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AUD spring 2020 research > week 8: research design > Flashcards

week 8: research design Flashcards

1
Q

research design is a plan that includes (4 things)

A
  • participant selection criteria
  • controlling extraneous variables
  • variables of interest and conducting observations
  • ensuring ethical procedure
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2
Q

is a good design enough to show the scientific value of a research project?

A

no! a good hypothesis is as important

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3
Q

what are 3 things that make a good hypothesis

A
  • propose a clear relationship
  • testable
  • must “make sense” in other words it can’t just be a random guess
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4
Q

what is a good research design created to do

A
  • answer the research question/questions

* control extraneous variables to ensure a high internal validity level

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5
Q

4 steps to building a good research study

A
  • identify the population
  • sampling protocol
  • select appropriate design
  • select appropriate statistical analysis
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6
Q

population

A

refers to all of the persons of interest for a particular study

  • defined in the planning phase of the study
  • all members must have one or more predetermined characteristic in common
  • –note that population can also be defined as a group unit (all first grade classrooms in the U.S.)
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7
Q

parameters

A

numbers from observations on the entire population

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8
Q

statistics

A

numbers from observations on a sample

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9
Q

intended population

A

all persons whom the researchers want to apply their results to

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10
Q

accessible population

A

the group from which the researchers actually recruit their participants

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11
Q

census

A

when researchers attempt to gather data from all members of a population

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12
Q

why are participant selection criteria critical

A
  • first they need to be based on established standards
  • need a representative sample
  • affect internal validity, ability to generalize, and ability to replicate the study
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13
Q

what is a representative sample

A
  • includes individuals from each constituency in the target population including minorities
  • simple sampling is usually sufficient, but if not then uses stratified sampling
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14
Q

what is a sample

A

refers to the persons within the population that actually participate in the study

  • –use term participants not subjects
  • –important to recruit a sample that represents the population well
  • –avoid systematic exclusion
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15
Q

inferences

A

conclusions drawn in an indirect way

  • –researchers make inferences about the population based on the data gathered from the sample
  • indirect because did not study the entire population
  • the inference accuracy depends on how well the sample represents the population
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16
Q

what is the most common approach to group research

A

studying a sample and inferring characteristics of the population as a whole
*value of research depends on how well the sample represents the population

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17
Q

unbiased vs biased sample

A
  • unbiased= all members of the pop have a equal opportunity to be selected
  • biased= some members of a pop have an unequal opportunity, or no opportunity, of being selected
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18
Q

bias sources for samples

A
  • failing to identify all members of a population (could be due to differences in the accessible and intended pop or because of a biased sampling method)
  • sample of convenience–using a group of participants who are easy to access
  • volunteerism–cannot be avoided but because they have to do informed consent everyone who chooses to partake might have a common factor
  • **to minimize bias, use different random sampling methods
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19
Q

simple random sampling

A

every member of the population has an equal chance of being selected for the study

  • assign all participants an identifying number, and use a table of random numbers to select participants
  • could also use a spreadsheet to do this
  • –“RandBeween” function on excel
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20
Q

systematic sampling

A

generally yields a sample free from intentional bias

  • start with a list of potential participants, establish a sampling interval, and select every so many participants according to the number representing your sampling interval
  • –the first will be random
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21
Q

stratified random sampling

A

helps increase the likelihood that the sample accurately represents the population of interest

  • –strata or criteria that characterize the pop are identified
  • –individuals are chosen at random from each sub-pop (strata)
  • –percentage is to be similar to the population ex 50% males and females
22
Q

cluster sampling

A

obtain a random sample of predefined groups (med centers, classrooms, etc)

  • oftentimes cluster sampling is combined with simple random sampling to create multistage sampling
  • –begin with cluster sample list and select a certain # of clusters at random
  • –predefined groups or clusters tend to be more similar to one another than the population as a whole, use in studies with relatively large # of patients
23
Q

purposive sampling

A

goal is not to generalize findings to a large group, but to obtain expert opinion or personal accounts of people with a unique experience or personal perspectives

  • –want to recruit the best source of info
  • when the answer to a question requires input from a special persons, must make purposeful effort to id these individuals
24
Q

evaluating the participant selection procedures of studies

A
  • is difficult if the participants are not thoroughly described, causing the internal validity to suffer
  • bad description implies carelessness in sampling
25
Q

random assignment to groups

A

required in assigning to experimental and control groups

*can use random number table or spreadsheet

26
Q

4 reasons why random assignment to groups is important

A
  • adds validity to stats tests
  • minimized confounding variables
  • randomization while using blinding will eliminate bias
  • ethical aspects meaning all participants are treated equally
27
Q

randomization creates groups that are

A
  • balanced for individual characteristics
  • free of experimenter selection bias
  • –will be balances when the sample size is 200+
28
Q

what are randomization options for groups with sample size less than 100

A
  • block randomization
  • stratified randomization
  • minimization
29
Q

block randomization

A
  • assigns participants to groups in block
  • –block size must be divisible by the number of groups
  • –each pattern has an equal chance of being selected with each pattern deciding where the next participants will go
  • problem is it is possible to guess some participant allocations so use blinding
30
Q

stratified randomization

A

good for small sample size to put into groups

  • the smaller the size of the study, the more likely there will be random imbalance between groups
  • create a block randomization list for each strata
  • impractical to stratify more than 2-3 variables
31
Q

minimization

A
  • not a method of randomization technically, aims to minimize imbalance
  • –1st participant is randomly assigned to a group
  • –each following is assigned to a group so that any imbalance between groups is minimized
  • advantage is it produced balanced groups and controls for variables
  • disadvantage is it violates assumption of randomness, assignment are easily predicted
  • use allocation concealment or hide assignment from investigator
32
Q

power

A

design sensitivity to detect significance when present

  • –poor sensitivity = increased type 2 error
  • recommended power is 80%
33
Q

what is power analysis affected by

A
  • stat test
  • internal validity of type I error (a=0.05)
  • measurement of reliability (effect size)
34
Q

how can design sensitivity be calculated

A

g power test

35
Q

what are 3 types of single group designs

A
  • single group observed in two or more conditions
  • –no scientific comparability
  • –no control group
  • pre-test/post-test (most common)
  • –test sensitization can be a problem)
  • –statistical regression
  • –appropriate with experimental, hard with quasi experimental
36
Q

two group designs benefit

A

allow for control of confounding variables

  • involve 2+ groups at different levels of the independent variable
  • –one is treatment other is no treatment
  • –looking for a treatment effect
37
Q

parallel design for two group design

A

participants are assigned randomly to experimental and control groups

38
Q

cross-over design for two group design

A
  • participants alternate between treatment and control conditions and act as their own controls
  • requires smaller number of participants
  • suffers from the danger of carryover effects
39
Q

independent research design for two group design

A

*requires a random assignment of participants to experimental and control groups

40
Q

quasi-experimental design

A
  • when it is not possible to assign participants to groups randomly
  • –attempt to match groups on the basis or critical variables
  • –success depends on how well groups are matched
  • –id extraneous variables and match based off of them
41
Q

experimental design

A

random assignment of participants to groups

42
Q

how could you account for extraneous variables in two group designs

A
  • pretest/posttest design

- –problems with sensitization

43
Q

three ways to increase the complexity of a research design

A
  • add conditions to the independent variable
  • add independent variables
  • increase the number of groups
44
Q

multivalent research designs

A

*two or more levels of the same independent variable

45
Q

factorial research designs

A

*2+ independent variables all with different levels

46
Q

dependent (related) designs

A

factorial research design where each participant experiences all treatment conditions

47
Q

independent designs for factorial research

A

matched groups of participants experience a single treatment condition

48
Q

mixed designs for factorial research

A

combination of related and independent variables

49
Q

interaction with factorial designs

A

interpretation becomes increasingly difficult as the number of independent variables increase

50
Q

multiple group designs

A
  • include two or more comparison groups as well as an experiments
  • each comparison group controls for one or more variables
  • example is 3 groups each with a different degree of HL: mild, moderate, severe

AUD spring 2020 research (7 decks)

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