RESEARCH DESIGN Flashcards
PICO
P- population/patient/problem
I- intervention
C- comparison
O- outcome
research design is overall plan for
answering research question
your questions should have
- nature of comparison
- type of setting
- population, sample
- independent and dependent variable
comparisons between 2 groups or more
- single group: 2 or more points in time (pre/post)
- single group under different circumstances or experiences (group v individual therapy)
- based on relative rankings (severe v mild autism)
- compare with samples from other studies
research designs methods to be used to control
variables (isolate dependent and independent variables)
control variables
external things that will affect dependent and independent variables
research designs timing and frequency of
data collection (when, relative to other events) overtime looking at change
research designs setting
(naturalistic v laboratory)
research designs nature of communications with subjects
(fully divulge or not)
are you going to tell them they’re subjects
dimensions along which designs can be described
- experimental vs non-experimental
- degree of structure imposed
- time dimension
- type of group comparisions betwene subjects and within subjects
- non-experimental
experimental
quantitative
non-experimental
qualitative
degree of structure imposed
how much we are controlling
time dimension
- longitudinal
- cross-sectional
multiple points of data collection secondary to
- study time related processes
- determine time sequences
- developing comparisons
- enhancing research control
trend studies
- looking at impact of something that is happening over time
- multiple periods of time
- observing what’s going on with trends
cohort
- when you take small population (by age)
- study them over time with respect to a phenomenon (subject)
panel studies
take same cohort and measure across 2 periods of time
follow up studies
how they feel about same topic in a period of time (5 years down the road)
cross section cohort
comparing 2 different cohorts
follow up study can be
panel study
type of group comparisons between subjects and within subjects
2 subjects at the same time
within subjects
in same group of people (more control of variables)
between subjects
different group of people
non-experimental
not manipulating anything (qualitative)
- retrospective
- prospective
retrospective
- have an outcome
- want to knwo what causes are (antecedent) study prior to habits
prospective
- know causing variables
- going forward in time
- see how many develop outcomes
cons of retrospective
- memory might not be accurate
- might get defensive
- might not be telling the truth
usually do prospective after you gain evidence from
retrospective
good design must be
appropriate to question asked
good design minimize
biases that can distort results of study
biases result from
- differences among participants in groups (non-random groups)
- researcher’s preconceptions minimize this (can do double blind study)
- use triangulation to reduce bias
good design precision must be enhanced
sensitivity with which effect of independent variable relative to effects of (confounding) extraneous variable can be detected, want it high
good design power should be adequately dealt with
- ability of design to create maximal contrast amongst group being compared
- one can detect relationship between variables
true experimental designs
- manipulation
- control
- randomization
manipulation
independent variables
control
not getting any treatment
outside things impact treatment
randomization
pick random from different population
- extreme view will be cancelled
- equal chance that they can be picked for any group
examples of randomization
- table of random #’s
- flipping a coin
- most reliable method for equating groups on all possible characteristics that could effect outcome of study
cluster randomization
picking people in clusters, random, pick a bunch to represent zip code
matching
people in each group match and have equal numbers
experimental designs: basic
- posttest only
- pretest/posttest
posttest only
someone’s been given a treatment and measuring outcome after the fact
pretest/posttest
measuring outcomes before and after
solomon four group design
take into account influence of pretesting on subsequent posttest results
factorial design
1 independent and 1 dependent
main effects
1 variable
interaction effects
combine independent varibales
repeated measures design (cross over)
each patient is randomly assigned to a sequence of treatments
randomized control trials
population receiving the program or policy intervention is chosen at random from eligible population, and control group is also chosen at random from the same eligible population
quasi experimental designs
- manipulation and control or randomization
- non-equivalent control group design
- time series designs
- time series- non-equivalent control group design
non-experimental designs
- why choose them?
- 2 broad classes
- ex-post facto research
- descriprive
ex-post facto research
after-the-fact research is a category of research design in which the investigation starts after the fact has occurred without interference from the researcher
descriptive
- descriptive correlational
- univariate descriptive
- retrospective
- prospective
other non-experimental types
- survey research
- evaluation research
- needs assessment
- secondary analysis
- meta analysis
- historical research
- case studies (single subject designs)
strategies in a research study
- triangulation
- mixed methods
- fully integrated
triangulation
collecting data from 3 ways
mixed methods
mixing methods from different parts of continuum
fully integrated
different methods work hand in hand
integrated research designs 2 broad categories in multi-method research
- component
- integrated
component
- triangulated
- complementarity
- expansion
integrated
- iterative
- embedded or nested
- holistic
- transformative
advantages of experimental design
most powerful method available for testing hypotheses of cause and effect relationships between varaibles
disadvantage of experimental designs
- many situations in which experimental design is impossible
- secondary to ethical or protocol considerations
- artificiality of setting
- randomness not always real
hawthrone effect
- placebo effect
- knowledge of being included in study may be sufficent to cause people to change their behavior
study could have double hawthorne effect
- when those conducting study also change their behvaior
- therefore, do double blind study so neither researcher nor subject know
repeated measures design
- one group tested under all conditions
- within subjects design
repeated measures design strong design because of
ability to control potential influence of individual differences
repeated measure design disadvantage is
- practice effects
- exposure to test
- one way to combat this is increase length of time between treatments, so subject’s scores can go back to baseline
one way repeated measure design
1 group of subjects exposed to all levels of 1 independent variable
- order effects can occur
- that is potential biasing effect of test sequence
- so randomize order of presentation for each subject
- 1 way ANOVA test
ANOVA
- Analysis of variance
- find out whether differences between groups of data are statistically significant
crossover design
when there are only 2 levels of independent variable
- 1/2 recieve treatment A, then treatment B
- 1/2 recieve treatment B followed by treatment A
- again add time in between to remove any testing effect or treatment residual effect
- 2 way ANOVA, paired t-test
paired t-test
interested in the difference between two variables for the same subject
two way design with two repeated measures
- 2 independent variables with 2 levels each
- subjects get all 4 conditions
- 2 way ANOVA
correlational
degree of association among variables
-it is function of covariation in data
descriptive correlational
describe nature of existing relationships
univariate descriptive
1 variable
restrospective
examine data collected in past
prospective
current data and follow up in future
survey research
statistical
evaluation research
how well something is doing (standardize)
what is going on
needs assessment
collect data on what place is like/what they need may have multiple needs
secondary analysis
take existing data set ask different research question and analyze
meta analysis
take a bunch of studies that are asking same question and study in different population combine statistically (quanitiative)
content analysis
can be numerical/qualitative
frequency of idea
historical research
go back and look at old records/artifacts to recreate history
case studies
look at individual in depth every client factor
look at them holistically
