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ENVR 301 > QUIZ 1 > Flashcards

QUIZ 1 Flashcards

1
Q

how do we generate reliable knowledge?

A

by using the scientific method
observation–> ordering and classifying of facts–> generalizations –> hypothesis making –> testing –> verification –> knowledge

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2
Q

steps to designing research

A

problem–> question of interest –> specific predictions –> methods and research design –> data collection –> data analysis –> interpretation

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3
Q

research design example

A

migration and changes on agricultural patterns in Oaxaca, mexico

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4
Q

problem in Oaxaca

A

Is the arrival of remittances from migrants
changing the agricultural strategies of Zapotec
communities?

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5
Q

Oaxaca specific qs

A

what kind of changes are being implemented?

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6
Q

Oaxaca context

A
  • place: mountains of Oaxaca
  • Socioeconomic context: demographic
    and economic collapse
  • Ecological issues: landscape ecology
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7
Q

oaxaca problem

A

Socioeconomic context results on
changes pressures over environment

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8
Q

Oaxaca variables

A

Relevant fields of inquire: agriculture
strategies, population, cash, commodities,
land cover

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9
Q

Oaxaca methods of data collection

A

1) Aerial picture analysis for land cover change
2) Demographic descriptive statistics and life stories
3) Tax records and mapping
4) Household income analysis
Question of Interest

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10
Q

oaxaca research design

A

Unit of analysis: individual/ household/
extended family
Timing of the process (1960s onwards), of the
research (seasonality?)
Scale: small community + multilocal
Sample: number of households
Question of Interest

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11
Q

Oaxaca results
- There is a clear process of forest
transition

A

People left
- Remittances are a fundamental part
of the local household economies
- Cultivar portfolio has changed (less
types of crops, less area devoted to
cultivation)

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12
Q

what is interdisciplinary research ?

A

Crosses traditional boundaries
between fields

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13
Q

research questions define…

A

context, scale, timing and history (process)
-variables, sample strategy, methods

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14
Q

classic research question problems

A
  • Concept definition
  • Required spatial scale of analysis
  • Temporality (of event and of
    research)
  • Goal definition
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15
Q

independent variable

A

initial variable of
which we know its changes

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16
Q

dependent variable

A

results on another
variable depending on the changes of the independent
variable

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17
Q

constants

A

value that doe snot change
either a reality or an assumption

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18
Q

process (diachronic studies)

A

the idea that things change across time
- time is an accumulation of points

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19
Q

process: consequences of synchronic studies

A

limits analysis of flows ( trends; predictions; patterns)
- idea of variability cannot be detached from the concept of process

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20
Q

process: questions and time

A
  • temporality (diachronic/ synchronic)
  • longitudinal vs cross-sectional
  • repetitive relevance (ex: annual, seasonal)
  • temporal scale (short to long term)
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21
Q

questions and space = scale of question

A
  • Macro (relative to the question and
    context)
  • Micro (relative to the question and context)
  • Networked research (links between relevant
    nodes)
  • Multiscalar research (links between different
    scale levels)
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22
Q

breaking down research

A

variables (dependent/
independent)
Constants
Context
Process in time (history/ change)
Process in space
Evidence (data)
Sampling

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23
Q

what does time and space refer to in research Q?

A
  • Demography across time
  • Demography across space and
    time
  • Redefining scale
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24
Q

what is replicability ?

A

The notion that same methods, same
locale, should generate the same results

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25
Q

how do you validate an interpretation ?

A

Validity would, then, depend on the
accumulation of such identical results
(statistical approach to the validation)

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26
Q

what is evidence?

A

data we produce
data we process
data we interpret

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27
Q

types of data

A
  • quantitative / qualitative
  • ‘objective’/ subjective
  • artifacts (archaeological),
    texts (interviews, novels, direct
    observation), measurements..
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28
Q

question while sampling

A
  • To who?
  • To how many? –idea of
    sampling-
  • How?
  • What are the consequences of
    each choice?
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29
Q

what is sampling theory?

A

The selection of some
part of the whole in
such a way that we
can use the part to
inform us about the
whole

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30
Q

what is probability sampling?

A

each element of population has equal chance of selection

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31
Q

define population

A

group of people, items or units under investigation

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32
Q

define census

A

information obtained by collecting
information about each member of a “population”

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33
Q

define sample

A

Obtained by collecting information only
about some members of a “population”

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34
Q

why do we use samples?

A
  • Cost & time, or a census downright
    impossible
  • Sampling provides adequate
    information
  • Some tests are destructive (car
    safety collision tests)
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35
Q

components of sampling

A
  • Design (randomness, hierarchical,
    snowball)
  • Size (representativity)
  • Location (spatiality of the sampling)
  • Composition (social variables):
    gender, occupation, age, kin, status,
    …
  • Awareness
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36
Q

how do you identify a ‘representative’ sample?

A

Sampling Theory (random)
- Each sample point must be independent
- Each sample point must have an equal
and independent probability of being
picked
- Adequate number of sample points

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37
Q

when to use random sampling

A

Natural Sciences prefer ‘Random’ or
‘Probability’ Sampling (otherwise results
may be biased, i.e., not representative of
population

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38
Q

why use non-random sampling?

A

Sometimes only biased samples are
available. Social sciences are conducive
to non-probability sampling: snowball
sampling, purposive, convenience

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39
Q

what are consequences of sampling?

A
  • From the privileged sole informant,
    to talking to everybody (from
    minimal sample versus universe)
  • Reflecting about representativity
  • Randomness versus purpose
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40
Q

types of random sampling

A

simple
systematic
stratified
cluster

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41
Q

simple random sample (equal chance)

A

Obtain a complete sampling frame
- Give each case a unique number
starting with one
- Decide on the required sample size
- Select that many numbers from a
table of random numbers
- Select the cases which correspond to
the randomly chosen numbers

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42
Q

systematic sampling (arranged in some order, first random, followed by k th)

A

Sample fraction
- divide the population size by the
desired sample size
- Select from the sampling frame
according to the sample fraction
- e.g sample faction = 1/5 means that
we select one person for every five in
the population
- Must decide where to start (start is
random)

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43
Q

stratified sampling

A

Premise - if a sample is to be
representative then proportions for various
groups in the sample should be the same
as in the population
 Stratifying variable
 characteristic on which we want to
ensure correct representation in the
sample
 Order sampling frame into groups
 Use simple random or systematic sampling
to select appropriate proportion of people
from each strata

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44
Q

cluster sampling

A

Involves drawing several different samples
by dividing a large geographic area into
smaller units
 e.g., divide Montreal into boroughs
 Select simple random samples from the
boroughs
 start with large areas then progressively
sample smaller areas within the larger

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45
Q

types of non random sampling

A

snow ball
convenience

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46
Q

snow ball sampling

A

Identify possible informants by
asking our current informants
about suitable new subjects
Identification of networks
Ideal for specialized
communities

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47
Q

what kinds of of qs needs snowball sampling?

A

Questions on minorities or invisible
communities
- Questions on dispersed groups of
individuals (diaspora communities,
networks of specialized individuals,…)
- Questions on secretive of mistrustful
groups)

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48
Q

what is convenience sampling?

A

Glorified “do whatever you can”

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49
Q

what are control cases?

A

Chose two similar samples
- Proceed to the experiment with
one of them, leave the other as
an example of the initial
situation

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50
Q

why use control cases?

A

Asses change by, simultaneously
assessing lack of change
- understand the
mechanisms of change by
assessing two different processes
on identical locales

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51
Q

ethics: relevance

A

-Understanding the values of the
research site
- Understanding the
consequences of your research
- Conducting proper research
- Legal process

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52
Q

responsibilities in research

A
  • To studied people and animals
    (to subjects and context)
  • To scholarship and science
  • To the public
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53
Q

research ethics

A

Research often confronts different
stakeholders interests
* Ethics as a complex field of
competing interests
* The researcher does not remain
outside of the game (becomes a
player or turned into one via
expectations)

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54
Q

how is data generated? (primary extraction)

A
  • Observing social or biological behavior
  • Interviewing
  • Measuring frequencies
  • Collecting samples
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55
Q

secondary treatment of data : processing

A

Statistics
- Discursive analysis
- Modeling
- Geographic Information Systems

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56
Q

different types of research methods

A

archival research or recollection of social data on the field

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57
Q

field data collection is gathered by either

A

surveys/ interviews or observation

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58
Q

composition of surveys and interviews

A

By structure
- structured, semistructured, unstructured
By theme
▪ Life stories, genealogies
▪ Free listing, triads pile sorting
▪ Diet breadth, income analysis

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59
Q

field observation

A

time allocation and participant observation (method and framework)

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60
Q

different fields of inquiry

A
  1. Demography
  2. Domestic Economy
  3. Ethnohistory
  4. Ethnobiology
  5. Health
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61
Q

what does interviewing consist of ?

A

Talking to people
- Opinion versus facts (interpretation)
- Narratives or points
- Practicalities: time, setting, themes
- Memory

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62
Q

types of questions

A
  • Closed versus Open-ended Questions
  • Closed questions includeYes/No responses, Likert
    Scale questions, and Categorical Choices.
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63
Q

advantages of open-ended questions

A

When not all categories
are known
- Can answer in detail with
clarification
- Used if too many
categories
- Used if issue complex,
exploratory, preliminary
- Allows expressiveness

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64
Q

disadvantages of open-ended questions?

A

Worthless, irrelevant
responses possible
- Statistical Analysis
difficult
- Requires time to
respond
- Looks longer to
respondent

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65
Q

advantages of closed questions

A

Standardized
- Easier to respond to
- Easier to code
- Clearer about
meaning of question
- Better with sensitive
topics (multiple
choice)

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66
Q

disadvantages of closed questions

A
  • Easy for respondent
    to “just guess”
  • Respondent may not
    find the right
    category
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67
Q

wording to avoid in questions

A

avoid double-barrelled (and/or) and leading questions

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68
Q

order of interview questions

A

general –> specific –> open-ended and sensitive questions

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69
Q

historical data collections via…

A

written history (documents) = lit rev.
oral history
- interviews
-life stories
-genealogies

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70
Q

why is archival research important?

A

contextualization!! and historical data or state

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71
Q

how do you replicate archival research ?

A

citation of sources
contrasting sources
justification with data and source

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72
Q

what is a life story?

A

collection of recollections of personal historical narratives associated to individual past experiences

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73
Q

what is narrative analysis?

A

Local definitions of the key concepts (avoid
assumptions )
* Certain level of interpretation
* Narrative style and structure, presence of
metaphors
* Repetition across subjects

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74
Q

how do you replicate data from life story analysis?

A

Researcher interpretation
- Informants’ pollution
- Subjectivity
- Political motivations
- Competition

75
Q

comparing life stories for internal consistency and reliability

A
  • Compare versions of the same traditions told by
    individuals from close groups
  • Compare accounts of stories affecting two groups
    (migrations and wars) explained by individuals of
    both groups
  • Discover regional trends by looking at whether
    themes and events have entered into the oral
    records of neighboring groups
76
Q

social production of knowledge

A
  • All landscapes are full of anthropogenic
    features (resulting from human agency)
  • Social agency is informed by knowledge and
    perceptions of reality
  • Knowledge is culturally organized
77
Q

ethnobiological methods

A
  • free lists
  • triads
  • pile sorting
  • rankings
78
Q

what does comparison of different social groups show?

A

unveil differences in how a specific cultural
domain is managed: occupation, gender, age, cultural or geographic origin

79
Q

free list create?

A

spontaneous lists (of things, opinions…)

80
Q

problems of free lists

A
  • Over-differentiation and under-differentiation
    (group versus species and subspecies)
  • Translation issues
    -Previous knowledge of the question
    -Expectations
81
Q

triads

A

-attempt to identify classificatory logics
- provide three elements generated by the free lists and ask subjects to pair two of them (and explain why)
-need to pay attention to cultural and geographic context

82
Q

classificatory rationality

A

morphological similarities
use
stories
ontological categories

83
Q

pile sorting

A

-organize concepts in groups
-subdivide the groups (hierarchical clustering)
-interrogate about the logic behind distinctions

84
Q

ranking

A
  • Ask the informant to rank a data set
    (provided by the researcher or produced by
    the informant) according to a criteria
    -Compare rankings depending on social
    strata, cultural background, gender
85
Q

difference between ranking and free lists

A

ranking has conscious classification (associated
to values or political views)

86
Q

why do we observe behaviour ?

A
  • Identification of behavioral patterns
  • Understanding rationalities and constrains
    behind those patterns
  • What people says is not always what people
    do(ideal/bias/unconscious)
  • Memory is fickle
87
Q

limits of interviewing

A
  • history and memory
  • self interested bias
  • cultural expectations
  • contradictory subjectivities
88
Q

participant observation includes

A

long term field work, hang out/build trust, learning behavioural codes, describing everyday practices and learning local world view

89
Q

contextual information includes

A

person, behaviour, setting (location), date & time, age, sex, household, and marital status

90
Q

household econometrics

A

time allocation, income distribution, diet breadth,

91
Q

the organization of time is significant, time sampling needs…

A

systematic following, self-administered, random spot sampling, people and places

92
Q

types of sampling is according to

A

target !

93
Q

focal sampling

A

single individual

94
Q

several individuals, simultaneous behaviours

A

scan sampling

95
Q

behaviour sampling

A

types of behaviour

96
Q

sampling during a period

A

continuous sampling

97
Q

instantaneous sampling

A

specific moments in succession

98
Q

time can be a proxy for

A

productivity
efficiency
preference

99
Q

problems of time

A

lying, overestimation, division of labor, cost of tools fabrication

100
Q

key point of coding

A

defining categories

101
Q

problems of coding

A
  • simultaneity
  • reliability
  • context dependence
  • mixing code categories
  • classification of problems
102
Q

what do we observed?

A
  • Frequency (instances per unit time)
  • Duration (length of single occurrence)
  • Intensity (pace, useful for energetic
    expenditure studies)
  • Sequence of behaviors (behavior flow) to
    complete a task (steps in food preparation)
103
Q

what is latency

A

the time between the end and start of a behaviour

104
Q

goals of observation

A
  • Sequence, duration, and frequency of
    behavior
  • Understand the context of such behaviors
  • Activities/ social indicators
  • Identification of unconscious patterns and
    trends (individual or collective)
105
Q

common criticisms of observational methods

A

reductionism
focus on single issue
classification of behaviours is complicated
definition of categories, representativity of data collected, reactivity (researcher’s impact), size of observation + sample issues (randomized and size)

106
Q

income distribution via interview

A

analysis of the composition of the income available to a household

107
Q

source of the income
-type of activity, type of currency provides…

A

data on productivity, environmental impacts, trade and labour networks
-households connection to larger economic networks and inequalities

108
Q

criticisms of income distribution

A

cash does not summarize wealth circulation, no data on redistribution, sensitive material, subject to high levels of occultation

109
Q

analysis of socio-economic change

A

tradition: production and consumption
time devoted to production, actual profitability, changes on labour allocation viability

110
Q

dietary breadth

A

collection of data on food consumption (who, what, how much, frequency)

111
Q

diet breadth includes

A

diet composition
eating units
sharing networks
child-rearing units

112
Q

dietary breadth correlates to socioeconomic variables

A
  • Provides information on: nutrition, health,
    production, trade
  • Differences between groups may point out
    at inequalities or cultural preferences
  • Environmental consequences (diet emerges
    from economic practices and these have
    direct impacts on the environment)
113
Q

demographics are made up of

A

structure and population dynamics
- size and territorial distribution of a population
- historical evolution of the population

114
Q

demographic unit of reference

A

one population, analyzed via the family or the individual

115
Q

types of demographic information

A

census : economic activity, level of eduction, ethnic group, civil status
Parrish register: marriage age, deaths
taxes: economic activities

116
Q

situation of a population

A

Absolute size
▪ Abundance
▪ Size and settlement patterns

117
Q

problems of population

A

useful surface, no total, problem of scale;
define the limits of the population, surface
(administrative units, property,… irrelevant with
groups that are not self-sufficient)

118
Q

population structure

A

Age and sex (pyramid; in %). Information about history and
population

119
Q

population pyramid trends

A

Broad base (young population, rapid growth),
▪ wider at the top (lack of generational renewal and population
reduction),
▪ similar values dif. age groups (stagnation).
▪ Strong differences indicate relevant past episodes

120
Q

discursive analysis

A
  • To detect patterns in usage and meaning
  • To understand motivation and purpose
  • To analyze internal structure and its
    consequences
121
Q

textual analysis or quantitative analysis includes

A

Content analysis Semantic networks Grounded theory

122
Q

literary analysis

A

qualitative deconstruction

123
Q

common issues of discourse analysis

A

selecting and contextualizing texts, coding and interpretation

124
Q

literary analysis

A

Discourses and ideas as products of cultural
and historical context
¡ “Truth”: historical variable resulting from
uneven social relationships
¡ Common themes and rules and structures to
organize them

125
Q

textual analysis: content analysis –> develop codebook

A
  • Presence and prevalence of key words
  • Relationships between texts, respondents, or
    words (patterns)
  • Repetitions and connections between codes to identify the patterns
126
Q

textual analysis: semantic networks

A

labeling to show relationships
(correspondence analysis =connections, hierarchical clustering =dependences)
study of the connections between nodes (concepts) more than the concepts themselves

127
Q

textual analysis : grounded theory
–> Relationships amongst categories
–> Systematic coding of data

A

Topic of interest: describe lived experiences
- Diverse perspectives
- Multiple comparisons between data collected
- Unveil local meanings and local perspectives
- Focus often on a core category

128
Q

example of combining methodologies

A

What do they say? (interviews)
What do they do? (behavioral
observation)
What do they eat? (diet breadth)
How do they pay for it? (income
analysis)

129
Q

Social methodologies

A

-archival and bibliographical research
-interviews and questionnaires
-behavioral observation
analytical tools

130
Q

population

A

the pool of individuals from which a statistical sample is drawn for a study.

131
Q

census

A

procedure of systematically calculating, acquiring and recording information about the members of a given population

132
Q

sample

A

a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population

133
Q

sampling frame

A

the actual set of units from which a sample has been drawn

134
Q

random sample

A

in which each sample has an equal probability of being chosen

135
Q

representative sample

A

is a sample from a larger group that accurately represents the characteristics of a larger population

136
Q

data generating process

A

measurements taken from the real world (just a small glimpse) –> data

137
Q

inferential statistics

A

allows you to draw conclusions based on extrapolations, and is in that way fundamentally different from descriptive statistics that merely summarize the data that has actually been measured.

138
Q

examples of inferential statistical questions which amount to measuring differences

A
  • What is the average level of life satisfaction in different
    Canadian provinces?
  • Are successive (younger) cohorts in Quebec choosing to own
    fewer cars?
139
Q

causality

A

is a relationship between two events, or variables, in which one event or process causes an effect on the other event or process.

140
Q

causality example

A

there is a positive correlation between ice cream sales and sunburns. Meaning, as ice cream sales increase, so do instances of sunburns.

141
Q

causal salad

A

including confound while lacking a real causal model

142
Q

inconvenient truths

A

-Covariates create confounds
-Prediction is not causal inference
-Data not enough
-Reproducibility not enough

143
Q

sources of variation in causal diagrams

A

spatial, temporal, demographic variables

144
Q

in an experiment; causal variable X is…

A

manipulated directly

145
Q

a confounding variable causally affects both

A

X & Y

146
Q

subjectivity of causality

A

-all conditions are causes
- often the difference between the “fundamental” one and
others is merely rhetoric or, rather, policy interest

147
Q

measurement validity

A

how well your metric captures the
underlying concept you are trying to measure

148
Q

internal validity

A

the degree to which the design of an
experiment controls extraneous variable, demonstrate cause-and-effect relationships

149
Q

external validity

A

is the degree to which effects found in an
experiment generalise to other individuals, contexts, and outcomes.
For sampled studies, this means to times and places outside the
sampling frame ( can lack generalizability)

150
Q

threats to external validity

A

1- interaction of selection and treatment: unrepresentative
responsiveness of the treated population
2 - interaction of setting and treatment: effect of the treatment
may differ across geographic or institutional settings
3 - interaction of history and treatment: effect of the treatment
may differ across time periods.
4- The effect may not persist, as individuals and institutions
adapt over time to the treatment.
5 - The treatment may be a “partial-equilibrium” effect (other
components of the sytem also undergo related changes,
reducing or eliminating the effect

151
Q

considerations of experimental design

A

What is your treatment?
Who or what is the treatment group?
Who or what is the control group? How similar are they to
your treatment group?
How will you measure the treatment effect?

152
Q

a classic experimental design ( pre-test/ post-test control group)

A

1 Random assignment to treatment and control groups
2 Control of the timing of the independent (treatment) variable.
3 Controls all other conditions under which the experiment
takes place.
4 Evaluate the differences-in-differences

153
Q

what is an experiment?

A

a set of actions and observations, performed in the
context of solving a particular problem or question, to support or
falsify a hypothesis or research concerning phenomena

154
Q

what are natural experiments

A

serendipitous situations in which
assignment to a treatment (or multiple treatments) and a control
group happens randomly and visibly, and outcomes are analysed
for the purposes of putting a hypothesis to a severe test

155
Q

instrumented variation

A

if you are unable to experimentally vary the relevant variables,
researchers seek to find some variation in them that is driven by
factors that are clearly identified and understood. You can do this
through the use of an “instrument”

156
Q

instrumented variation used when

A

-there is no fortuitous assignment into treatment/control
groups,
-there is no single natural driver of variation,
-and, in fact, there are confounding variables or two-way
causality that make causal identification difficult

157
Q

natural vs instrumented natural experiments

A

Are subjects sorted unambiguously into different (discrete)
categories / treatments? (→ Natural experiment)
Or is the treatment composed of multiple influences, only one
of which (the instrument) is “random”, ie exogenous

158
Q

to claim causality

A

time order - the cause must have occurred before the effects
co-variation (statistical correlation) - changes in the value

159
Q

statistical control

A

we “control for” some variable or factor Z through statistical
adjustment, it means we try to take out the effect of Z on Y in
order to see what remains (which we may assume is due to X )
-This kind of “control” is done after the fact, during the statistics
phase, ie when the experiment or observation is done and the data
are in. = statistical adjustment

160
Q

four fundamental confounds (directed acrylic graphs, DAGs)

A

the fork
the pipe
the collider
the descendent

161
Q

covariate

A

an independent variable that can influence the outcome of a given statistical trial, but which is not of direct interest.

162
Q

GIS software

A

ArcGIS
QGIS
GEOMEDIA- local gvts
Smallworld (GE)-used by utility companies

163
Q

spatial features can be

A

discrete or continuous

164
Q

discrete spatial features

A

houses, roads, wells = vector

165
Q

continuous spatial features

A

rainfall, elevation = raster

166
Q

discrete geographic features are better represented by

A

georelational vector data model (points, lines and polygons)

167
Q

vector data rules

A
  • thematic object forms its own layer ( roads separated from railways)
    each layer can have only one type of feature ( can’t mix points with polygons)
168
Q

continuous geographic features are better represented by

A

raster data model or grids & cells

169
Q

an example of raster data model : the digital elevation model (DEM)

A

a digital terrain representation technique, where elevation values/topography are stored in raster cells
- useful for hydrological modeling

170
Q

forms of raster data models

A
  • aerial photographs (digital orthophoto quadrangle)
  • satellite images
171
Q

remote sensing and GIS

A

a form of ‘primary’ data collection
- can be used to collect information about objects on the ground using satellite or plane based sensors

172
Q

pixel values in a raster image

A

valued between 0-255
0 - black
255- bright cell

173
Q

colors are a proxy for the number values because

A

different land covers reflect different colors

174
Q

why vegetation reflects near infrared

A

absorb red, green, and blue, to convert into food
infrared is all that’s left

175
Q

spatial analysis in GIS

A

map projections
attribute data
cartography: making a map & choropleth maps

176
Q

vector analysis with GIS using

A

buffers
overlays
-union
-clip
-intersection

177
Q

buffer

A

polygon created by reclassification at a specified distance from point, line, area

178
Q

overlay

A

places one ‘theme’ (e.g. soils) over another e.g (parking lots) e.g. check for soils which will cause problems of drainage for proposed parking lot

179
Q

GIS analysis: buffer and overlay

A

buffers can be combined with polygon overlays in order to analyze spatial information
e.g. find all habitat areas of owls that are within 500 m of country roads

180
Q

coordinate systems

A

(x,y) coordinate systems for drawn through the centre of the projection create new reference (x,y) for places in the globe

181
Q

basic elements of a map

A

title, map features, legend, north arrow, scale bar, neat line

182
Q

raster analysis

A

map algebra
- zonal
-focal
-local
-incremental

183
Q

DEM-specific

A

-slope
-aspect
-cross-section
-inter-visibility
-hydrology

ENVR 301 (8 decks)

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