MindMap Gallery Quantitative and qualitative research Comparison
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Quantitative and qualitative research Comparison
Quantitative and qualitative research are two distinct approaches used in the field of research and analysis. Each method has its own characteristics, strengths, and limitations, and they are often used in combination to provide a comprehensive understanding of a research topic. Here's a comparison of quantitative and qualitative research.
Edited at 2022-09-02 21:55:20
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Quantitative and qualitative research: Comparison
Quantitative research
operates with variables
variable: any characteristic that is objectively registred and quantified
Constructs
any theoritical defined variable ex. love, agression, violence, memory
to enable research, constructs needs to be operationalized
Operationalization
expressing in terms of observable behaviour
Types:
Experimental studies
1 IV and 1 DV
IV manipulated by the researcher, the DV is expected to change as the IV changes
Cause and effect interference
Sampling
Random
create list of all the members of the target population and randomly select a sub set
Stratified
Stratified sampling is a sampling technique where the researcher divides or 'stratifies' the target group into sections, each representing a key group (or characteristic) that should be present in the final sample.
Self selected
recruiting volunteers
Convenience
recruiting participants that are easily available
Generalizability
the extent to which the resilts of the study can be applied beyond the sample and the settings used in the study itself
External validity
characteristic of generalizability of findings to other people and situations
Population validity
the extent to which findings can be generalized from the sample to the target population
Ecological validity
the extent to which findings can be generalized from the experiment to other settings or situations
Construct validity
a characteristic of the quality of operationalizations
Credibility
thge extent to which results of the study can be trusted to reflect reality
Internal validity
a characteristic of the methodological quality of an experiment
controlling confounding variables
Bias
flipside of credibility
threats to internal validity
selection
history
maturation
testing effect
instrumentation
regression to the mean
experimental mortality
experimenter bias
demand characteristics
Correlation studies
No variables
Sampling
Random
Stratified
Self selected
Opportunity
Generalizability
Construct validity
Population validity
Bias
on the level of interpretation of findings
curvilinear relationships
cannot be captured in a standard correlation coefficient should be investigated graphically
the third variable problem
is there perhaps a third variable?
spurious problem
correlations obtained by chance
Descriptive studies
relationship between variables are not investigated- approached separately
aim
nomothetic approach
data
numbers
focus
behavioural manifestation
objectivity
more objective- the researhcer is eliminated from the studied reality
The experiment
confounding variables
other variables that can interfere in the relationship between the IV and the DV
the target population
the group of people to which the findings of the study are expected to be generalized
the sample
the sample must be representative of the target population
Experimental designs
independent measures
the IV is manipulated by randomly allocating participants into different groups rationale: all potential confounding variables cancel each other out
advantages
can have multiple groups
no order effect- harder to figure out the aim of the study
disadvantages
participant variability
how to overcome the disadvantages
groups has to be large enough so when randomly allocating into groups the individual differences will cancel each other our
matched pairs
researchers use matching to form the groups
advantages
groups do not need to be large
researcher can keep certain pre-known confounding variables constant in all groups
disadvantages
theory driven: what variables are likely to be confounding?
matching variables needs to be measured first
how to overcome the disadvantages
keeping the experiment simple- one matching variable, two groups
matching variable
repeated measures
the same group of participants is exposed to two or more conditions and the conditions are compared
advantages
no problem with the participation variability
sample can be small
disadvantages
order effect
how to overcome the disadvantages
counterbalancing, minimum of conditions
order effect
counterbalancing
the order of trials in the group is reversed. ex. first group -music-, -no music- second group -no music- , -music-
Types of experiments
true laboratory experiment
allocation into experimental groups is done randomly cause and effect relationship
quasi- experiment
allocation into groups is done on basis of preexisting differences no cause and effect relationship
field experiment
natural experiment
Correlational studies
no variable is manipulated by the researcher cause and effect inference cannot be made two or more variables are measured and the relationship is mathematically quantified
correlation
a measure of linear relationship between two variables -1, +1
effect size
statistical significance
Qualitative research
Sampling
Quota sampling
pre-decided number and characteristics of participants
Purposive sampling
size and special characteristics are not defined in advance recruit whoever is of interest to the researcher
Theoretical sampling
stops collecting data when data saturation has been reached
Snowball sampling
invited participants invites friend of theirs to become participants as well
Convenience sampling
qualitative research methods
observation
naturalistic observation
laboratory observation
overt observation
participants are aware of the fact that they are being observed
covert observation
participant observation
nonparticipant observation
structured observation
unstructured observation
interview
structured interview
semi structured interview
unstructured interview
focus group
case study
content analysis
Generalizability
Sample to population generalization
Case to case generalization
Theoretical generalization
aim
idiographic approach
data
texts
focus
human experiences, interpretations, meanings
objectivity
more subjective- researcher included in the studied reality
Credibility
Bias
participant bias
acquiestance
a tendency to give a positive answers whatever the question
social desirability
dominant respondent
sensitivity
researcher bias
confirmation bias
leading question bias
question order bias
sampling bias
biased reporting
thick descriptions
credibility checks
establishing a rappport
building a relationship of trust with the participant
triangulation
method triangulation
data triangulation
researcher triangulation
theory triangulation
iterative questioning
epistemological reflexivity
personal reflexivity