Midterm #2 - Qualitative Research Design Flashcards

1
Q

Main philosophical worldview that uses qualitative research designs

A

Post-positivism

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2
Q

What is Post-positivism?

A

Belief that there is 1 reality, 1 objective truth that is waiting to be discovered through research

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3
Q

Key defining features of post-positivism

A

DETERMINISM - Causes determine effects (use of experiments)
Reductionism = reduce ideas to small, discrete,
testable variables (makes hypotheses and
research questions)
Empirical observation and measurement = knowledge based on observation and measurement
Theory testing = theories are tested, supported, refined

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4
Q

Difference between Determinism and Causality

A

Determinism is a philosophical doctrine, and causality is a principle under it

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5
Q

Determinism

A

Doctrine that assumes every even has causes (nothing happens on it’s own)
- Makes a causal claim
- Post-positivist relation = we find and test for the cause
- no such thing as an “Accident”, implies no cause (injury prevention doesn’t like this, wants to anticipate and prevent)

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6
Q

Causality

A

Principle that everything has a cause
- Effects can have multiple causes

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7
Q

What do we need to claim causation?

A

1) Covariation
2) Isolation of causal variable
3) Effects come after causes (order)
4) Manipulating the cause will manipulate the effect

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8
Q

Covariation

A

Statistical correlation
correlation =/= causation !

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9
Q

Isolation of causal variable

A

Only x -> y. No z!
Cause is not because of a third variable
z a.k.a extraneous/control variable

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10
Q

3 Goals of Science

A

Describe - Descriptive (what) goals only cover CORRELATION
Explain - explanatory (how) goals
Predict - predictive (why) goals
- Both include causation

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11
Q

3 Types of claims

A

Causal (affects, leads to, causes, leads to, etc.)
Association (associated , relates, linked, correlates, etc.)
Frequency (counts, x # of, x% of, x-y/day, just giving the ‘what’, etc.)

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12
Q

Frequency claim

A

Describes rates or degrees of ONE measured variable

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13
Q

Association claim

A

At least 2 measured variables
- Correlated somehow (+, -, or 0)
- No manipulation or interference (takes away #4 of causal claim requirements)
- Make predictions based on strength of association (except for 0), stronger closer to 1

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14
Q

Causal claim

A

Variable x (expected to) changes variable y
- Independent and dependent variables
- Usually experimental design

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15
Q

Mediator variable

A

variable z that relates/bridges x and y
- Mediation model: X → Z → Y
- If you take Z away, no relationship
NOT the same as a “third variable”
- Interventions operate via mediators, so you NEED to identify it

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16
Q

Moderator variable

A

Changes sign/strength of the IV’s affect on DV
- Statistical interaction on a graph
- Effect modifiers = creates/enhances/modifies relationship

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17
Q

Confounder variable

A

Variable that changes relationship between IV and DV because it’s related to both
- Not part of the causal chain
- Source of difference for both variables (ex. population size in church-murder example)

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18
Q

Validity of qual. research designs is composed of

A

Internal and external validity

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19
Q

Validity of qual. measures are composed of

A

Content and statistical validity

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20
Q

Internal validity (IV)

A

Confidence in our results, and that any Δ in outcome was because of the treatment (also addressing threats to validity
- NOT because of a 3rd variable

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21
Q

Threats related to experimental procedure (IV)

A

Testing: participants just get better at the test, measures skill instead of their true reactions
Instrument Accuracy: Measures must be valid and reliable (working, no calibration error, no misuse, same collection technique)

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22
Q

Threats Related to Treatment or
Manipulation (IV)

A

Diffusion of treatment: Participants in different groups (esp. control group) talk to each other and adopt treatments (results will be the same b/w both groups)
Halo effect: Researcher bias towards their hypothesis (often not aware, double-blinding fixes this)

23
Q

Threats Related to the Participants (IV)

A

Maturation: growing, learning, puberty, natural changes (fix by having a control group also experiencing maturation to see if that’s the only difference)
History: Events other than the treatment impacting study (mental issues, trauma) (fix by having traumatized people in experimental and control groups)
Regression: Extreme scores tend to regress back to the mean (best jump ever, and the next few tests you’re vertical goes down to the mean, this can impact baseline/pretests and skew results)

24
Q

Threats Related to the Participants (IV)

A

Selection Bias: No random assignment (even volunteers who come may introduce selection bias)
Experimental Drop-Out (Mortality): People leaving, researchers compare drop-outs to people who complete to see any predictors (ex. drug trials, something about intervention isn’t working)
Placebo Effect: Participants react how they think they’re supposed to (ex. common in drug trials, people think they’re in control)
Hawthorne Effect: Participants may act favourably b/c they’re being watched (fix through control group and double-blind)

25
Q

Ways to minimize threats to IV

A
  • Control group
  • Random Assignment
  • Validated Measures (validity of measures)
  • Double-blind (participants and researchers don’t know who is receiving which treatment
  • Identifying potential confounding factors
26
Q

External Validity (EV)

A

How well we can generalize the findings to real world
- Concerns how sample was chosen

27
Q

Threats to EV

A

Selection x Treatment Interaction: characteristics of participants (kin students) make treatment (exercise intervention) only work for them (more likely to stick to it), further research needed to generalize
Setting x Treatment Interaction: highly controlled lab environments aren’t well translatable to real world (more “real world” procedures/research needed)
History x Treatment Interaction: timing of intervention is important, the past or future may be different (age, time period) (replicate research in different times with new info + tech to see if it’s still valid) (ex. Korea boat tragedy)

28
Q

Generalizability

A

How much findings in study (internal validity) can be generalized to real world (external validity)
Normally high IV = low EV

29
Q

4 Kinds of Qualitative Research Designs

A

1) True Experimental Design/Experimental
Design
2) Quasi-Experimental Design
3) Pre-Experimental Design
4) Non-Experimental Design

30
Q

True Experimental Design

A
  • Identified cause-and-effect relationship (causality)
  • Highly controlled lab setting (ensures IV, but less EV)
  • At least 1 experimental/treatment/intervention group (EG)
  • At least 1 control group (CG)
  • Random assignment to remove bias (RA)
    RA+CG+manipulates independent variable=true experiment
31
Q

Two subtypes of True Experimental Design

A

1) Randomized Control Trial (RCT):
- Pre and post test design - assumes pre test doesn’t impact results (since both groups do it) and both groups are equivalent and randomized
- Post test design only
2) Solomon Four-Group Design - examines effects of pre-test on outcome

32
Q

What does a pre and post test RCT design look like?

A

1 independent variable:
R O1 T O2
R O1 O2

> 1 independent variable:
R O1 TX1Ty1 O2
R O1 TX1Ty2 O2
R O1 TX2Ty1 O2
R O1 TX2Ty2 O2

R=randomize
O1=pre test
T=intervention
O2=post test

33
Q

What does a post-test RCT design look like?

A

R T O1
R O1

  • Best kind of test, but rare b/c difficult
  • Shows that a pre-test IS NOT required for a true experiment
34
Q

What does a Solomon Four-Group Design look like?

A

R O1 T O2
R O1 O2
R T O2
R O2

35
Q

Quasi-Experimental Design

A

Approximate conditions of a true experiment in a setting where we can’t control/manipulate every relevant variable
- No Random Assignment (lowed IV b/c groups aren’t equivalent, less accurate replicas)
- Usually higher EV than true experiment (b/c normally done in a natural environment, better generalization, out in the real world)
- Independent variable still introduced/manipulated
- Dependent variable still measured
Normally done b/c of lack of money, resources, physical condition (ex. pregnancy)

36
Q

What does a Quasi-Experimental Design look like?

A

Non-equivalent Control Design (b/c no random assignment R)
O1 T O2
O1 O2

37
Q

Pre-Experimental Research Design

A
  • No control, but sometimes ‘comparison’ group (non-equivalent group identified during or after intervention)
  • Independent variable still introduced and manipulated
  • Dependent variable still measured
  • Less IV and EV
  • No Random assignment
    This can be used as a lead-up to actual experiment, seeing if intervention can actually be handled, if it’s very hard to get participants, etc.
38
Q

Non-Experimental Research Design

A
  • No random assignment (b/c no groups to randomize to)
  • No manipulation of IV
    Not doing anything, just observing and comparing
  • Can use pre-existing groups (compared to understand potential effects of differences on dependent variable)
  • Frequency claims, correlational, longitudinal designs, descriptive, cross-sectional (compare existing groups at one time like active vs. inactive)
    Used in epidemiological research, population health knowledge
39
Q

Sample vs. Population

A

A sample is a group of participants in a study that represent a population
A population is a well-defined collection of individuals of interest with similar characteristics (who we want to generalize findings to)

40
Q

Random vs. Randomize

A

Random = no pattern or regularity
Randomize = to select subjects without detectible biases or patterns (allows generalization to population)

41
Q

Probability vs. Chance

A

Probability = quantifiable likelihood that something will happen
Chance = random, no idea if something will happen

42
Q

Law of Large Numbers

A

If we repeat random events again repeatedly, their fraction of success gets closer to the average/expected value (ex. flipping a coin is 50/50, rolling dice is 1 in 6)

43
Q

3 Steps in the Sampling Process

A

1) Unit of analysis: population of interest, groups to be investigated
2) Define study population: know the nature of the population the sample is coming from
3) Establish sampling frame: complete list (ex. UAlberta students, phone directories, census lists, etc.)

44
Q

2 Types of samples

A

Probability Sampling: random selection, probability of an individual being selected is known
Non-Probability Sampling: probability of an individual being selected is NOT known (constructivists use this)

45
Q

Types of probability sampling

A

Random (simple random, systematic)
Stratified
Cluster

46
Q

2 types of Random Sampling

A

Simple Random: everyone has an equal + random chance of being chosen, IV, eliminates bias
Systematic: pick a random starting point on a list, pick every nth subject

47
Q

Stratified Sampling

A

Concerned about underrepresented groups
- Divide samples into subgroups, and randomly select from the subgroups proportional to population/sampling frame
- Ensure small groups aren’t overlooked

48
Q

Cluster Sampling

A

Focused on where people cluster, helpful when population is spread out/hard to access
- sampling frame focuses on clusters, select a few clusters, randomly select within those clusters
ex. soccer teams

49
Q

4 Types of Non-probability Sampling

A

1) Convenience - most accessible/most willing
2) Quota - population divided into subgroups based on census data, selected based on reflected proportion in population (like stratified but no random assignment, still bias)
3) Purposive - Focus on a limited population/small geographic area, participants have experience with phenomena of interest
4) Snowball - Identify 1 or more subjects, and ask them to find more, useful for difficult to reach populations

50
Q

Non-response rate/bias

A

Error due to non participation or non response (ex. who answers the phone)

51
Q

Sampling error

A

Inaccuracies in generalizations of a population because sample doesn’t represent the population
- SD of sample, sample size

52
Q

Types of quantitative data and levels of measurement

A

Discrete data: takes on a particular numerical or categorical value (Nominal, ordinal)
Continuous data: any value/range (Interval, ratio)

53
Q

Difference between levels of measurement

A

Nominal - values hold no meaning, no numeric value
Ordinal - Groups are different to a degree (we don’t know what degree, but still a vague order)
Interval - Differences between groups to a specified degree, but no true zero point
Ratio - True zero point

54
Q

Concept vs. Construct

A

Concept is an idea, can be concrete or abstract
Construct is something we invent to tap into concept