Data Collection Flashcards
1
Q
D: Experimental Designs
A
- manipulated IV; recorded DV
- clear causal relationships (bear in mind validity/reliability/design)
2
Q
M: Correlational Designs
A
- all variables measured; strength of association assessed
- possible causation via theoretical considerations
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
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
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
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
7
Q
Data Collection Methods
A
- the optimal method/combo must always be chosen
- include: OBSERVATIONAL METHODS, CASE STUDIES, SURVEYS, INTERVIEWS, EXPERIMENTS, NEW TECHNOLOGIES
8
Q
M: Observational Studies
A
- beh observed in natural setting; pps how about observation (overt) or not (covert)
- CONTROLLED/NATURALISTIC/PARTICIPANT
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?
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
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
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
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
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
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
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)
