SOC200 - Unobtrusive Research (Chapter 9) Flashcards
UNOBTRUSIVE RESEARCH
any mode of observation which uses indirect ways to observe & collect necessary data
studying subject without being known + affecting measurements
triangulation: multiple methods + indicators from diff sources to increase validity + reliability
Some of the more unusual data sources in unobtrusive research include
Garbage: used quantitative data for consumption pattern + went through garbage to corroborate
Data ppl freely volunteered didn’t equal data they found in garbage
Graffiti: content analysis of juvie centers in US, compared with official reports of experiences
Obituaries: news articles of recent deaths
Whether sports considered central to their identity for men
Sports more masculine
Why do unobtrusive research?
Minimizing a “Hawthorne Effect”: Social behaviour can be studied without being affected by research process
Convenience: data sources depend on researcher’s imagination
Data collection less time sensitive + less costly
Likely easier to pass an ethics review
Why do unobtrusive research?
Readily available data
Studying residues – not much ethical problems
Corroboration: increase validity of findings through multiple data sources
Used to supplement other sources
Get sense of validity of sources
ANALYZING EXISTING STATISTICS:
source of data collected + analyzed by others that researcher then reports on
Data collected & reported on by government agencies: canadian social trends – monthly publishing of stats canada findings
Data collected & reported on by private institutions &
associations: stocks, financial data, product catalogue
ANALYZING EXISTING STATISTICS:
agenda: can we trust the validity of this info?
Data collected & reported on by independent researchers: academics, professors, compiled in books (Recent Social Trends in Canada)
Data collected & reported on by NGO’s: IMF, UN – publish lots of data on hundreds of countries on any topic – GINI
To supplement whatever you are studying
ANALYZING EXISTING STATISTICS:
Important Points
data may be constrained by promises of confidentiality
May be main source of data for inquiry
ANALYZING EXISTING STATISTICS:
Important Points
Existing stats should always be considered at least as supplemental source of data
Provides historical/conceptual context within which to locate your research
You are collecting raw data
More often supplemental source
ANALYZING EXISTING STATISTICS: Methodological Issues
Beware ecological fallacy: aggregated nature of existing statistics means the U of A is not often the individual
Validity Problems: when using existing statistics, we’re limited to data that has already been reported on:
data may not cover exactly what we’re interested in, so measurements may not validly represent variables + concepts you want to study
ANALYZING EXISTING STATISTICS: Methodological Issues
Reliability Problems: quality of stats may be grossly inadequate
reported + unreported crime
ecological fallacy: Reliablity
CONTENT ANALYSIS
suitable for studying recorded human communications through books, print media, electronic media (internet + text messages), + cultural forms (songs, poems, + art)
Internet: free, lots of sources of data
Art: human interactions of art
Field is wide open in research of interaction
CONTENT ANALYSIS: Appropriate Topics
study of communications +: “Who, says what, to whom, why, how, and with what effect?”
observation of communications in selected sample of media
Sampling: depends on unit of analysis – which depends on what you’re looking at
CONTENT ANALYSIS: Appropriate Topics
Recording presence/absence of info used to indicate concept(s) of interest
use/frequency of certain words, phrases, slang, idioms, characterizations, etc.
More male leaders that female leaders as seen in obituaries
Coding
converting raw data into a standardized form
Terms used in the obituaries to describe former leaders (according to masculine or feminine stereotypes) were classified into 58 categories established in a previous study.
Coding
percentage of interrater-concordance was 93%. In case of dissimilar classification, coders discussed categorization until reached agreement.
93% of codes used – high level of consistency betw coders