values of then IV Experiments share 2 important features 1 Treatment experimental group group of subjects that receives the treatment or experimental manipulation 2 Control Groups a comparison group that receives no treatment Groups must be similar before IV is manipulated 0 IV needs to be manipulated many social variables can t be manipulated such as age rage or seX etc 0 Random assignment might be unfair or bias 0 Weak on external validity arti cially constructed circumstances may not hold in quotrealworld o Threats to internal validity History other events effect DV confounding variables Study subject changes endogenous change Differential attrition mortality problem that occurs in experiments when comparison groups become different because subjects in one group are more likely to drop out for various reasons compared with subjects in the other groups Selection Bias no random assignment groups not truly equal Instrumental decay deterioration over time of a measurement instrument resulting in increasingly inaccurate results Regression effect source of cause invalidity that occurs when subjects chosen because of their extreme scores on a dependent variable become less extreme on a posttest as a result of mathematical necessity rather than treatment Contamination experimental group or the comparison group is aware of the other group and it in uences the posttest result Double blind procedure an experimental method in which neither the subject nor the staff know which subjects are getting the treatment Placebo effect subjects receive treatment that they consider to be bene cial and improve as a result of that expectation a A type of contamination in experimental and quasiexperimental designs that occurs when members of the treatment group change relative to the dependent variable because their participation in the study makes them feel special


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