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RMB - Theory - Coggle Diagram
RMB - Theory
Research Approaches
- Experimental
- Assess causality
- Compare groups (between/within) - random assignment
- Manipulate IV, measure DV
- Control extraneous variables
- Gold standard - not always possible
Pros: causality/directionality, high internal validity
Cons: low external validity, can't always randomise, hard/time-consuming
Designs
Within-groups / repeated measures
- Test same group 2+ times (all do each treatment) - multiple scores / participant
- BUT could anything else be responsible? occurred naturally?
- Pros: less participants, eliminates individual diff, more powerful
- Cons: can't use if carryover effect, time-related factors, may be too long
- Counterbalancing: admin in diff order - removes time effects (fatigue, practice) - complete or partial
Between-groups / independent groups
- Compare different groups (e.g. treatment vs control)
- Participants do 1 treatment - 1 score / participant
- BUT groups might not have been equal to begin
- Pros: always option, no time effects
- Cons: more participants, large individual diff. have effect, assignment bias
Mixed: between-groups + within-groups
- Randomise to control vs treatment
- Measure both groups before & after
For causality
- IV precedes DV
- Relationship between IV & DV
- Rule out other causes
Controlling extraneous variables
- Hold constant, but limits generalisability
- Balance/match, but can't control for many
- Random assignment, but hard for small groups
- Quasi-experimental
- Address causality (not infer) - control groups, multiple measures
- Has confounding variable (usually pre-existing groups)
- Pros: addresses causality, sometimes only option if experimental impossible/unethical
- Cons: non-equivalent groups, limited control of extraneous variables, assignment bias
- Non-experimental
- Examine relationship (no causality)
- No manipulation
- Pre-existing groups
- Pros: quicker, higher external validity
- Cons: no causality, low internal validity
- Correlational
- Describe relationship
- Pros: non-intrusive, high external (no manipulation), good for prelim, good if unethical/impossible to manipulate
- Cons: no causality, low internal, 3rd variable problem + directionality problem
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Validity
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Balancing internal/external validity: increasing one often decreases other
- More control => more internal, less external
- More real world => more external, less internal
Open Science
- Collaboration, pre-registration
Developmental
Cross-sectional: groups tested once at same time; between subjects
- Pros: time efficient, no selective attrition, no practice effects
- Cons: individ. changes not assessed, cohort effects
Longitudinal: test participants > once; within subjects
- Pros: no cohort effects, assess individual change
- Cons: attrition bias/selective attrition, practice effects, reduced generalisability, biased sample
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Longitudinal-sequential: 2+ groups, followed longitudinally
- Pros: detect cohort effects, cross-sectional & longitudinal comparisons
- Cons: expensive
Sampling
Populations vs Samples
- Population: entire set of interest
- Target: of interest + hard to reach all
- Accessible: of interest + can reach
- Sample: set selected from pop, to represent pop, participate in study
Representative samples
- Representative sample: sample with same characteristics as population
- Important for generalising
- Biased sample: sample that has diff characteristics to population, because of
- Sampling error: diff b/n sample & pop. due to people selected
- Chance
- Selection / sampling bias: sampling procedure favours some people over others,
e.g. non-response bias (people who respond are diff)
- Small sample size
- Non-sampling / measurement error: error due to how observations are made, e.g. poor instruments / procedures, interviewer effect, respondent effect, knowing study purpose, induced bias
- Sampling: process of selecting individuals
Probability Methods
- Entire pop. known (size, individuals)
- Specified probability of selection
- Unbiased / random: equal chance of selection
-> impractical/impossible, rarely used
Simple random
- Every individual has equal chance of selection
- With replacement: independent
- Without replacement: not selected > once
- List all members
- Randomly select
- Can be biased because of chance
Systematic
- Same as simple random to select 1st participant
- Then select every nth participant
Stratified random
- Select equal-sized, random samples from subgroups
- -> All subgroups equally represented, for comparing subgroups
Proportionate stratified random
- Select random samples, so sample proportions match pop. proportions
- -> Sample composition matches pop. composition
Cluster
- Select random pre-existing groups of people
- -> Quick, easy, BUT less independent
Non-Probability Methods
- Entire pop. not known (size, individuals)
- Odds of selection not known
- Biased method of selection - common sense, ease
-> greater risk of biased sample
Convenience
- aka accidental, haphazard sampling
- Use easy to get, available & willing
- Pros: easy, cheap, timely
- Cons: little control over representativeness, likely biased
Quota
- Fill quotas for subgroups
- Can match population %
Purposeful
- Select info-rich cases, knowledgeable, available
- For in-depth study
- Often used in qual research
- Not trying to generalise
Codesign
- Participants involved in decisions that affect them
- Best practice for Indigenous research
Ethics
- Pregnant women / foetuses
- Children / young people
- Dependent / unequal relationships
- Dependent on medical care / unable to consent
- Cog impairment, intellectual disability, mental illness
- Illegal activities
- Other countries
- ATSI people
Qual Research
Approaches
- Phenomenology: meaning
- Ethnography: organisation / culture, immersive
- Grounded theory: dev theory from real world data, iterative data collection / analysis
Methods
- Interviews
- Structured, semi-structured, unstructured
- Face-to-face, phone; one-on-one, group (6-12)
- Observation: participant, structured, 'episodes'
- Document analysis
Analysis
- Themes: common thread in data
- Prelim: emerging themes, ID gaps, ongoing analysis
- Thematic analysis: stop when saturated, coding
Principles
- Purposiveness: purpose -> RQ -> method -> data -> analysis
- Congruence: fit / consistency between problem -> RQ -> method -> data -> analysis in way of thinking
- RQ or data may demand qual approach
Why / when?
- Little known or complex
- Discover new theory
- Understand in detail / how participants understand
- Accuracy, not reproducibility
Scales
Definitions
- Construct: abstract concept
- Variable: concrete representation
- Operational definitions: specified methods/procedures for measuring variables - repeatable, objective
- No single variable or op. def. completely captures construct -> use multiple
- Allows re-measurement
Generating operational definitions
- Define construct -> generate operational definition
- Nomological net: define in terms of other constructs (based on theory & common sense)
- Interviews & theme analysis: collect qual data -> themes -> def
- Research: lit review, theory -> def
- Direct observation: note behaviours -> def
- Expert judgement: ask expert -> def
Constructing self-report measures
- Pros: direct, easy, cheap
- Cons: easy to distort
- Types
- Open-ended Q: flexible, access true thoughts BUT hard to compare/summarise, limited by willingness/ability of participants
- Restricted Q (limited options): easy to analyse/summarise, quant info BUT less flexible
- Rating-scale Q (numeric scale): easy to analyse/summarise (numeric interval), easy to understand/answer BUT response set
- Likert: continuum, numbers with equal spacing, agree/disagree, average?/sum
- Semantic differential: pairs of bipolar
- 5-10 categories: can't discriminate more
Assessing scales
- Does construct exist?
- Useful?
- Valid?
- Reliable?
- Items appropriate?
Validity & Reliability
- Need reliability to be valid
- Do NOT need validity to be reliable - can be reliable BUT not valid
Validity
- Whether measures what it claims to
- Face: looks like it measures what it claims to (superficial)
- Concurrent: related to established measure
- Predictive: predicts behaviour (according to theory)
- Construct: works as theory / past research suggests - grows gradually
- Convergent: 2 diff methods for same construct -> related
- Divergent: same method for 2 different construct -> not related
Reliability
- Consistency (with same people & conditions)
- Large error -> low reliability:
- Observer error: measurer
- Environmental changes
- Participant changes
- Types
- Test-retest: for successive measurements - time sampling
- Inter-rater: for simultaneous measurements
- Internal consistency: compares groups of items (e.g. split-half, Cronbach's alpha) - content sampling
Items
- Sensitivity: discriminates high / low
- Simple: brief, clear, unidimensional, no double-barrelled, no double -ves
- No item bias: emotional, personal, inaccessible
- Response set: tendency to give same response to most Q -> use alternate responses (+ve & -ve), but -ve places extra demand
- Infrequency check: checks random responses
Administering surveys/scales
- Interview:
- Mail:
- Phone:
- Internet: economical, efficient, flexible BUT not representative, non-response bias, hard to control respondents
- Non-response bias: individuals who complete survey not representative
- Interviewer bias: interview influences responses (e.g. tone, rephrasing Q)