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Research/Scientific Inquiry I > Validity > Flashcards

Validity Flashcards

1
Q

Define sampling frame

A
  • represents the group of individuals who have a real chance of being selected for the sample
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2
Q

What types of subjects are experiments performed on

A
  • performed on a representative sample of subjects rather than on the entire population of individuals
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3
Q

Describe sampling

A
  • investigators need to establish specific inclusion & exclusion criteria for the subjects in studies
  • without criteria, there are limits to the generalizability of the study results (this concept is referred to as “external validity”)
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4
Q

Define Type I error

A
  • occurs when a difference is found in the study sample but there is in fact no difference present in the population at large
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5
Q

Define Type II error

A
  • occurs when a difference exists in the population at large but the study results reveal no difference in the study sample
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6
Q

What is the most common reason for a Type II error

A
  • inadequate sample size
  • this is also referred to as having low “statistical power” for the study
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7
Q

How is an estimation of sample size performed

A
  • it is done by performing an a priori power analysis
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8
Q

What percentages of Type I and Type II errors are investigators willing to accept

A
  • Type I: 5% risk
  • Type II: 20% risk
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9
Q

Define probability sampling

A
  • involves the use of randomization to select individual potential subjects from the sampling frame
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10
Q

Define nonprobability sampling

A
  • is a method of sampling in which selected subjects are not drawn randomly from the sampling frame
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11
Q

Types of probability sampling

A
  • random sampling
  • systemic random sampling
  • stratified random sampling
  • cluster random sampling
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12
Q

Define random sampling

A
  • is a method of sampling in which every potential individual in the sampling frame has an equal chance of being selected for study participation
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13
Q

Define systematic random sampling

A
  • is method of sampling in which every xth individual out of the entire list of potential subjects is selected for participation
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14
Q

Define stratified random sampling

A
  • provides a method for dividing for dividing the individual members of the sampling frame into groups, or strata, based on specific subject characteristics
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15
Q

Define cluster random sampling

A
  • is a process of dividing the sampling frame into groups based on some common characteristics & then randomly selecting specific clusters to participate in the study out of all possible clusters
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16
Q

Types of non probability sampling

A
  • Convenience sampling
  • Purposive sampling
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17
Q

Why is random sampling considered superior to non-random

A
  • the results of the study are more likely to be representative of the population at large
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18
Q

Define convenience sampling

A
  • a type of sampling in which potential subjects are selected based on the ease of subject recruitment
  • consecutive sampling
  • self selection bias
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19
Q

Define purposive sampling

A
  • a type of non-random sampling
  • it entails potential subjects from a predetermined group to be sought out & sampled
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20
Q

How can quantitative data be obtained

A
  • instrumented devices
  • clinician measurement
  • clinician observation
  • patient self-report
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21
Q

What are the 3 types of measurement data

A

1) Categorical (nominal; 1, 2, 3 categories)
2) Ordinal (1 —–> 5)
3) Continuous

22
Q

Describe categorical/nominal data

A
  • involves a finite number of classifications for observations
  • a numeric value must be assigned to each category
  • the order of numbers assigned to each category is inconsequential
23
Q

Describe continuous data

A
  • measured on a scale that can continuously be broken down into smaller & smaller increments
24
Q

Describe ordinal data

A
  • uses categories
  • the order of the numeric classification is of consequence
  • Ex. likert scales, in which a numeric value is assigned to each possible response
25
Q

Describe internal validity

A
  • refers to the validity of a study’s experimental design
  • internally valid if the experiment can conclusively demonstrate that the independent variable has a definite effect on the dependent variable
  • if other factors influence the dependent variable & these factors are not controlled for in the experimental design, internal validity may be questioned
26
Q

Describe extraneous factors

A
  • internal validity should be thought of as along a continuum rather than as a dichotomous property
  • in lab experiments it is easier to control for confounding factors & thus enhance internal validity than it is in clinical trials
  • confounding variables are extraneous factors that may result in false relationships
  • extraneous factors must be either controlled or quantified
27
Q

Describe validity bias

A
  • potential threats to internal validity often involve some sort of bias
  • bias may be inherent to either the subjects in the study or the experimenters themselves
  • make sure that the subjects in different groups have similar characteristics, by either randomly assigning subjects to treatment groups or utilizing some type of matching procedure
28
Q

Define selection bias

A
  • the characteristics that subjects have before they enroll in a study may ultimately influence the results of the study
  • ex. age, maturation, sex, medical history, injury or illness severity
29
Q

Define delimitations

A
  • decisions that investigators make to improve the internal validity of their studios
30
Q

What 3 entities can be blinded in a study

A
  • the subjects may be blinded to whether they are receiving an experimental treatment or a control treatment
  • members of the experimental team who are performing outcome measures should be blinded to the group assignment of individual subjects & the values of previous measurements for individual subjects
  • clinicians who are treating patients in clinical trials should be blinded to the group assignments of individual subjects
  • blinding is important to the internal validity of a subject
31
Q

Describe external validity

A
  • relates to the degree to which the results of a study are generalizable to the real world
  • the more tightly controlled a study is in terms of subject selection, administration of interventions, and control of confounding factors the less generalizable the study results are to the general population
32
Q

Describe ecological validity

A
  • it is an important issue in terms of translating treatments from controlled laboratory studies to typical clinical practice settings
33
Q

What are the 3 types of validity measures

A
  • Content validity (outcome comprehensiveness): Face validity
  • Criterion validity (outcome comparison): Concurrent and Predictive (high correlation with future criterion)
  • Contract validity: Convergent, Discriminant, and Known groups (different outcomes based on groups)
34
Q

Describe face validity

A
  • refers to the property of whether a specific measure actually assess what it is designed to measure
  • is an important issue in the development of “functional tests” for patients in the rehab sciences
  • is determined subjectively & most often by expert opinion
35
Q

Describe content validity

A
  • refers to the amount that a particular measure represents all facets of the construct it is supposed to measure
  • is similar to face validity but is more scientifically rigorous
36
Q

Describe accuracy

A
  • is defined as the closeness of a measured value to the true value of what is being assessed
  • should not be confused with precision of measurement
37
Q

Describe concurrent validity

A
  • refers to how well on measure is correlated with an existing gold standard measure
  • is an important property to be established for new measures aiming to assess the same properties as an existing test
38
Q

Describe construct validity

A
  • refers to how well a specific measure or scale captures a defined entity
  • stems from psychology but is applicable to other areas of study, such as the health sciences
39
Q

Describe convergent validity

A
  • is the measurement property demonstrating whether a given measure is highly correlated with other existing measures of the same construct
40
Q

Describe discriminative validity

A
  • is indicative of a given measure’s lack of correlation or divergence from existing measures that it should not be related to
41
Q

Define reliability

A
  • refers to the consistency of a specific measurement
42
Q

Describe intratester reliability

A
  • is the ability of the same tester to produce consistent, repeated measures of a test (AKA intrarater reliability & test-retest reliability)
43
Q

Describe intertester reliability

A
  • is the ability of different testers to produce consistent repeated measures of a test (AKA interrater reliability)
44
Q

Describe ICCs

A
  • estimates of reliability for measures of continuous data are often reported as intraclass correlation coefficients (ICCs)
  • ICCs are reported on a scale of 0 to 1
45
Q

Describe Pearson’s r

A
  • assesses the association between two continuous measures across a sample of subjects
  • if as one measure increases in value the second measure also increases incrementally, then Pearson’s r will approach 1
  • Pearson’s r indicates that scores on the two measures are highly correlated
  • it will not show that the scores of the two measures are systematically diverging from each other
46
Q

Describe precision of measurement

A
  • how confident one is in the reproducibility of a measure
  • precision is reported as the standard error of measurement (SEM) in the unit of measure
  • precision takes into account the ICC of the measure as well as the standard deviation(s) of the data set
47
Q

Describe limits of agreement (LOA)

A
  • Bland & Altman recommend that the limits of agreement (LOA) be calculated when two measurement techniques (or two raters) are being compared to each other
  • this technique compares the absolute differences between two measurement techniques & specifically looks for systematic error
  • if the subject difference is zero, the two techniques are identical
  • the LOA represent a 95% confidence interval of the difference between the two measures
48
Q

Describe agreement

A
  • estimates of the consistency or reproducibility of categorical data
  • intrarater & interrater agreement are defined the same as with reliability measures
  • estimates of agreement are reported with the kappa statistic, which also ranges from 0 to 1 with 1 indicating perfect agreement
49
Q

Commonly used nominal statistical test based on number of groups

A
  • 1 group, 2 independent groups, and >2 independent groups use X^2 test
  • 2 dependent groups uses McNemar test
  • > 2 dependent groups uses Cochran Q test
50
Q

Commonly used ordinal statistical test based on number of groups

A

1 group: Kolmogorov-Smimoff 1 sample test
2 independent groups: Mann-Whitney U test
2 dependent groups: Wilcoxon test
>2 independent groups: Kruskal-Wallis ANOVA
>2 dependent groups: Frriedman ANOVA by ranks

51
Q

Commonly used interval/ratio statistical test based on number of groups

A

1 group: t-test of sample mean vs. known population value
2 independent groups: independent samples t-test
2 dependent groups: paired t-test
>2 independent groups: ANOVA
>2 dependent groups: repeated measures ANOVA

Research/Scientific Inquiry I (7 decks)

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