Soc 315 midterm

explanation of observed regularities or patterns

• definitions
• descriptions
• relational statements

connect two or more variables so that if you know the value of one variable you can convey information about the other variable

two variables that always go together in a particular way

two variables go together with some degree of regularity but the relationship isn't inevitable

explanations of specific social phenomena (ex. job satisfaction, criminal behaviour, suicide, etc.)

• broad sweeping historical explanations of societal change
• general and abstract
• (ex. feminism, structural-functionalism, etc).

• general/abstract idea
• category that serves to organize observations and ideas about some aspects of the social world

• theory -> observations/findings
• researcher comes up with a theory to explain a certain phenomenon, then deduces hypothesis 
• if the findings don't support the hypothesis, the theory needs to be revised or rejected

• observations/findings -> theory
• a theory is the outcome of research, not the starting point
• you are constructing a theory instead of testing a theory

• notions of what can be known and how knowledge can be acquired
• what is considered "acceptable knowledge"?

• an epistemological position
• affirms the importance of following the natural sciences 
• values empiricism (only phenomena confirmed by the senses can be accepted as knowledge, subject to empirical testing)
• "value-free" science - scientists from any place or situation if given the same data must be able to come to the same conclusions

• deduction: key purpose of theory is to generate hypotheses that can be tested, allow assessments of explanations of observed laws and principles
• induction: can also arrive at knowledge by gathering facts (systematic collection) which leads to a generalization of laws

• scientific statements (describe how and why certain social phenomena operate the way they do) belong in science
• normative statements (outline whether certain acts or social conditions are morally acceptable) belong in philosophy or religion

• sees the role of social scientists to group the subjective meanings of peoples actions
• people use common-sense constructs to interpret their lives, and these thoughts motivate their behaviour 
• interpretivists want to access the 'common-sense thinking' of people to interpret peoples actions and social world from the point of view of the actors
• alternative to the social science usually done by positivists

• branch of philosophy concerned with the nature of reality
• ex. "what kind of things have existence?"

• objectivist
• think there is no such thing as social reality
• relation to research: likely to emphasize on formal properties of organizations

• constructionist
• no objective social reality against which our conceptions and views of the world may be tested
• relation to research: likely to focus on active involvement of people in reality construction

standard by which we assess each other

uses numbers, statistics in collection and analysis of data

relies on words, non-numerical symbols

• theory
• practical considerations
• epistemology
• ontology
• politics
• values

the logic and techniques of collecting and analyzing data

broad structure that guides the collection and analysis of data

explanation that applies to humanity in general, not just the people in the study

• 1. correlation - proposed cause and effect must vary together
• 2. time order - the proposed cause must happen earlier in time than the proposed effect
• 3. non-spuriousness - alt. explanations for the correlation observed must be ruled out

• doesn't necessarily apply to others, but helps to explain why the actors of interest behave the way they do
• involve a detailed 'story' or description of people studied based on empathetic understanding

• reliability
• replicability
• validity

• measurement/construct validity
• internal validity
• external validity

• style of research that seeks to minimize the use of artificial methods of data collection
• social world should be disturbed as little as possible

• true or false
• ex. heat water to 100C and it will boil

• a prediction statement?
• ex. children from poorer homes are less likely to do well in school

• has to do with the stability/consistency of measurement
• an empirical issue
• do you get the same result if you measure again?

• consistency across researchers
• ex. clearly asked questions with no room for interviewers to modify questions

• consistency across observers
• clear rules for recording what you are observing

• are you really targeting what you want to be measuring?
• the adequacy of the measurement -> conceptual issue
• ex. does the number of rooms in a house measure wealth? not really

single case in the population

all cases about which you are seeking knowledge, or all the cases to which your conclusions are meant to apply

list of all possible elements in the population from which the sample will be selected

subset of a population, elements selected for investigation

• sample that is a microcosm of the population
• "represents" the essential characteristics of the population

sample selected using random process so every element has equal chance of being selected

• sample selected using non-random method
• some elements are more likely to be picked

error of estimation that occurs when theres a difference between the characteristics of a sample and those of the population from which it was picked

when a unit selected to participate in the study refuses, can't be contacted, etc

attempt to collect data from all elements in a population rather than a sample

• using random number generators or something to ensure that everyone has the same chance of getting picked
• chance and nothing else determines what elements are selected to be part of the sample

• selected directly from the sampling frame, ex. if 1/20 is the chance someone has to be picked, choose a random start within the sampling frame, then begin selecting every 20th person
• need to makes sure that there is no ordering/pattern within the sampling frame

• stratifying the population into subgroups by a criteria (ex. faculty), and selecting a simple random sample or systematic sample from each of the resulting strata
• ensures sample is stratified in the same way as the population

• primary sampling unit isn't individual units of a population, but a cluster of them (aggregate)
• ex. sampling students from universities within specific geographic regions
• greater efficiency in data collection
• seek diversity in primary sampling units
• typically involve some kind of random sampling at the final stage
• sample weights may be needed!

increasing, increases, decrease

% of the sample that actually participates in the study

• the extent to which people included in the sample differ from the population as a whole
• the people who don't answer the survey might be different somehow?

• the measure appears to reflect the content of the concept in question
• does it sound reasonable to you?
• assessed conceptually

• can be gauged by employing a criterion relevent to the concept in question bu on which cases differ
• does the item (or scale) cover the range of possible content?
• assessed conceptually, typically by experts in the field

• might be gauged by comparing it to measures of the same concept developed through other methods
• does a (new) measure of a concept correlate with other accepted measures of that concept?
• assessed empirically
• ex. student attendance rates and student satisfaction surveys

• seeing whether concepts used in the research relate to each other in a way that is consistent with what their theories would predict
• is a (new) measure of a concept correlated with other variables that you predict (theoretically) should be related?
• assessed empirically
• ex. assignment - education and environmental concerns

is the independent variable really affecting the dependent variable or is something else responsible?

is the presumed cause (x) really responsible for the outcome (y) or is there a third variable affecting both?

• evidence of correlation
• temporal ordering (cause precedes effect)
• non-spurriousness (absence of alternative explanations for the correlation)

can you generalize from this study to other settings? the population?

• sample bias
• random sampling error
• non-response error
• measurement (how you ask the question, reliability and validity of measurement)
• data preparation error
• interpretive error

can statistically estimate the amount of random sampling error

• fractions used to generate population estimates from disproportionate stratified random samples
• can also be calculated in some cases to handle different levels of non-response

• consider a non-probability design
• build it to make sure it is as representative as possible
• try to identify potential bias

• volunteers are self-selected on the basis of interest in the subject
• most problematic with bias
• researcher has almost no control over how the sample is generated

• non-probability sample
• subjects are conveniently available to the researcher (ex. psych undergrads required research participation)
• not the best way to construct a representative sample
• external validity problems

• non-probability
• used to find members of a highly specific population or build a reasonably representative sample of a larger population
• ex. going out and looking for people to interview in certain locations

• non-probability
• sample members provide names of additional potential sample members
• try to increase representativeness by starting several "snowballs"
• researcher has some control over sample composition

• obtaining a representative sample
• obtaining reasonably high response rate
• obtaining valid and reliable responses to questions
• efficient (time and money) data collection

• sample members respond to a set of questions in a similar way, responses are being motived by something other than the questions being asked
• boredom
• social desireability
• acquiescent

trying to be agreeable by answering the same way (ex. agree, agree, agree)

over 50%

• the "tailored design method"
• an extension of the social exchange theory
• explains why individuals are motivated to engage in certain social behaviors and not others
• need to install trust in participants, tell them how important their participation is, make questions interesting/engaging

• establish researcher-participant relationship to increase participant engagement
• don't make participants do your work
• obtaining reliable and valid information

• covers
• nice paper
• large enough font
• lots of white space
• return-address envelope

• establish legitimacy
• explain requirements of respondents (survey content, time, additional expectations)
• address ethical issues
• concise, professional

• anonymity: you don't know who the answers are coming from
• confidentiality: you won't tell anyone the info given by respondents

• factors that can lead to systematic measurement error
• ex. task effects, interviewer effects, respondent effects

• bias due to missing response options
• bias due to unbalanced response scales
• bias due to provision of selective information (don't tell you as much information as you want)

• easy to process
• enhance comparability of answers
• some provide question clarification for respondents
• can  answered quickly and easily
• reduce risk of interviewer/transcriber bias

• answers may lac sponteneity and authenticity
• must make sure answerable categories don't overlap
• can be difficult to make exhaustive
• respondents may differ in interpretations of forced-choice answers

• respondents can answer in their own terms
• allow unusual, maybe unexpected responses
• since the questions don't suggest answers, you can tap into a participant's knowledge and ideas about the issue 
• can maybe generate fixed-choice answers

• time consuming to record
• answers have to be coded
• prospective respondents may put off having to write out a full answer
• may be inaccuracies in writing down exactly what respondents are saying

• better interviewers
• sharpened questions
• not too many open-ended Qs

• the data analysis
• stats techniques used depend on how a variable is measured
• size and nature of a sample can limit the suitability of certain kinds of stats

• nominal
• ordinal
• interval-ratio

• categorical
• composed of categories that have no relationship to each other except that they are different

• values can be ranked
• ex. Likert-style questions

• based on a unit of measurement
• takes the form of actual numbers

• mean
• median
• mode

• range
• standard deviation

• for univariate analysis
• provides the numbers and percentages of cases belonging to each of the categories of the variables in question
• can be created for all 3 variable types

• for bivariate analysis
• like a frequency table, but allows two variables to be analyzed simultaneously to examine relationships between them

percentage to 100% in the direction of the independent variable

• smaller
• significance is easier to obtain with bigger samples

ex. in multivariate analysis, when a statistical relationship exists for some groups but not for others

• set up a null hypothesis
• establish an acceptable level of statistical significance
• determine the statistical significance of the findings
• decide whether to reject or not reject the null hypothesis

• ensure interviewees receive the same form of questioning and the same interview stimulus
• allow interviewees answers to be aggregated to form group rates
• means that the variation in answers is due to "true/real" variation in the characteristic being measured rather than extraneous factors

an interviewer isn't consistent with the way they ask questions or record answers

when there is more than one interviewer who may not be consistent with one another on how they ask Q's or record A's

• rules for assigning answers to categories
• variation can occur in the way things are categorized bc of how the interview schedule was administered, or the way answers were recorded

• structured interview without an interviewer present
• make sure they are easy to follow
• less open-ended questions
• shorter to reduce the risk of "respondent fatigue"

internal consistency

• Cronbach's alpha
• 1= perfect internal reliability
• 0= no internal reliability
• 0.80 usually used as an acceptable standard

describes in words what the concept means, ex. a dictionary defn

• spells out the operations the researcher will perform to measure the concept
• ex. how to measure the incidence of crime?