Data
collection
Types of measurement
Interval level measurement
"Real"
measurements, such as height and weight, the size of the difference between
measurements can be assessed.
Ordinal level measurement
Where is
possible to say that one is bigger than an other, so data items may be ranked
relative to each other, e.g. 1. no
infestation 2. slight infestation 3. moderate infestation 4. heavy infestation 5. severe infestation
Ordinal
means ability to order but it is not possible to say 4 is twice as infested as
2.
Nominal or catogoral level
measurement
A Piece
of data can only be attributed to a particular category. Categories can not be
ranked or ordered, e.g. gender distribution, HIV status, type of IV fluid
Data collection may be direct or indirect
Indirect
Indirect is data which has
been collected by other researchers or institutions and may come from;
1. Official statistical
material, Office of Population and Census Surveys, Local councils, Health
Authorities.
2. Scientific publications
3. Data files from past
research
Direct
Measurement at interval
level, e.g., numbers of things, pulse rate, units of alcohol consumed
Observation, overt or
covert, participant on non-participant, need to account for
Instruments such as
Questionnaires
Use of open and closed
questions
Likert –type responses
Interviews, structured or
unstructured
Group interviews
Vignette
Possible errors in qualitative data collection
Trustworthiness - Honesty of researcher and the participant
Confirmability - Consistency and repeatability of decision making processes
Transferability - The degree to which findings may be applied to another individual or
group
Credibility- The confidence that the researcher can have in the truth of their
findings
Possible errors in quantitative data collection
Introduction of human
subjectivity in group allocation
More than one assessor
Poorly constructed and
ambiguous questionnaires
Related to principles of
reliability and validity
Sampling errors
Sampling
Sampling involves the selection
of a group of people, events or behaviours to enter into a study.
The individual components in
a study are referred to as the elements, when elements are people they are
called subjects.
The sample must be
mathematically selected to accurately represent the whole population under
study.
Sampling or eligibility criteria
These are developed from the
research question.
Inclusion and exclusion
criteria.
If the sampling criteria are
good the sample should yield results which may be generalised to the whole
target population.
Representativeness
The accessible population
must be representative of the target population.
Sampling error
This is the difference
between the sample statistic and a population parameter.
Sampling error reduces the
power of a study to identify differences between groups.
Sampling error may introduce
random or systematic variations.
Randomization
Every individual in a target
population should have the same chance of being selected into a sample.
All subjects should be randomly
selected and the randomly allocated to an experimental or control group.
In order to select from a population
randomly every individual in a population must be identified, this is called
the sampling frame. Individuals are then selected from the sampling frame.
A sampling plan should be
used to reduce bias and promote Representativeness.
In many research studies a
convenience sample is used.
Sample size must be
adequate, more will be needed to detect subtle effects.
Identify the type of data being
given in this table;
Name Eye
Social Temperature Income
Colour Class `C
Kim Hanson Red
4 36.8 £97 000
John
Campbell Blue 3 17 £3 250
Jean
Longrigg Green 1 40`C £103 002
Cath Boyes
Likert
–type scales
How often
do you fall asleep in lessons?
Always Often Occasionally Never
Attending
research lessons causes;
No stress Extreme
stress
John
Campbell is good looking;
Strongly
agree
Agree Disagree Strongly
disagree
Sampling
How would
you obtain a random sample of voters to predict the result of a general election
in the UK