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Research Consolidation slides Flashcards

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1
Q

Aim

A

Broad, general, long-term

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2
Q

Objectives

A

Specific, focused, short-term, measurable

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3
Q

Research questions

A

Rephrase objectives to focus on variables

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4
Q

Qualitative

A

Descriptive

Phenomenological

Ethnographical

Grounded theory

Participatory action research
(PAR)

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5
Q

Quantitative

A

Experimental (hypothesis testing)
* Randomised controlled trials
* Quasi-experimental trials

Non-experimental (descriptive,
correlational)
* Cross-sectional
* Cohort
* Case-control

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6
Q

Qualitative vs Quantitative

Focus

A

Qualitative: Quality (features)

Quantitative: Quantity (numbers)

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7
Q

Qualitative vs Quantitative

Reasoning

A

Qualitative: Usually inductive

Quantitative: Usually deductive

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8
Q

Qualitative vs Quantitative

Goal

A

Qualitative: Understand

Quantitative: Predict, test hypotheses

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9
Q

Qualitative vs Quantitative

Sample size

A

Qualitative: Small, purposive

Quantitative: Large, general

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10
Q

Qualitative vs Quantitative

Data collection

A

Qualitative: Interviews, observations

Quantitative: Questionnaires, experiments

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11
Q

Qualitative vs Quantitative

Data analysis

A

Qualitative: Researchers’ interpretation

Quantitative: Statistical methods

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12
Q

Qualitative vs Quantitative

Results/findings

A

Qualitative: Usually verbatim quotes

Quantitative: Usually precise numbers

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13
Q

Quantitative research question

A

PICO

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14
Q

Literature review vs Systematic review

Purpose

A

LR: Provide context/background
information, not meant to answer
research question.

SR: Identifies, selects, synthesises, and appraises studies that meet prespecified inclusion criteria to answer a research question.

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15
Q

Literature review vs Systematic review

Protocol

A

LR: No protocol

SR: A-priori protocol is developed and published
(PROSPERO)

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16
Q

Literature review vs Systematic review

Search

A

LR: Nil, normally includes well-known
articles

SR: Well-defined, comprehensive search strategy

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17
Q

Literature review vs Systematic review

Methodological appraisal

A

LR: NIL

SR: Internal validity is judged by various tools eg
ROB

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18
Q

Literature review vs Systematic review

Synthesis

A

LR: Usually narrative

SR: Narrative, meta-analysis, meta-synthesis

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19
Q

Literature review vs Systematic review

Findings

A

LR: Not reproducible

SR: Reproducible

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20
Q

Observational studies

A

Cohort studies
Cross-sectional studies
Case-control studies
Case reports

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21
Q

Experimental studies (causal r/s)

A

Randomized
controlled trials

Quasi-experimental
studies

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22
Q

Synthesized evidence

A

Umbrella
review

Meta-
analyses

Systematic
reviews

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23
Q

Steps to perform a systematic review

A

Find a good topic

Formulate clear and well-defined research question

Develop systematic review protocol

Conduct systematic search strategy

TiAb and full-text screening using eligibility criteria

Methodological appraisal

Data extraction & organisation

Data analysis

Evidence quality appraisal

Write: integrate, synthesise, summarise

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24
Q

Qualitative research study
design definition

A

A type of research method that collects non- numerical data for in-depth understanding of phenomenon in their natural setting.

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25
Qualitative research study design Purpose
Explore a phenomenon (e.g. perception, meaning, experience) that is vague - Groundwork for quantitative study when there is insufficient insights - E.g. why people behaviour a certain way? * Explain a quantitative result
26
Formulating research question: Types of inquiry
Ontological (understand participants’ realities) Epistemological (understand knowledge of phenomenon)
27
Qualitative design: Descriptive
Describe and interpret perceptions/meanings.
28
Qualitative design: Grounded theory
Collect rich data on a topic to inductively develop theories.
29
Qualitative design: Phenomenological
Understand a phenomenon by describing and interpreting participants’ lived experiences.
30
Qualitative design: Ethnography
Researchers immerse themselves in target groups to understand culture.
31
Qualitative design: Participatory action
Both researchers and participants conduct research together to drive social change.
32
Data collection methods
In-depth interviews *Individual vs focus group - Semi-structured vs Unstructured * Observations + field notes - Use of 5 senses * Surveys with open-ended questions * Secondary data * Existing texts, images, audio-recordings, video-recordings
33
Aim of In-depth interview techniques
Aim: Evoke thick and rich responses to obtain in-depth information
34
In-depth interview techniques
Build rapport + participant information * Anonymity & confidentiality * Permission to audio-tape record * Develop an interview guide with open-ended questions * More Why? How? Talk less, listen more → use prompts, silence DO NOT use leading questions
35
Sampling methods
1. Convenience 2. Purposive 3. snowball 4. Theoretical
36
Convenience sampling
Volunteers through advertisements
37
Purposive sampling
Non-probability sampling based on criteria set beforehand
38
Snowball sampling
Recruited participants to recommend others
39
Theoretical (grounded-theory) sampling
Decide on next target participant as collection continues
40
Sample size normally based on
data saturation: when no more new information emerges
41
Descriptive (most basic) sample size
>12 (Clark & Braun, 2013)
42
Grounded theory
20-30
43
Phenomenological
~10
44
Ethnography
25-50
45
Focus group
≥3 groups, each 7-10 participants
46
Basic data analysis method slide
47
General data analysis steps
1. Prepare: Materials for data analysis Transcript (include context of data collection) e.g. situation (time and date), environment (private room or in the open space), facial expression (e.g. facial grimace when talking about sensitive issues) 2. Immerse/familiarise: Iterative reading 3. Code: Label patterns/meaning units 4. Allow themes & subthemes to emerge
48
6-steps thematic analysis
1. Familiarize with data 2. Generating initial codes 3. Searching for themes 4. Reviewing themes 5. Defining and naming themes 6. Producing the report
49
1. Familiarize with data
Transcribing data, reading and rereading the data, noting down initial ideas
50
2. Generating initial codes
Coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code
51
3. Searching for themes
Collating codes into potential themes, gathering all data relevant to each potential theme
52
4. Reviewing themes
Checking if the themes work in relation to the coded extracts (Level 1) and the entire data set (Level 2), generating a thematic ‘map’ of the analysis
53
5. Defining and naming themes
Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells, generating clear definitions and names for each theme
54
6. Producing the report
Selection of vivid, compelling extract examples, final analysis of selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis
55
Computer-assisted data analysis
E.g. Nvivo, Atlas.ti, MaxQDA
56
Trustworthiness
A set of strategies used to establish trust or confidence (Lincoln, 1989; Morse, 2015)
57
58
Trustworthiness table
58
Rigor in Qualitative study
59
Quantitative research study design, Key dimensions to consider:
Experimental vs non-experimental * (RCT, quasi-experimental to identify causal r/s) vs (e.g. descriptive, correlational, comparative) -Cross-sectional vs longitudinal Snapshot vs change over time * Retrospective vs prospective
60
True Experimental (RCT)
PreTest-posttest control group Posttest-only control group
61
Quasi-Exp
Non-equivalent Control group pretest-posttest One group pretest-posttest Time series design
62
Non-Exp
Descriptive Descriptive Correlation Comparative design
63
True experimental research (RCT)
Gold standard for testing causal relationships * Also called pretest-posttest design with randomization * Non-equivalent pretest-posttest is called quasi-experimental trial Characteristics: * Intervention: manipulation of IV * Control group * Random assignment (group assignment by equal chance to eliminate confounding factors, allowing us to ascertain that DV is indeed caused by IV)
64
Randomization
Minimise selection bias through allocation concealment Trials with unclear randomization shown to overestimate interventional effects by 40%(Schulz & Grimes, 2002) Best to have different people performing different steps of randomization to prevent bias during - Participant recruitment - Participant allocation - Intervention administration - Outcome assessment
65
Quasi Experimental research Strength
Practical it is difficult or impossible to deliver an innovative treatment randomly to some people but not to others People are not always willing to be randomised in clinical trials
66
Quasi Experimental research Weakness
Weaken the cause and effect relationship Absence of randomisation -> implied change in DV= effect of IV + initial group difference in internal factor Absence of control group -> implied change in DV = effect of IV + effect of unknown external factor
67
One group pretest-posttest design
68
Non-experimental research
Descriptive * E.g. Examine the quality of life among patients with CHD Correlational * Examine relationship between variables * E.g. Examine the relationship between medication adherence and quality of life in patients with CHD Comparative * To compare variables between samples * E.g. A comparative study on health-related quality of life between patients with MI and DM
69
Cross sectional vs longitudinal
70
Retrospective vs prospective

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