Research Consolidation slides Flashcards
Aim
Broad, general, long-term
Objectives
Specific, focused, short-term, measurable
Research questions
Rephrase objectives to focus on variables
Qualitative
Descriptive
Phenomenological
Ethnographical
Grounded theory
Participatory action research
(PAR)
Quantitative
Experimental (hypothesis testing)
* Randomised controlled trials
* Quasi-experimental trials
Non-experimental (descriptive,
correlational)
* Cross-sectional
* Cohort
* Case-control
Qualitative vs Quantitative
Focus
Qualitative: Quality (features)
Quantitative: Quantity (numbers)
Qualitative vs Quantitative
Reasoning
Qualitative: Usually inductive
Quantitative: Usually deductive
Qualitative vs Quantitative
Goal
Qualitative: Understand
Quantitative: Predict, test hypotheses
Qualitative vs Quantitative
Sample size
Qualitative: Small, purposive
Quantitative: Large, general
Qualitative vs Quantitative
Data collection
Qualitative: Interviews, observations
Quantitative: Questionnaires, experiments
Qualitative vs Quantitative
Data analysis
Qualitative: Researchers’ interpretation
Quantitative: Statistical methods
Qualitative vs Quantitative
Results/findings
Qualitative: Usually verbatim quotes
Quantitative: Usually precise numbers
Quantitative research question
PICO
Literature review vs Systematic review
Purpose
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.
Literature review vs Systematic review
Protocol
LR: No protocol
SR: A-priori protocol is developed and published
(PROSPERO)
Literature review vs Systematic review
Search
LR: Nil, normally includes well-known
articles
SR: Well-defined, comprehensive search strategy
Literature review vs Systematic review
Methodological appraisal
LR: NIL
SR: Internal validity is judged by various tools eg
ROB
Literature review vs Systematic review
Synthesis
LR: Usually narrative
SR: Narrative, meta-analysis, meta-synthesis
Literature review vs Systematic review
Findings
LR: Not reproducible
SR: Reproducible
Observational studies
Cohort studies
Cross-sectional studies
Case-control studies
Case reports
Experimental studies (causal r/s)
Randomized
controlled trials
Quasi-experimental
studies
Synthesized evidence
Umbrella
review
Meta-
analyses
Systematic
reviews
Steps to perform a systematic review
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
Qualitative research study
design definition
A type of research method that collects non- numerical data for in-depth understanding of phenomenon in their natural setting.
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
Formulating research question: Types of inquiry
Ontological (understand
participants’ realities)
Epistemological (understand
knowledge of phenomenon)
Qualitative design: Descriptive
Describe and interpret perceptions/meanings.
Qualitative design: Grounded theory
Collect rich data on a topic to inductively develop
theories.
Qualitative design: Phenomenological
Understand a phenomenon by describing and interpreting participants’ lived experiences.
Qualitative design: Ethnography
Researchers immerse themselves in target groups to understand culture.
Qualitative design: Participatory action
Both researchers and participants conduct research together to drive social change.
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
Aim of In-depth interview techniques
Aim: Evoke thick and rich responses to obtain in-depth
information
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
Sampling methods
- Convenience
- Purposive
- snowball
- Theoretical
Convenience sampling
Volunteers through advertisements
Purposive sampling
Non-probability sampling based on criteria set
beforehand
Snowball sampling
Recruited participants to recommend others
Theoretical
(grounded-theory) sampling
Decide on next target participant as collection continues
Sample size normally based on
data saturation: when no more
new information emerges
Descriptive (most basic) sample size
> 12 (Clark & Braun, 2013)
Grounded theory
20-30
Phenomenological
~10
Ethnography
25-50
Focus group
≥3 groups, each 7-10 participants
Basic data analysis method slide
General data analysis steps
- 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)
- Immerse/familiarise: Iterative reading
- Code: Label patterns/meaning units
- Allow themes & subthemes to emerge
6-steps thematic analysis
- Familiarize with data
- Generating initial
codes - Searching for themes
- Reviewing themes
- Defining and naming
themes - Producing the report
- Familiarize with data
Transcribing data, reading and rereading the data, noting down initial ideas
- Generating initial
codes
Coding interesting features of the data in a systematic fashion across the entire data set, collating
data relevant to each code
- Searching for themes
Collating codes into potential themes, gathering all data relevant to each potential theme
- 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
- 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
- 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
Computer-assisted data analysis
E.g. Nvivo, Atlas.ti,
MaxQDA
Trustworthiness
A set of strategies used to establish trust or confidence (Lincoln, 1989; Morse, 2015)
Trustworthiness table
Rigor in Qualitative study
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
True Experimental (RCT)
PreTest-posttest control group
Posttest-only control group
Quasi-Exp
Non-equivalent Control group pretest-posttest
One group pretest-posttest
Time series design
Non-Exp
Descriptive
Descriptive Correlation
Comparative design
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)
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
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
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
One group pretest-posttest design
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
Cross sectional vs longitudinal
Retrospective vs prospective