Data Acquisition Approach ll Flashcards
1
Q
Primary data collection method (3)
A
- Experimental method
- lab experiment
- controlled trials - Observation method
- Survey method
- in-depth interview
- focus group discussion
- questionnaire
2
Q
Issues regarding questionnaire constructions (5)
A
- Structural
- Content
- Language
- Pre-testing
- Reliability & validity
3
Q
Steps in constructing questionnaire (4)
A
- Determine what information is needed
- Drafting of questions
- Pre-testing of questionnaire
- Revision of questionnaire, if needed
4
Q
Structural issues (2)
A
- mode of delivery of questionnaire
- open-ended or close-ended
5
Q
General format
A
- Introductory statement
- Demographics questions
- can be last section also - Factual questions
- Opinion questions
- Closing statements & return instructions
6
Q
Question context (2)
- administration
- delivery of questionnaire
A
- Administration of questionnaires
- self administered
- interviewer-administered - Delivery of questionnaire
- mails / emails
- telephone
- in person
- determines how questions & response options are constructed
7
Q
Types of question formats (2)
A
- Open-ended
- Close-ended
- checklists
- rankings
- rating scales
8
Q
Examples of rating scales (5)
A
- Unipolar scale
- Bipolar scale
- Likert scale
- Visual Analogue Scale (VAS)
- Wong-Baker face scale
9
Q
Advantages of open-ended response (4)
A
- more detailed answers
- can study respondents’ interpretations expressed in their own words
- minimise successful guessing (knowledge question)
- useful to wrap up entire survey at the end
10
Q
Disadvantages of open-ended response (7)
A
- Less structured
- Difficult to ensure systematic recording of response (if interviewer administered)
- Difficult to maintain unbiased while adequately probe for more complete/understandable answer
- More difficult for respondents to answer compared to close-ended response
- More time required to complete such question
- ~75% of respondents will leave such questions blank (if self-administered)
- Difficult to code information for data analysis
- wider variety of response
11
Q
Advantages of close-ended response (4)
A
- Tightly structured
- only options for respondents to choose from - Ensures standardisation of response
- Easily encoded & analysed
- Less time taken to collect response
- respondents take a shorter time to answer
12
Q
Disadvantages of close-ended response (4)
A
- Less depth in answers
- Imposes researchers priorities on respondent
- May bias responses if range of options are not exhaustive
- Presentation formats may affect responses
13
Q
How to write good questions? (4)
A
- be clear
- be concise
- provide complete options in close-ended questions (avoid gaps or overlap categories)
- bias & leading questions
14
Q
Clarity in questions by avoiding __ (4)
A
- big words
- technical jargons (use layman terms)
- double negatives
- double-barreled questions
eg questions with or, and
15
Q
Language issues (3)
A
- translate to other languages
- cross cultural adaptation of survey instrument (translate to appropriate language)
- back translate to ensure meaning of questions is retained
16
Q
Pre-testing (4)
A
- pilot test with a group of respondents
- ensure all questions are understood as intended, otherwise rephrase to capture intended meaning
- check length of questionnaire
- ensure questionnaire is able to obtain adequate information to answer research question
17
Q
Strengths of self-administered questionnaire (3)
A
- Cheap to administer
- Less susceptible to interviewer bias
- Can be administered via mail / email
18
Q
Limitations of self-administered questionnaire (3)
A
- Lower response rate
- lead to non-response bias - Difficult to elicit detailed response
- Less control over how questionnaire is filled out
19
Q
Strengths of interviewer-administered questionnaire (3)
A
- Higher response rate
- More detailed responses can be elicited
- Greater control over how questionnaire is filled out
20
Q
Limitations of interviewer-administered questionnaire (3)
A
- Expensive to administer
- More susceptible to interviewer bias
- More time consuming
- interviewer has to be present to collect response
21
Q
Response rate
A
= (no. of participants who completed the questionnaire)/(total no. of eligible persons who were asked to participate)
22
Q
Concerns over low response rate (3)
A
- leads to non-response bias as respondents may differ from non-respondents in their characteristics, hence answers of respondents may differ from the potential answer of non-respondents
- compromise internal validity
- weakens external validity & generalisability of survey results
23
Q
When to use mail/email mode of delivery of questionnaire? (6)
A
- Geographically disperse sample groups
- Directed to specific groups
- can obtain specific emails - Limited research budget & manpower
- Provide time for respondents to think before answering
- Provide privacy for respondents
- Questions should be closed-ended, simple & clear
24
Q
When to use telephone interviews? (2)
A
- Open-ended questionnaire
- Complex interviews
eg req skipping of questions depending on respondent’s previous answer
25
CATI
Computer Assisted Telephone Interviews
- directly key in respondent's answers into computer system
- ensure quality & speed
26
When to use in-person interviews? (3)
1. Open-ended questionnaire
2. Complex interviews
eg req skipping of questions depending on respondent's previous answer
3. Knowledge-based questions
27
CAPI
Computer Assisted Personal Interviews
- directly key in respondent's answers into computer
- ensure quality & speed
28
Practicality considerations when using mail/email to delivery questionnaires (3)
1. Accessibility of sample
- req specific emails/mailing address
2. Low response rate (5-30%)
3. Think of ways to increase response rate
eg incentives, provide return envelope with postage, reminders, deadlines
29
Practicality considerations when using telephone interviews (2)
1. Accessibility of sample
- req contact details
2. Good response rate (65-75%)
30
Practicality considerations when using in-persons interviews (2)
1. Accessibility of sample
- req locations of sample
2. Good response rate (70-80%)
31
Turn Around Time (TAT)
= (no. of surveys to be collected x duration of each interview) / (no. of interviewers x no. of hours each interviewers works per day)
32
Census
Entire population
33
Sampling
- a subset of the population
34
Sample size calculations for Questionnaires (2)
=/ sample size calculator for clinical trials
1. Cochran's formula
- large sample size
- small sample size (>5%)
2. Yamane's formula
- fixed 95% CI
- p=0.5
35
Cochran's formula (large population)
no = [(Z^2)(pq)] / (e^2)
no = sample size calculated
Z = Z score of normal distribution = 1.96 at 95% CI
p = estimated proportion of an attribute that is present in the population
= 0.5 (if unknown)
q = 1-p
e = desired level of precision
= 0.05
36
Cochran's formula (small population)
- when no/N > 5%
n = no / [1+[(no-1)/N]]
```
n = sample size for small population
no = sample size calculated for large population
N = population size
```
37
Yamane's formula
- assuming 95% CI and p=0.5
n = N / [1+N(e^2)]
n = sample size calculated
N = population size
e = desired level of precision
= 0.05
38
Types of sampling (2)
1. Random / Probability sampling
| 2. Non-random / Non-probability sampling
39
Types of Random / Probability sampling methods (4)
1. Simple random sampling
2. Systematic random sampling
3. Stratified random sampling
4. Cluster sampling
40
Types of Non-random / Non-probability sampling (3)
1. Convenience sampling
2. Quota sampling
3. Snow-ball sampling
41
Simple random sampling (2)
- every subject has an equal probability of being selected
| - req a full list
42
Systematic random sampling (4)
- req a full list
- generate a random number to determine the _th item to select
- generate another random number from 1 to _ to determine the starting number
- _ is determined by dividing population size by desired sample size
43
Stratified random sampling (2)
- divide population into relevant strata
| - random sampling from each strata
44
Cluster sampling (3)
- divide populations into clusters
- randomly select a subset of clusters
- all units or random sample units within subset of clusters are surveyed
45
Convenience sampling
- select participants whom access is easy
46
Quota sampling
- reserve a certain proportion of participants to particular types of people
eg gender
47
Snow-ball sampling (2)
- selected participants nominate others whom they know could be interviewed
- good to use if target population is difficult to be identified / accessed
eg drug addicts
48
Secondary data sources (6)
1. Prescription/medical & dispensing records
2. Registry data
* *
3. Claims databases
4. Cross-sectional survey data
5. Large prospective cohort data
6. Spontaneous reporting / surveillance data
49
Data sources with either exposure data or outcomes data only (2)
- req record linkage
eg via IC
- combining the information from both data belonging to same individual into one record so that the same person is counted only once
50
Strengths of medical/prescription/dispensing records (7)
1. Clinical data available
eg labs
2. Best source for disease outcome
3. Efficient
- data already collected, large sample size & long follow up data
4. Prospectively collected data
5. Detailed information on medical records
6. Can study many drugs in relation to many outcomes
7. Can conduct nested case-control studies
51
Limitations of medical/prescription/dispensing records (5)
1. Compliance to medication unknown
2. Non-prescription drugs missed
eg OTC & GSL
3. Diagnosis based on codes / texts
4. Incomplete information about habits, past history & other potential confounders
5. Uncertain completeness of data from other physicians & sites of care
- incomplete data
52
Types of registry data (2)
1. National Registry of Disease Office (NRDO)
- cancer
- renal
- stroke
- acute myocardial infarction
- donor care (liver & renal)
2. Drug use registry
eg pregnancy registries for anti-epileptic drug use
53
Strengths of registry data (4)
1. Details & systematically collected clinical data
2. Representative of patients / drug users
3. Inexpensive if data already available
4. High reliability of data
- periodic audits
54
Limitations of registry data (4)
1. Minimal information on drug use
2. No appropriate control or comparison groups
- can use other drug users as a comparison or other disease as a control
3. Expensive if data not readily available & have to create one
4. Missing data
55
Considerations in collecting/using secondary data for use (4)
1. Reliability
- data collection process
- reproducibility
2. Suitability
- can the data collected answer the research question
3. Adequacy of data
- completeness of data
4. Ethical considerations of data use
- may involve IRB
- PDPA
- de-identification of data
