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Research Methods > Final Exam > Flashcards

Final Exam Flashcards

1
Q

sampling plan

A

specifies in advance how participants are to be selected and how many to include
to obtain an accessible sample to make an inference on target population

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2
Q

Target population

A

who you are trying to make an inference about

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3
Q

accessible population

A

aggregate of cases that conform to designated criteria and that are accessible for the study

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4
Q

sampling

A

process of selecting cases to represent an entire population, to permit inferences about the population

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5
Q

elements

A

basic units about which data are collected, usually humans

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6
Q

two goals of sampling plans

A
  1. representativeness of the general population

2. adequate size

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7
Q

if the population is not representative, what type of validity is threatened

A

external and construct

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8
Q

strata/stratum

A

subpopulations, mutually exclusive segment of a population defined by one or more characteristics
Ex: high school degree or w/o

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9
Q

multistage sampling

A

samples are selected in multiple phases
First Phase - large units are selected (i.e. hospital)
Next stage, smaller units are sampled (patients)

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10
Q

bias

A

systematic overrepresentation or underrepresentation of a population subgroup on a characteristic relevant to the research question

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11
Q

what drives sampling plan strategy

A 
feasibility
Ethics
Desired rigor
Convenience
Costs
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12
Q

probability sampling

A
  • Samples are randomly selected
  • Everyone in population has an equal chance of being selected
  • Used to control sampling bias
  • Useful when focus is on population diversity
  • Used when researcher needs to ensure accuracy
  • Finding correct target population is not simple
    (ex: public health initiatives)
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13
Q

non-probability sampling

A

Samples are selected based on researcher’s judgment
Not everyone has equal chance to participate
Sampling bias is not a primary concern
Useful in environment that shares similar traits
Does not help representation of entire population in terms of accuracy
Finding target population is very simple

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14
Q

when do you use probability sampling (3)

A
  1. reduce sampling bias
  2. when population is diverse
  3. to create an accurate sample
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15
Q

relationship between selecting a representative sample and sample size

A

Probability of selecting an unrepresentative sample decreases as size of sample increases

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16
Q

most basic type of probability sampling

A

simple random

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17
Q

stratified random sampling

A

population is divided into 2 or more homogenous strata form which elements are selected at random

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18
Q

proportionate stratified random sampling

A

participants are selected in proportion to the size of population stratum (i.e. race)

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19
Q

cluster sampling

A

involves selecting groups rather than selecting individuals as the first stage of a multistage approach

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20
Q

systematic sampling

A

involves selecting every kth person from a list

k = Divide N (population size) by n (sample size)

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21
Q

non probability convenience sampling

A

using the most conveniently available people as participants

Weakest form of sampling but most commonly used

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22
Q

snowball sampling

A

variant of convenience sampling → early sample members (seeds) are asked to refer other people who meet the eligibility criteria

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23
Q

quota sampling

A

non probability sampling
one in which the researcher identifies population strata and determines how many participants are needed from each stratum

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24
Q

consecutive sampling

A

recruiting all the people from an accessible population who meet eligibility criteria over a specific time interval or for a specified sample size

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25
Q

judgmental/provisional sampling

A

uses researcher’s knowledge about the population to make decisions

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26
Q

5 threats to statistical conclusion validity

A 
Low statistical power
Effect size - small, moderate effects need a larger sample size
Heterogeneity of the population
Cooperation
Attrition
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27
Q

5 steps in sampling

A

Identification of the population
Specification of the eligibility criteria
Specify the sampling plan: decide method of drawing the sample and how large it will be (i.e. w/power analysis)
Recruit the sample: screening instruments
Generalizing from samples

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28
Q

which studies do not need a power analysis

A

Descriptive and exploratory studies, and non randomized trials

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29
Q

5 things required for sample size calculations

A

Significant level desired (a)
Power level of test desired (1 - beta)
Desired sample size (n)
Effect size desired (d) or Cohen’s d

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30
Q

magnitude b/w variables: small effect

A

.20

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31
Q

magnitude b/w variables: moderate

A

0.50

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32
Q

magnitude b/w variables: large effect

A

.8

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33
Q

Type I error

A

rejection of null hypothesis H0 when it is true
Concluding a relationship exists when it fact it does not
False positive

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34
Q

type II error

A

accepting the H0 when it is false
Concluding no relationships exists when it fact it does
False negative

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35
Q

how do you avoid type I error

A

by setting alpha at level they are comfortable with usually .05 or .01

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36
Q

how to control type II

A

by setting power (1 - beta) at 80% or 20% risk of committing a type II error

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37
Q

counterfactual

A

Expressing what has not happened but could, would or might under differing conditions
Ex: if researcher is doing an intervention - need to think what could, would or might happen under a different situation if intervention wasn’t done - what hasn’t happened (natural course of condition over time or intervention/condition that would influence outcomes)

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38
Q

3 criteria for causality

A

Temporal - IV before DV (cause before effect)
Relationship b/w IV and DV
No confounders

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39
Q

what type of study design enhances causality

A

experiemental, strongest - strongly controlled, minimizes bias

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40
Q

true experiments need what 3 things

A

intervention (manipulation)
control condition
randomization

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41
Q

blinding (masking)

A

concealing whether participant in intervention or control - conceal form participant, providers, data collectors, data analyst

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42
Q

single blinded

A

one group of participants does not what group is randomized, intervention or control

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43
Q

double blinded

A

those receiving intervention and those delivering intervention don’t know which group participants are in

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44
Q

placebo effects

A

changes in the outcome attribute to the placebo condition b/c of participants expectations of benefits or harm

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45
Q

complete randomization

A

no restrictions, allocate each person as they enroll into a study on a random basis - should only be used for studies of 200 or more

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46
Q

simple randomization

A

starting with a known sample size and then pre specifying proportion of subjects who will be randomly assigned to different tx conditions

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47
Q

gold standard for randomization

A

have someone unconnected w/ enrollment perform the treatment allocation

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48
Q

steps in RCT (6)

A 
Screen for eligibility of the study
Obtain informed consent
Collect baseline data
Randomly assign to condition
Administer control or intervention
Collect outcome data
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49
Q

basic experimental design

A

two groups and 1 intervention and 1 control group w/ outcome measure

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50
Q

Pre test post test design

A

you measure outcome twice: before and after intervention

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51
Q

post test only design

A

data on outcome are only collected once - after randomization and completion of the intervention

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52
Q

factorial design

A

manipulating 2 or more things
I.e. weight gain of infants - touch therapy, music therapy and control group
Can look at interventions separately and together
Look at interaction effect

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53
Q

crossover design

A

involves expsoing the same people to more that one condition

Must randomly assign participants to different orderings of treatment

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54
Q

concerns with cross over design and how to mitigate

A

Must be wary of carry over effects -when people are exposed to 2 different conditions, they may be influenced in the second condition by their experience in the first one
Can mitigate with a washout period - no treatment

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55
Q

hawthorne effect

A

caused by people’s expectations/knowledge of being in the study appears to affect peoples behavior

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56
Q

quasi-experiments

A

controlled trials without randomization, control group or both

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57
Q

Non equivalent control group design:

A

involves 2 groups of participants for whom outcomes are measured before and after intervention
Weaker because it cannot be assumed that the experimental and comparison groups are initially equivalent

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58
Q

time series design

A

data are collected over an extended period during which an intervention is introduced
Extended time period strengthens ability to attribute change to intervention
ex: Ex: rapid response teams were implemented in acute care units → administrators want to examine effects on patient outcomes –> Compare mortality rate before implementation and 3 months after

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59
Q

major strength of quasi-experimental studies

A

practical, mimics real world

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60
Q

limitation of quasi experimental

A

Could be other explanations for what happened (i.e. population is different) - rival hypotheses

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61
Q

descriptive correlation studies

A

to describe relationship among variables rather than to support inferences of causality

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62
Q

univariate descriptive studies

A

studies involves multiple variables but the primary purpose is to describe status of each, not to study correlation

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63
Q

Prevalence studies

A

done to estimate prevalence rate of some condition at a particular point in time
Cross sectional designs

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64
Q

incidence studies

A

estimate frequency of new cases

Need longitudinal designs to estimate incidence

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65
Q

retrospective design

A

ones in which a phenomenon existing in the present is linked to phenomena that occurred in the past
Begin with DV and then examines whether it is correlated with one or more previously occurring IV

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66
Q

prospective non experimental design

A

cohort design - researcher start w/ a presumed cause and then go forward in time to the presumed effect

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67
Q

nominal

A

mutually exclusive categories/groups but no hierarchy

involves assigning numbers to classify characteristics into categories

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68
Q

ordinal

A

ranked, sorted groups (highest to lowest), involves sorting people based on their relative ranking on an attribute
Doesn’t tell us about how greater one level is from another

I.e. education level

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69
Q

interval

A

occurs when researchers can assume equivalent distance between rank ordering on an attribute
Ex: temperature scale

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70
Q

ratio

A

interval level data which has a true zero (absence of a factor), the intervals between objects and the absolute magnitude of the attribute because there is rational meaningful zero

I.e. speed - 0 = not moving, person’s weight

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71
Q

+ skewed

A

longer tail points to the right

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72
Q
  • skewed
A

tail points to the left

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73
Q

Unimodal distribution

A

has only one peak - a value with high frequency

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74
Q

Multimodal distribution

A

two or more peaks

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75
Q

mode

A

most frequently occurring score value in a distribution

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76
Q

median

A

point in a distribution above and which 50% of cases call - the midpoint
Usually reported if the data is skewed

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77
Q

mean

A

sum of all scores divided by the number of scores = average

Affected by every score

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78
Q

which is most stable - median, mode or mean

A

mean b/c it accounts for every data point

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79
Q

range

A

highest - lowest

Subtract lowest data point from the highest

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80
Q

variance

A

spread/dispersal of the data

Heterogenous or homogenous

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81
Q

standard deviation

A

average variance from mean, based on every score

More stable because it’s based on every score

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82
Q

inferential stats

A

Allows researchers to draw conclusion about a population, given data from a sample and permits inferences about whether results are likely to be found in a population

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83
Q

sampling error

A

tendency for statistics to fluctuate from one sample to another; the challenge is how to decide whether estimates are good population parameters

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84
Q

sampling distribution of the mean

A

theoretical distribution of a test statistic (i.e. mean) from an infinite number of samples as data points

85
Q

standard error of the mean

A

standard deviation (SD) of a sampling distribution of the mean (i.e. estimated from the sample’s SD and the sample size)

86
Q

the smaller the SEM…

A

the less variable the sample means → more accurate a single mean as estimate of the population value

87
Q

what are the 2 forms of statistical inference

A

estimation of parameters and hypothesis testing

88
Q

who are you estimating for in an estimation of parameters

A

the entire population

89
Q

what are the 2 forms of parameter estimation

A

point estimation and interval estimation

90
Q

point estimation

A

calculating a one value/single statistic (i.e. a sample mean) to estimate the population parameter (i.e. population mean)

91
Q

interval estimation

A

calculating a range of values the parameter (i.e. population mean) has a specific probability of being located
- interval at which we are confident it lies between

92
Q

confidence limits

A

range of values for the population and probability of being right with a certain degree of confidence (95% or 99%)

93
Q

binomial distribution

A

probability distribution of the number of successes in a sequence of independent yes/no trials each of which yields success with a specified probability

94
Q

hypothesis testing

A

For a particular sample or group in research context

Provides objective criteria for deciding whether hypotheses are supported by data

95
Q

how to avoid type I error

A

significance (a or p-value) of 0.05 or 0.01

96
Q

how to avoid type II error

A

controlled by setting power (1-B) at 80%

increase sample size

97
Q

bivariate tests

A

analysis of 2 variables to assess empirical relationship b/w them

98
Q

what does a signifcance level of .05 mean

A

we accept the risk that out of 100 samples drawn from a population, a true null hypothesis would be rejected 5 times

99
Q

non signifcant result

A

relationship is from chance fluctuations (accept H0)

100
Q

significant result

A

means that the H0 is improbable and thus statistically significant (reject H0 and accept HA)

101
Q

two tailed tests

A

hypothesis testing in which both ends of the sampling distribution are used to define the region of improbable values
5% of sampling distribution is equally split b/w 2 tails: 2.5% on each side

102
Q

one tailed test

A

critical region of improbable values is entirely in 1 tail of the distribution - corresponding to direction of they hypothesis set by the researcher
5% of the sampling distribution is on 1 side
must know direction of hypothesis in advance! can allow detection of small differenes

103
Q

parametric tests/stats

A

involve estimation of a parameter, require measurements on at least an interval scale and involve several assumptions

104
Q

non-parametric tests

A

Does not estimate parameters
Uses a rank ordering procedure
Uses variables/data on a nominal or ordinal scale

105
Q

when do you use a test for independent groups

A

when comparisons involve different people (i.e. men and women), and study uses a b/w subjects design

106
Q

when do you use a test for dependent groups

A

within subjects design, single group of people

compare same participants over time or various conditions - related to one another

107
Q

if you run a large number of tests on the same data, what type of error increases

A

type I (false +)

108
Q

chi-square test

A

A statistical test used to determine if group differences in a cross-tabs (or proportion of categories in 2 group variables) differ from one another

109
Q

pearson’s r - correlation coefficient

A

Designates magnitude of relationship (strength and direction) b/w two variables measured on at least an interval scale, can be used b/w groups and within group situations

110
Q

multivariate analysis

A

Statistical procedures for analyzing inter-relationships among 3 or more variables

111
Q

simple linear regression

A 
Regression analysis is used to predict outcomes
1 IV (x) is used to predict a DV (y)
112
Q

what does Y’ = a +bX stand for

A 
basic linear regression eq
Y’ = predicted value of DV y
A = intercept constant
B = regression coefficient
X = actual value of IV
113
Q

MLR equation

A 
Y’ = a + b1X1 + b2X2
Y’ = predicted value of variable Y (DV)
a = intercept constant
b1 = regression coefficient for variable X1
X1 = actual value of variable X1
b2 = regression coefficient for variable X2
X2 = actual value of variable X2
114
Q

simultaneous multiple regression

A

enters all predictors into the regression eq at the same time

115
Q

hierarchical multiple regression

A

involves entering predictors into eq in a series of steps

116
Q

stepwise multiple regression

A

Sequentially add predictors based on ordering the IVs according to their predictor power, evaluate fit at each step

117
Q

elimination multiple regression

A

Reverse of the forward stepwise - place all IV predictors in up front and remove them 1 at a time if they do not contribute to overall eq, evaluate fit at each step

118
Q

what is R and what does it show?

A

Multiple correlation coefficient

shows strength of relationship between several IV and a DV but not the direction

119
Q

ANCOVA

A

Analysis of covariance
Extension of ANOVA by removing effect of extraneous variables (CoV) before testing whether mean group differences are statistically significant

120
Q

what two things are used in interpreting mLR results

A

z scores and beta weights

121
Q

MANOVA

A

multivariate analysis of variance
Tests for sig differences in 2 or more groups on 2 or more (interval or ratio level) DV outcomes simultaneously (i.e. SBP and DBP)

122
Q

adjusted means

A

r/t to ANCOVA

dependent variable after removing the effects of covariates

123
Q

MANCOVA

A
  • Multivariate analysis of covariance

allows for control of confounding variables when there are 2 or more outcome variables

124
Q

logistic regression

A

used to predict categorical outcomes

Predicts a categorical DV (i.e. compliance) based on relationships b/w 2+ IV predictors w/ any level of measurement

125
Q

odds ratio

A

ratio of the odds of an event in one group to the odds of an event in another group

126
Q

what does an OR of 1.0 represent

A

an OR of 1.0 indicates no differences b/w groups

127
Q

mixed methods research

A

planned integration of qualitative and quantitative data within single studies or a coordinated series of studies

128
Q

meta inference

A

conclusion generated by integrating inferences from the results of the qual and quant strands of an MM study

129
Q

3 advantages of MM studies

A

Complementarity
Practicality
Enhanced validity

130
Q

4 disadvantages of MM research

A

Requires a researcher to be competent in both methods - methodologically bilingual
All members of the team need this dual method expert skill set
Expensive
Mixed methods studies may be of longer duration than single method studies

131
Q

what is the central feature of MM research

A

Integration

132
Q

how many research questions are involved in MM studies

A

at least 2

133
Q

what does + sign indicate in MM study

A

indicates convergent design
Purpose is to obtain different but complementary data bout the central phenomenon under study
Qual and quant are collected simultaneously,

134
Q

what does → indicate in a MM design

A

sequential

135
Q

how do you identify the priority of an MM study

A

capital letters

136
Q

which MM designs have qual first

A

exploratory

137
Q

which MM studies have quant first

A

Explanatory designs

138
Q

what does this mean: QUAN + QUAL

A

Qual and quant are collected simultaneously, with equal priority

139
Q

what is the intent of exploratory MM studies

A

Intent is to use rich info to develop a quant feature like a new measure, survey, intervention or digital tool

140
Q

is the sample size larger in quant or qual of MM studies

A

quant

141
Q

MM sampling: identical

A

occurs when same people are in both strands of the study

142
Q

MM sampling: parallel

A

samples in two strands are completely different, but likely drawn from same population
concurrent or sequential designs

143
Q

MM sampling: nested

A

participants in the qual strand are a subset of the participants in the quant strand

144
Q

MM sampling: multilevel

A

involves selecting samples from different levels of hierarchy
Different but related populations (hospital admins, clinical staff, patients)

145
Q

inference transferability

A

degree to which mixed methods conclusions can be applied to other similar people, contexts, settings and time periods

146
Q

inference quality

A

incorporates notions of both internal validity and statistical conclusions validity within quant framework and credibility within qual framework
Refers to the believability and accuracy of the inductively and deductively derived conclusions from an MM study

147
Q

meta inference

A

conclusions are generated by integrating inferences obtained from the results of qualitative and quantitative strands of a mixed methods study

148
Q

quantitizing

A

qual data is converted into numeric codes that can be analyzed quantitatively

149
Q

qualitizing

A

transform quant data into qual information

150
Q

metasyntheses

A

Interpretative translation of abstract phenomena produced by integrating findings from multiple qualitative studies

151
Q

qualitative evidence syntheses

A

SR of qual evidence focused on particular aspects of an intervention, phenomenon, or program, i.e. barriers to participation, satisfaction w/ treatment

152
Q

Mixed study reviews

A

integrate findings from qual and quant studies from mixed methods studies

153
Q

Meta analyses

A

systematic reviews of quant studies - especially those that focus on intervention and use statistical integration
Use each study to develop a common metric = effect size

154
Q

effect size

A

averaged across each study yielding aggregated info about not only the existence of a relationship b/w variables but also estimate of magnitude

155
Q

scoping review

A

preliminary investigation that clarifies the range and nature of evidence base
Addresses broad questions and uses flexible procedures and typically does not formally evaluate evidence quality
Can suggest strategies for full systematic review and can indicate whether statistical integration is feasible
Used to identify areas of further research

156
Q

rapid review

A

done within a period of a few weeks, do not involve statistical integration and involve a less rigorous search for available evidence
Often used to inform emergent decisions facing clinicians in health care settings

157
Q

narrative lit review

A

generic review that identifies and reviews published literature on a topic
Approx time frame = 1-4 weeks

158
Q

living systematic review

A

updated as new research becomes available and are published as online only evidence summaries in rapid formats

159
Q

overview of reviews/umbrella review

A

reviews in which the unit of analysis is another review

160
Q

two types of next generation systematic reviews

A

Individual patient level meta analysis

Network meta analysis

161
Q

integrative reviews

A

Broad, not as tightly defined as SR

Span theory and philosophy and include quant designs

162
Q

systematic review

A

a rigorous synthesis of research findings on an RQ, using systematic sampling, data collection and data analysis procedures and a formal protocol, quant or qual, considered strongest form of evidence

163
Q

advantages of meta-analyses

A

objectivity
enhances power
precision - draws conclusions about intervention’s effect with specified probability the results are accurate

164
Q

PROSPERO

A

international prospective register of systematic reviews

165
Q

Grey literature

A

studies with more limited distribution = dissertations, conference presentations

166
Q

publication bias

A

tendency for published studies to overrepresent statistically significant findings

167
Q

risk of bias

A

refers to the likelihood of an inaccuracy in the estimate of a causal effect = threat to internal validity

168
Q

fixed effects model

A

assumed that a single true effect size underlies all study results and that observed estimates vary only as a function of chance

169
Q

random effects model

A

assumes that each study estimates different yet related true effects and that estimates are normally distributed around a mean effect size

170
Q

if there is little heterogeneity, what kind of results do models yield

A

nearly identical results

171
Q

if there is higher heterogeneity…

A

analyses will yield different estimates of the average effect size
would want to use the random effects model which is more stable

172
Q

forest plots

A

graph the estimated effect size for each study and the 95% CI around each estimate

173
Q

I^2 test

A

adjusts for the number of studies in the analysis

(0-100%, 50% = moderate heterogeneity)

174
Q

sensitivity analysis

A

test of how sensitive the results of an analysis are to changes in the way the analysis was done

175
Q

GRADE

A

two part process - quality of evidence about an intervention’s effect is graded for each outcome –> recommendation is made about using /not using intervention with strength of recommendation (strong or weak)

176
Q

summary of findings table

A

show the results of the meta analysis for each outcome, number of participants and studies on which the effect size was based and then the quality of evidence score

177
Q

what are 2 types of qualitative SRs

A

aggregative and interpretive

178
Q

aggregative qual SRs

A

involving pooling of findings across qual studies in the review

179
Q

which is more structure: aggregative or interpretive

A

aggregative

180
Q

interpretative qual SRs

A

emphasize creation of integrated conceptualizations and theories by interpreting and reconfiguring findings from qual studies
not that structured

181
Q

meta-synthesis

A

Interpretive translation produced by integrating findings from multiple qualitative studies

182
Q

frequency effect size

A

indicates the magnitude of a finding - number of reports with unduplicated info that contain a given finding

183
Q

intensity effect size

A

indicates concentration of findings within each report

184
Q

metadata anlaysis

A

study of results of reported research in a specific substantive area of investigation by means of analyzing the processed data

185
Q

metamethod

A

study of methodologic approaches and rigor of the studies included in the metasynthesis

186
Q

metatheory

A

analysis of theoretical underpinnings on which studies are grounded

187
Q

statistical conclusion validity

A

concerns the validity of inferences that there truly is an empirical relationship/correlation b/w presumed cause and effect

188
Q

internal validity

A

concerns the validity of inferences that, given an empirical relationship exists, it is the independent variable, rather than something else that cause the outcome

189
Q

construct validity

A

validity of inferences from observed persons, settings and cause and effect operations included in the study to the constructs that these instances might represent
Degree to which an intervention is a good representation of the underlying construct that was theorized as having the potential to cause beneficial outcomes

190
Q

external valdiity

A

concerns whether inferences about observed relationships will hold over variations in persons, setting or time
Relates to generalizability o f inferences

191
Q

threats to internal validity

A 
temporal ambiguity
selection bias
history
maturation
mortality/attrition
testing effect
192
Q

threats to external validity

A

represetantiveness and selection effects
interaction effect b/w relationsihps and people
interaction effect b/w causal effects and tx variation

193
Q

threats to statistical conclusion validity

A

low stat power
restriction of range (homogeneity)
unreliable tx implementation

194
Q

threats to construct validity

A 
reactivity to the study
hawthorne effect
researcher expectancies
novelty effects
compensatory effects
tx diffusion/contamination
195
Q

effect size

A

expresses the strength of relationships among research variables
If there is strong correlation b/w IV and DV - may need a small sample size

196
Q

t-test

A

testing for difference b/w 2 group means

197
Q

independent t-test

A

two independent groups

experimental vs. control or pre-and post scores for a group of the same people

198
Q

paired t-test

A

2 measurements from same person over different time or paired participants together

199
Q

1 way ANOVA

A

sum of squares b/w groups and within groups for 3+ groups on 1 IV

200
Q

2 way ANOVA

A

3+groups and 2 IV

201
Q

RM ANOVA

A

repeated measures

3+ groups over repeated times

202
Q

why do you use a post hoc test

A

used to examine where group differences are occuring

203
Q

type 1 or type 2: think there is no change when there really is

A

type 2

204
Q

when to use MM (4)

A

New or poorly understood concepts
One approach enhanced by second sources of data
One approach alone is not effective
Quantitative results confusing to interpret

205
Q

limtiations of descriptive correlation studies

A

self-selection of groups

206
Q

strengths of correlational research

A

can get a large amount of data about a problem

207
Q

central limit theorem

A

when samples are large, the distribution of the sample means tends to be normally distributed.

208
Q

beta weight

A

indication of the relative importance of predictors because scores are standardized.

209
Q

how long does a scoping review take and how many reserachers

A

2-8 weeks

2

Research Methods (2 decks)

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