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Research Methods > Data Collection > Flashcards

Data Collection Flashcards

1
Q

D: Experimental Designs

A
  • manipulated IV; recorded DV

- clear causal relationships (bear in mind validity/reliability/design)

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2
Q

M: Correlational Designs

A
  • all variables measured; strength of association assessed

- possible causation via theoretical considerations

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3
Q

ED: Lab Experiments

A
  • done in lab; greatest environmental control
  • POSITIVES: highly controlled; variables easily excluded; easier to replicate standard procedure
  • NEGATIVES: artificial; unnatural pp beh; ecological validity questioned; demand characteristics affect beh
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4
Q

ED: Field Experiments

A
  • everyday environment; variables still manipulated
  • POSITIVES: natural; more likely IRL responses (high eco validity); less affective demand characteristics (particularly when covert)
  • NEGATIVES: less control; difficult replication; poor reliability
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5
Q

ED: Natural Experiments

A
  • everyday environment; variables NOT manipulated
  • POSITIVES: natural; high eco val; unlikely demand chars (particularly when covert); tool around ethics (ie. stress research)
  • NEGATIVES: costly (time/finance/etc.); no control; randomisation impossible so self-selection/reliability may be an issue
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6
Q

CD: Natural Observations

A
  • often used in when looking for strength of associations
  • POSITIVES: allow study of “unethical” phenomena w/o experiments; establish how well experiments generalise to natural contexts (confirmatory evidence); prediction of direction/strength of variable relationships based on theories.
  • NEGATIVES: can’t establish causation w/o logical relationship sequence
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7
Q

Data Collection Methods

A
  • the optimal method/combo must always be chosen

- include: OBSERVATIONAL METHODS, CASE STUDIES, SURVEYS, INTERVIEWS, EXPERIMENTS, NEW TECHNOLOGIES

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8
Q

M: Observational Studies

A
  • beh observed in natural setting; pps how about observation (overt) or not (covert)
  • CONTROLLED/NATURALISTIC/PARTICIPANT
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9
Q

OS: Controlled Observations

A
  • lab; control over context; systematically classified beh w/categories and timed schedule; coded by +1 researcher for inter-rater reliability
  • CODING: numbers/scale
  • EG: BANDURA (1961); bobo doll aggression; AINSWORTH (1970); attachment styles
  • POSITIVES: easily replicable; easy quantitative stat method/software analysis of data; quick conduct, so large samples
  • NEGATIVES: Hawthorne effect (demand chars limit validity); is overt beh dif?
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10
Q

OS: Naturalistic Observations

A
  • natural beh; data recorded variously
  • CODING: numbers/scales
  • EG: CROFT (2017); killer whale menopause; LEVINE (2011); group violence
  • POSITIVES: informs further studies; high eco validity
  • NEGATIVES: small samples are unrepresentative; difficult to replicate; substantial training required; cause/effect relationships may be impossible
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11
Q

OS: Participant Observations

A
  • alternate naturalistic; researcher = pp; false role/identity “undercover”
  • EG: FESTINGER (1957); cults and cognitive dissonance
  • POSITIVES: same as naturalistic
  • NEGATIVES: challenges w/data recording; lack of objectivity; researcher bias
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12
Q

OS: Sampling Methods

A
  • EVENT; events/coding identified in advance; event frequency/characteristics recorded; all other beh ignored
  • TIME; events/coding identified in advance; obs taken in specific time period (ie. 10min p/h) w/set schedule (ie. each hour for 12h); beh at other times ignored
  • INSTANTANEOUS/TARGET TIME; observations at specific time point; all others ignored
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13
Q

M: Case Studies

A
  • individual/group/event analysed w/detail
  • multiple techniques (ie. observational coding/interviews/psychometric tests)
  • involves IDIOGRAPHIC APPROACH (ie. what happens to pp; reconstructing history of pp/group)
  • often part of CLPSYCH/psychiatry; rich info; further research prompt
  • POSITIVES: rich description; indicated possible new hypotheses; study rare phenomena; opportunity for “unethical” variable investigation
  • NEGATIVES: unable to give cause-effect/causal inference; atypical/unrepresentative focus; ungeneralisable; relies on heavy subjectivity; difficult replication
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14
Q

M: Surveys

A
  • questions given to sample from pop
  • POSITIVES: carefully selected representative sample gives accurate info for pop; large sample = high external reliability
  • NEGATIVES: unrepresentative sample may give misleading info; researcher bias/social desirability risks distortion
  • IN-PERSON; TELEPHONE; MAIL-OUT; WEB-BASED
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15
Q

S: In-Person Surveys

A
  • participants complete survey in presence of researcher
  • POSITIVES: control; reduced biases; explicit instructions/queries; highest quality data
  • NEGATIVES: time-consuming/expensive; small samples
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16
Q

S: Telephone Surveys

A
  • researcher telephones pps
  • POSITIVES: cheap; large samples; anonymous
  • NEGATIVES: less control; less credibility; unavailable/less sophisticated visual materials
17
Q

S: Mail-Out Surveys

A
  • researcher mails survey to pps (snail/email)
  • POSITIVES: cheap; convenient; time for completion; no interviewer bias; visual aids; rapid data collection; anonymous
  • NEGATIVES: full instructions required; self-selection bias
18
Q

S: Web-Based Surveys

A
  • survey made available online w/respondents
  • POSITIVES: convenient; cheapest; completion time; no interviewer bias; visual aids; rapid data collection; anonymous
  • NEGATIVES: full instructions required; self-selection bias
19
Q

M: Interviews

A
  • relatively small respondent number give rich data in “live” interaction (ie. in-person/telephone)
  • POSITIVES: well selected representative sample gives rich info about pop
  • NEGATIVES: unrepresentative sample may give misleading info; researcher biases/social desirability bias risks distortion
20
Q

M: Open VS Closed Questions

A
  • open questions give rich data source but are harder to empirically analyse; qualitative analysis techniques required
  • closed questions give more specific answers but a bias is likely
  • issue w/all surveys or methods involving questions
21
Q

M: Experiments

A
  • IV manipulated; DV measured; lab/field
  • POSITIVES: best method for cause-effect relationships; physically controlling for variables; alternatives ruled out
  • NEGATIVES: various issues w/reliability may threaten validity of conclusions
22
Q

DCM: New Technologies

A
  • technological solutions rapidly expanding; allow data collection previously impossible
  • includes: INTERNET/DIGITAL THERAPY/TELEPSYCHOLOGY; SMARTPHONE APPS; AMBULATORY DATA MONITORING; VR; SOCIAL MEDIA
23
Q

NT: Internet/Digital Therapy

A
  • FAIRBURN & PATEL (2017)
  • POSITIVES: popular; access to secluded groups; clinically relevant changes on large scale; supported interventions more effective than unsupported; supported digital treatments as effective as face to face
  • NEGATIVES: missing direct comparisons of existing treatments for same problem; effects of the digital (ie. age; psychopathologies w/o completion); how are they different to in-face treatments?
24
Q

NT: Smartphone Apps/Ambulatory Monitoring

A
  • RESEARCH: for studying beh/mood/physiological responses in IRL settings
  • INTERVENTIONS: for supporting beh change w/IRL feedback and application of new skills IRL
  • GOAL PROCESSES IN MOOD DISORDERS: goals = life meaning/direction; central to psychotherapeutic work; disrupted goal content implicates mood disorders; IRL measurement (ie. MoodFood, ADEPT, DClinPsy, ERSC work at Exeter Uni)
  • INHIBITION TRAINING: “impulse control”; personalised brain-training leading to new cue-response relationships
25
Q

NT: VR (EG)

A
  • AUSTAKALNIS & BLATNER (1992); “… a way for humans to visualise, manipulate and interact w/computers and extremely complex data”
  • RIZZO et al (1997); advanced human-computer interference; user interacts in more natural/sophisticated way
26
Q

NT: VR

A
  • IMMERSIVE: visual display/body tracking in simulated environment in natural way allowing embodiment (ie. head-mounts; 360 videos)
  • SEMI/NON-IMMERSIVE: modern computers/consoles to show graphics on monitors/projections allowing navigation/interaction (ie. driving simulator; treadmills)
  • SOC APPLICATION: increases pp engagement; simulating situations give new skills in safe environment for therapeutic use; practice for training clinicians w/ simulating VR clients
27
Q

NT: VR (EVA)

A
  • NEGATIVES: coding skills required; “messy data” as it easily gets lost; machine learning/mathematical modelling for advanced VR requires interdisciplinary collaborations; less evidence for complex disorders; few studies to date; assessment requires determination of psychometric properties
  • KEY ISSUES: how immersive must it be to allow IRL transfer; can theory be successful; can therapists be replaced and affordable?
28
Q

NT: Social Media

A
  • access to large pool of hard to reach pps/content; established platforms w/specific blogs/forums which are mines for qualitative data analyses/specific research purposes
  • KEY ISSUES: informed consent; anonymity/confidentiality (BPS guidelines)

Research Methods (10 decks)

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