Lecture 14 ARM Flashcards
Why Numbers Matter in Anthropology - 1/7 (14 cards)
Anthropology’s evolving relationship with numbers - four factors
1.Traditionally qualitative - built on ethnography, rich descriptive data, “thick” description
2. Changing landscape - rapidly becoming more quantitative - surveys, statistics, “big data”
3. Subfield perspectives - some like archaeology and biological anthropology always used numbers, but now also sociocultural anthros use quantitative
4. Why the shift? - New research questions required larger data sets (global trends, cross-community comparisons), interdisciplinary influence, need for generalisable findings, funding/reporting demands
Deep suspicion of quantification in anthro scholarship
- Cultures are too rich in nuance to capture that with numbers
- Ian Hacking - biopower - obsession with statistics may be less about understanding a people but controlling them (GDP, etc) deeeep
- tool of colonial times
- context matters! meaning matters - which numbers alone cannot supply
HOWEVER-new realization that numbers CAN actually help in anthropology
Why measure - the upside of numbers
- Preciseness - fine distinctions between groups or trends
- Consistency - standard measures, eg asking a question the same way, can improve comparability
- Prediction and generalisation - test relationships between concepts through data
- Communication - numbers are a common language and people like clear figures (lowkey stemming from colonial classification but okay)
Adding a general layer of evidence that complements qualitative stories
Quantitative data
Information expressed in numbers or quantities (counts, percentages, measurements)
Examples of quantitative data in anthropology
- Survey results
- Demographic/census data
- Measurements of artefacts or bodies
- Frequencies of behaviours or occurrences
Qualitative data
Descriptive, non-numerical information (interview transcripts, observations, stories)
Key benefits of quantitative data
1) Reveal hidden patterns - that anecdotes cannot see - such as trends, outliers, large patterns
2) Comparability and generalisation - compare cross-culturally, move from specific cases to broader insights
3) Measure magnitude and frequency - determine how widespread or frequent a phenomenon is (adds perspective on importance or scale)
4) Evidence for arguments - provide solid, intersubjective evidence to support claims (numbers bolster credibility)
5) Complement qualitative findings - combine breadth (quant) and depth (qual) for a richer, holistic understanding
Balancing numbers and culture
How to contextualise quantitative studies
- Just actually contextualise through cultural and social factors
- Ask “why” behind “what” - meaning
- Humanize the data - use anexdotes to illustrate statistics
- Avoid “Stats for stats’ sake” - use the data that is relevant”
- Reflexivity with numbers - be aware of biases in data collection and analysis and transparency of methods
Statistics is NOT mathematics!
-Anthropological stats is about REASONING - tool for thinking, a methodology of science
-Focus on concepts and interpretation, not heavy calculations - make cultural sense of numbers
- Encouragement - quantitative is another reasoning to analyse data
Key point_ statistics will serve your research questions - always tying numbers back to anthropological meaning
Anthropology as a science - the power of comparison 1
1) Objective and transparent comparison across cultures, places and times through statistics
EG: generational change in a community - grandparents and grandkids speaking local language.
Anthropology as a science - the power of comparison 2
2) Replicability and evidence - replicability is easier with statistics.
EG: comparing BMI and HYPERTENSION rates across groups in migrant and non-migrant communities
Anthropology as a science - the power of comparison 3
3) Hypothesis testing - formally testing ideas about cultural phenomena, using evidence to support our claims.
EG: contact frequency of families in urban vs rural settings to test kinship ties x urbanisation.
Inductive vs deductive
Inductive: Qualitative-leaning. Empirical reality first, then theory follows. -Verification logic
Anthropology example: You chill with some elders, find that they yap more in rituals, base a theory on that
Deductive: Quantitative-leaning. Theory first, then empirical follows. Start with hypothesis, and associated with Popper’s falsification. You propose a hypothesis and try to disprove the null hypothesis.
Anthro example: Hypothesis - kinship networks will be weaker in urban settings - okay get yo ass to an urban setting and do some surveys to double check
BOTHAPPROACHESAREVALUABLE!
Case study 1 -Ethic/racial friendgroups
At a school - survey about diversity and hanging out in friend groups
- see that althouhg people hung out mostly along racial/ethnic lines, deeper findings showed that poeope are still inclusive
Case study 2- generational change in importance of religion. why?
