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Flashcards in Midterm 1 Deck (103):

Advantages of open questions

respondents can answer in their own terms
allows unusual answers
allows tapping into participant's knowledge
good for exploring new areas


disadvantages of open questions

time consuming to record/code
length may put respondents off
inaccuracies in transcription of spoken answers


coding, and the types of coding

deriving themes/categories of behaviour. researcher usually assigns number to code
allows information to be coded quantitatively
--> pre coding and post coding


post coding

going back to info to look for incidences of theme or category, may be unreliable because of inconsistencies in judgement from different coders


pre coding

when researcher designs coding grame before administering the survey


3 basic principles of coding

categories are mutually exclusive
categories are exhaustive (including "other")
clear rules regarding how codes are applied (ensuring consistency)


advantages of closed questions

easy to process
easy to compare
set of answers help clarify the meaning of the question
quick and easy to complete
reduces risk of bias from recorder


disadvantages of closed questions

answers lack authenticity/spontaneity
care needs to be taken to prevent overlap in categories
difficult to make answers exhaustive
irritates respondents when answer categories aren't relatable
reduces conversation/rapport in interviews


types of questions

-personal factual questions (age, occupation, how often do you go to the movies? etc. often have to rely on memory to answer)
-factual questions about entity or event (good when info isn't available elsewhere, leads to problems because people aren't careful/systematic observers)
-questions about beliefs (should Canada maintain military presence abroad?)
-questions about attitudes (common in structured interviews/questionaires, Likert scale is common)
-questions about knowledge (who was Canada's first prime minister?)


general rules for designing questions

-keep research question in mind (reduces risk of asking irrelevant questions)
-being specific (what exactly do you need to know?
-recognize ambiguity (how would you answer it?)


what does it mean to avoid ambiguity?

-avoid "often" and "regularly" as measures of frequency
-clarify words that mean different things to different people (ex: dinner)


what does it mean to avoid long questions?

-long questions may be nice for questions asking about behaviour, the longer they take to answer, the more it may facilitate memory recall


what does it mean to avoid double-barrelled questions?

ex: how frequently do you cook and clean? respondent may cook but never clean


what does it mean to avoid general questions?



what does it mean to avoid leading questions?



what does it mean to avoid questions that ask several questions

ex: who did you vote for in the 2011 federal election? should be did you vote in the 2011 federal election? if yes, which party did you vote for?


what does it mean to avoid negatives?

confusion can lead to innaccurate answers


what does it mean to minimize technical values?



what does it mean to ensure respondents have requisite knowledge?

-if respondents do not know about topic, answers won't be meaningful


what does it mean to ensure symmetry between question and answer set?

ex: do you believe in God? strongly agree, agree, disagree, strongly disagree


what does it mean to ensure answer set is balanced?

equal number of positive responses to negative responses


what does it mean to not overstretch people's memories?

ex: how many times do you drink in a year? vs how many times do you drink in a week?


what does it mean to provide "don't know" options?

-avoids forcing expression of views that aren't held
-allows out for those too lazy to do thinking (lower education, later questions in survey are more likely to utilize 'don't know' option


what does it mean to consider question order?

-all respondents should receive questions in the same order?
-researchers should be sensitive to possible effects of order


tips on question order

-general questions before specific questions (if specific is before general, aspects of specific answer may be omitted from answer for general because it has already been addressed)
-opinions/attitudes before behaviour/knowledge (ex: spouse reports doing 20% of housework, influences question 'is housework shared equally?')
-early questions should be related to announced topic
-important/meaningful questions early to stimulate interest
-questions that cause embarrassment/anxiety go at the end but not the very end or else the interview will leave a negative impression
-personal questions that are apparently irrelevant to research question go at the end
-long questions should be grouped by topic
-if respondent answers something that will be asked later in the interview, still ask the question because answer might've changed


vertical vs horizontal answers

-vertical reduces likelihood of confusion
-vertical clearly distinguishes answers from questions


vignette questions

-a form of closed question used to examine ethical standards and beliefs by presenting scenario(s) and asking how participants would respond


advantages of vignette questions

-anchors responses in realistic scenario, reduces unreflective reply
-because vignette is about other people, allows for distance between question and respondent, leading to more candid replies


disadvantages of vignette questions

-impossible to establish assumptions made by respondent about the scenario
-difficult to establish how far from respondent's normative views (ie what people say they'll do and what they do are different)


pilot studies

-pilot study cannot use members of sample in true study
-if study will use closed questions, open questions can generate closed answers
-can help develop interviewer's confidence
-helps identify unuseful question, highlights needed modifications
-flags questions that make respondents bored/uncomfortable
-identifies questions most often skipped
-determines adequacy of instructions
-offers opportunity to evaluate overall flow


using existing questions

-already piloted, tested for reliability/validity
-allows for comparisons to other research
-provide insight on best way to approach research


the main steps in quantitative research

theory, hypothesis, research design, devising measures of concepts, select research site, select research subjects/respondents, administer research instruments/collect data, process data, analyze data, findings/conclusions, write up findings/conclusions



ideas or mental representations of things
-building blocks of theory
-represents points around which social research is conducted
-categories for organization of ideas/observation
-concept can be interdependent or dependent variable, descriptive or comparative


independent variable vs dependent variable

something to be explained vs possible explanation


concept can be descriptive or comparative

changes in amount of social mobility in Canada over time vs variations among comparable nations in levels of social mobility


why measure concepts?

1. allows for delineation of fine differences between people in terms of characteristic in question (it's harder to recognize fine distinctions than extreme differences)
2. Provides consistent device for gauging distinctions (measure's results shouldn't be affected by time/person administering the measure)
3. Provides basis for estimates of the nature/strength of relationship between concepts



stand for or represent concept, necessary to measure concepts (can be indirect, for example absenteeism as an indicator for low job satisfaction)


two types of definitions of concepts in quantitative research

1. nominal; describes in words like dictionary (crime is any violation of the Criminal Code of Canada)
2. operational; spells out operations that will be performed to measure concept (to measure crime, this researcher will use statistics provided by police force)


ways to devise indicators

through questions part of the interview/questionnaire (respondents attitudes, personal experiences, behaviours, etc)
developing criteria for classifying observed behaviour (pupil behaviour in classroom)
through use of official statistics (stats canada)
developing classification schemes to analyze written data (analysis of how newspapers characterize sex workers)


using multiple-item measures in survey research

single indicator may misclassify some individuals if wording leads to misunderstanding of meaning
single indicator may not capture all meaning in underlying concept
multiple indicators allow for finer distinctions and sophisticated data analysis



concerned with consistency of measures by looking at stability over time, internal reliability, and inter-observer consistency


stability over time

-whether results fluctuate as time progresses, assuming that thing being measured isn't changing
-most thermometres have this reliability
-test using Test-retest method


internal reliability

-aka consistency
-multiple measures administered in one sitting should be consistent
-cronbach's alpha coefficient
-split half method


cronbach's alpha coefficient

commonly used test in which 1 is perfect internal reliability and 0 is no internal reliability, and .8 is typically considered minimum acceptable level


split half method

indicators divided into two halves, respondent's scores should correlate, in which 1 is perfect internal reliability and 0 is no internal reliability


inter-observer consistency

-judgements between several researchers in activity involving subjective judgement
-ex: classifying and categorizing open answers


measurement validity

whether indicator accurately/properly gauges concept
-face, concurrent, construct, convergent


face validity

measure appears to reflect concept, essentially intuitive process


concurrent validity

examining when criterion differs from case to case
-ex: when measuring absenteeism as indicator for low job satisfaction, lack of absenteeism should be seen in those with high job satisfaction


construct validity

whether concepts relate to each other in a way consistent to what their theories would predict
ex: routine jobs should have lower job satisfaction than jobs with varied activities. if the routine jobs are found to have equal job satisfaction as complex jobs, it lacks construct validity. Either measure was invalid, deduction was misguided, or theory needs revision


convergent validity

-validity should be gauged through comparison to other measures of same concept developed through different methods
-ex problem with convergent approach: measuring crime though police reports or victimization surveys


which validities are more important?

face and internal are usually the only ones tested


if measure is not reliable ....

it cannot be valid


goal of quantitative researchers

to understand social order by making sense of phenomena and evaluate theories and interpretations


establishing causality

describing why, not just how
good quantitative research inspires confidence in researcher's causual inferences


generalization of findings

sample must be as representative as possible in order to be confident results are not unique to the sample
probability sampling largely eliminates bias through random sampling


critiques of quantitative research

-fails to distinguish people from 'world of nature' (some claim science is only applicable to entities/processes that lack self-reflection)
-measurement process produces false sense of precision/accuracy
-reliance on procedure creates disjuncture between research and everyday life (relates of external validity, difference between what people do/say they'll do)
-analysis of relationship between variables promotes view of social life as remote from everyday experiences
-explanations for findings may not be empathetic (ex: poor inner city areas see more unwed mothers with children because women are marrying later in life instead of marriage losing its popularity)
-assumption of objectivist ontology (assumes reality exists)



can be 'read', was not produced for social research
-analysing documents is unobtrusive and non-reactive
-removes threat of validity


4 criteria for assessing quality of documents

1. Authenticity (genuine and unquestionable origin?)
2. Credibility (free from error and distortion)
3. Representativeness (typical of time/place? extent of uniqueness?)
4. Meaning (clear and comprehensive?)


diaries, letters, autobiographies

-often used by historians, not social researchers
-written by purported author?
-people who are aware of an audience may not reveal everything on paper (what people don't write can be of significance)
-who was able to record/write/read?
-what has been damaged/destroyed?


3 types of household photograph

idealization (formal portrait)
natural portrayal (informal snapshot)
demystifcation (subject in atypical, often embarrassing situation)


government documents

authentic, have meaning
perhaps not credible


advantages of secondary analysis

-saves costs and time
-high quality data (sampling procedures are usually rigorous, often national scope, generated by highly experienced researchers)
-opportunities for longitudinal analysis
-subgroup analysis (when samples are large)
-opportunity for cross-cultural analysis
-more time for data analysis
-reanalysis can offer new interpretations
-wider obligations of social researcher (social research is chronically underanalyzed)


disadvantages of secondary analysis

-lack of familiarity with data
-complexity of data
-ecological fallacy (occurs when you make conclusions about individual based on analyses of group data)
-no control over data quality
-absence of key variables


advantages and disadvantages of official statistics

-often based on whole populations rather than samples
-problem of reactivity is less pronounced
-possible to chart trends over time
-but records only those processed by stats collectors (consider crime/suicide rates)


unobtrusive method

removes observer from behaviour under study
-studying physical traces (like grafitti, paper trail of finance), archive materials (data form governments and ngos), and simple observation (observer has no control over behaviour)


types of variables

nominal, ordinal, interval/ration


nominal variables

-aka categorical, composed of categories with no relationship except that they are different
-order of categories is arbitrary


ordinal variables

-categories that can be ranked
-can be described as
-likert scale is common
-difference between categories is not necessarily equal
-no unit to measure


interval/ration variables

-can be measured by unit
-difference between categories is equal
-can have 0 value
-can be ranked


frequency tables

provides number and percent of subjects belonging to each category of variable


measures of central tendency

mean, median, mode; provides typical score in one number



value that occurs most frequently, applicable to all types of variables, especially nominal data



mid point of scores, if there is an even number of scores the median is the mean of the middle 2 values. applicable to interval/ration and ordinal variables



average, vulnerable to outliers



difference between the highest and lowest value, vulnerable to outliers


standard deviation

variation around the mean, vulnerable to outliers
work out the general mean, subtract the mean from every value, square every value, then find the mean of those values


bivariate analysis

examines relationship between 2 variables, esp through use of contingency tables


pearson's r

statistic used to examine relationship between 2 interval/ratio variables
the relationship must be broadly linear


statistical significance

indication of risk of genralizing sample statistic to population
set up null hypothesis, establish acceptable level of statistical significance, determine statistical significance, decide whether or not to reject the null


two types of error

type I - true null was rejected
type II - false null wasn't rejected


chi-sqaure test

applied to contingency tables
-measure of likelihood that relationship between variables in sample will also be found in population
-large chi-square to reject null hypothesis, larger n makes this easier


spurious relationship

when relationship appears to exist but isn't real


intervening variable

suggests relationship between 2 variables is not direct


prominent sources of error

-poorly worded questions
-interviewer error in asking question
-interviewee misunderstanding question
-interviewer lapses in memory
-interviewer error in recording answer
-mistakes entering data into computer
-bias caused by innate characteristics of interview(er/ee)
-intra-interviewer variability
-inter-interviewer variability


advantages of phone surveys

-cheaper and quicker
-easier to supervise
-confidentiality is not as much of an obstacle
-reduces bias because interviewer and interviewee can't see each other


disadvantages of phone surveys

-can only reach those with listed phone number
-hard to communicate with hearing impaired
-cannot be sustained for long times (


advantages and disadvantages of computer assisted interviewing

-good for filter questions
-good for randomizing order
-loss of sponteneity
-lack of rapport/commitment/motivation


research driven diaries

record of feelings/actions shortly after they occue


advantages and disadvantages of research driven diaries

-accurate data
-details may not be recorded quickly enough


advantages of questionnaires

-absence of interviewer effects


disadvantages of questionnaires

-cannot explain question
-greater risk of missing data
-cannot probe
-difficult to ask a lot of questions
-questionnaire can be read as a whole
-cannot verify who took the survey


problems with respondents

-acquiescence (agreeing just to please the interviewer, ex: bodychecking should be banned from hockey)
-social desirability



the selection of individuals/units of analysis


sampling frame

list of elements from which sample is chosen


3 sources of sampling bias

-not using random sample
-sampling frame is inadequate
-non responses


types of sampling probability

simple random sample, systematic random sampling, stratified random sampling, multi-stage cluster sampling


types of non-probability sampling

convenience sample, snowball sampling, quota sampling, structured observation and sampling


reducing non response

-keep calling back, be optimistic, reassure people that you aren't a salesperson, dress to appeal to wide spectrum of people during in-person interviewing, make sure you are flexible with time


3 Questions to ask during bivariate anlaysis

-does the association exist?
-how strong is the association?
-in what direction does the association exist?


calculating association with bivariate table

"percentage down, compare across"
-an association exists if column percentages change
-the greater the change, the stronger the relationship
-to measure maximum difference, find biggest difference in column percentage for any row of the table



probability that results are not due to chance is 95%


null hypothesis

there is no relationship between 2 variables