Pt 202 - Research Methods - Quiz #1

Front 1

Inter-rater reliability

Back 1

variation between 2 or more raters who measure the same group of subjects

Front 2

Intra-rater reliability

Back 2

stability of data recorded by one rater across 2 or more trials

Front 3

Variance

Back 3

Differences among scores

Front 4

Reliability Coefficient

Back 4

True Score/True Score + Error Variance = RC
* 1 = best RC
* 0 = worst RC

Front 5

Classic Reliability Theory

Back 5

Single score made up of true score + random error gives the best estimate of actual value.

Front 6

Generalizable Theory

Back 6

Not all variations from trial to trial should be attributed only to random error. Single score made up of true score + various types of error. Must consider specific measure when considering reliability of measure.

Front 7

Regression toward mean

Back 7

Single tests potentially extreme (high or low) score, multiple tests reveal score closer to group average

Front 8

Systematic Measurement Error

Back 8

form of measurement error where error is consistent across trials.

Front 9

Random Measurement Error

Back 9

Change that causes unpredictable measurements from trial to trail

Front 10

test-retest reliability

Back 10

no rater, changes based on re-test.

Front 11

Validity

Back 11

accuracy, measuring what you intended to measure, coming up with the right answer, average values near target. Minimizes systematic error.

applies to measure and study

Front 12

Snowball Sampling

Back 12

TYPE OF NON PROBABILITY(CONVENIENCE) SAMPLING

Have current subjects "tell a friend" to join study

Front 13

Purposive Sampling

Back 13

TYPE OF NON PROBABILITY (CONVENIENCE) SAMPLING

Handpicking based on subective judgement

Front 14

Quota Sampling

Back 14

TYPE OF NON PROBABILITY (CONVENIENCE) SAMPLING

Consecutive sampling - get specific number of subjects with various characteristics.
• 1st 50 pt's with left TKR
• 1st 50 pt's with right TKR

Front 15

Consecutive Sampling

Back 15

TYPE OF NON PROBABILITY (CONVENIENCE) SAMPLING

Recruiting all subjects that meet study requirements as they become available
• 1st 100 pt's with TKR

Front 16

Cluster Sampling

Back 16

TYPE OF PROBABILITY SAMPLING

For a population too large to make complete sampling list. Initial selection must elicit equal chance of each person in population to be selected.

Front 17

Disproportionate Sampling

Back 17

TYPE OF PROBABILITY SAMPLING

example:
- 100 female PT students have average height 64"
- 100 male PT students have average heigh 68"
75% of PT students are female so the average height of pt students with respect to gender is:
(.75)64 + (.25)68 = 65"

Front 18

Stratified Random Sampling

Back 18

TYPE OF PROBABILITY SAMPLING

use to get proportional representation of males to females

Front 19

Systematic Sampling

Back 19

TYPE OF PROBABILITY SAMPLING

Divide total # of elements in accessible population by the number of elements to be selected. Randomly select a starting point, select each element at the sampling interval.

ex: 100 = pop, need 10 subjects. Randomly select number between 1 & 10. Say we picked 7. Select the 7th element, then select every 10th element from #7 (10 = sampling interval)

Front 20

Simple Random Sampling

Back 20

TYPE OF PROBABILITY SAMPLING

Randomly select subjects using random number generation

Front 21

Probability Sampling

Back 21

Every member of population has an equal chance of being selected for study

Front 22

Target Population

Back 22

entire population with characteristics of interest

Front 23

Accessible Population

Back 23

portion of target population that has a chance of being selected for study participation

Front 24

Sample

Back 24

subgroup of population

Front 25

Generalization

Back 25

what is true for the sample is true for the population

Front 26

Sampling Bias

Back 26

sample characteristics do not equal target population characteristics. Generalization of study conclusion from sample to population may be incorrect.

Front 27

Population

Back 27

group that meets criteria

Front 28

Inclusion Criteria

Back 28

used to identify target population

Front 29

Exclusion Criteria

Back 29

exclude based on ethical reasons, finances, inadequate data, or potential to skew data.

Front 30

PICO

Back 30

PROBLEM - PT Diagnosis
INTERVENTION - treatment
COMPARISON
OUTCOME - (desired result)

• represents the components of a good clinical question

Front 31

Probability Sampling

Back 31

Every member of population has an equal chance of being selected for study

Front 32

Target Population

Back 32

entire population with characteristics of interest

Front 33

Accessible Population

Back 33

portion of target population that has a chance of being selected for study participation

Front 34

Sample

Back 34

subgroup of population

Front 35

Generalization

Back 35

what is true for the sample is true for the population

Front 36

Sampling Bias

Back 36

sample characteristics do not equal target population characteristics. Generalization of study conclusion from sample to population may be incorrect.

Front 37

Population

Back 37

group that meets criteria

Front 38

Inclusion Criteria

Back 38

used to identify target population

Front 39

Exclusion Criteria

Back 39

exclude based on ethical reasons, finances, inadequate data, or potential to skew data.

Front 40

PICO

Back 40

PROBLEM - PT Diagnosis
INTERVENTION - treatment
COMPARISON
OUTCOME - (desired result)

• represents the components of a good clinical question

Front 41

Methodological Studies

Back 41

Use correlative methods to demonstrate reliability and validity of measurement instruments

Front 42

Descriptive Research

Back 42

documentation of observations of one or more groups of people

Front 43

Experimental Research

Back 43

Establishing cause & effect

Front 44

Exploratory/Correlational Studies

Back 44

Establishing relationships between factors (weaker levels of cause & effect)

Front 45

Evidence Based Practice

Back 45

method to examine evidence to anser patient care questions

Front 46

Source of Knowledge

Back 46

methods of collection patient care related information. Ranges from traditional to scientific

Front 47

Reliability

Back 47

Precision, repeatability of a measure, values similar

minimizes random error

Front 48

Correlation

Back 48

Relationship

Front 49

Agreement

Back 49

recoding same actual values

Front 50

Continuous Data: Correlation Coefficient

Back 50

used to quantitatively describe the strength and direction of a relationship between 2 variables

examples: Pearson & Spearman's Correlation Coefficient
& Intraclass (ICC) Correlation Coefficient

Front 51

Categorical data: kappa statistic

Back 51

chance corrected measure of agreement, in addition to looking at proportion of observed agreements, kappa also considers proportion of agreements expected by chance

Front 52

Categorical Data: percent agreement

Back 52

how often raters agree on scores given to individual subjects

Front 53

responsive

Back 53

changes with change, doesn't change with no change

Front 54

predictive

Back 54

inclusive of all possible outcomes (an answer for every possible situation)

Front 55

Measurement Construct (Tools):
Straight forward tools

Back 55

to measure length - ruler, tape measure
to measure speed - distance/time

Front 56

Measurement Construct (Tools):
Less Straight forward tools

Back 56

Ex. Balance - rate basted on test.

Front 57

Face Validity

Back 57

knowledgeable individuals agree that the measure is measuring what is intended. WEAKEST TYPE OF VALIDITY

Front 58

Content Validity

Back 58

In the development stage of measure, knowledgable individuals identify all aspects of the measure that should be included/excluded. Adds expert opinion while developing measurement tool.

Front 59

Criterion related validity

Back 59

predictive ability of a test

Front 60

Criterion related validity:
validity of criterion

Back 60

must be reliable, free from bias & relevant to target test. Use gold standard test to test a new way to test the same thing

Front 61

Criterion related validity:
Concurrent Validity

Back 61

Criterion measure and target test scores taken at same time. Use gold standard test and new test at same time

Front 62

Criterion related validity:
Predictive Validity

Back 62

target test taken to predict the future criterion score/event. Measurement that can predict and outcome. EXAMPLE: measuring TKR pt's knee flexion prior to surgery to predict what their range will be 4 weeks post surgery.

Front 63

Construct Validity

Back 63

Does a developed construct appropriately measure the variable to be measured?

Front 64

Construct Validity:
Factor Analysis

Back 64

identifies different components of a construct

Front 65

Construct Validity:
Hypothesis Testing

Back 65

Construct behaves in measuring subjects as one would hypothesize

Front 66

Construct Validity:
concurrent techniques

Back 66

Known group methods
Convergence/discriminant

Front 67

Minimal Clinically Important Difference

Back 67

a measure of responsiveness. smallest difference in a measure that signifies and important difference in a patient's condition. Smallest difference that patient would perceive as "beneficial". allows determination of better vs not better.

Front 68

Statistical Conclusion Validity

Back 68

Is the statistical relationship between 2 or more variables under consideration determined by the appropriate statistical procedures?

Front 69

Internal Validity

Back 69

Did the measures of the investigation truly establish causality between variables under study?

Front 70

4 Threats to internal validity

Back 70

History - one group has different experience than other
Maturation - subjects change due to time not treatment
Attrition - loss of subjects in non-random fashion
Testing Effects - ability on follow-up test effected by learning how to take the test on initial measurement.

Front 71

Hawthorne Effect

Back 71

when people behave differently when they know they are being studied

Front 72

External Validity

Back 72

appropriateness of generalizing findings to larger population, outside of experimental situation

Front 73

3 Threats to External Validity

Back 73

non-representative samples
setting-specific characteristics that may not generalize
time in history when study was done

Front 74

Measurement Scales: Ratio

Back 74

equal intervals, true zero, meaningful proportions. Example: distance measurement

Front 75

Measurement Scales: Interval

Back 75

Equal intervals, nota true zero, proportions not meaningful.
Examples: time on calendar (days/months are equal intervals, 0 is arbitrary)
Temperature - for C & F 0 is arbitrarily set a freezing point

Front 76

Measurement Scales: Ordinal

Back 76

unequal intervals, numbers indicate rank, hierarchal order.
Example: Olympic medalists (unequal intervals but hierarchal)

Front 77

Measurement Scales: Nominal

Back 77

descriptive categories only, frequency counts per category.
Example: Hair colors in the room

Front 78

Continuous Measures

Back 78

scale that can be defined into ever diminishing increments (ration and interval scales)

Front 79

Categorical Measures

Back 79

measures that have categories, measured by frequency of occurrence

Front 80

Dependent Variable

Back 80

outcome or response variable - what is being measured for comparison or what is being predicted (continuous variable)

Front 81

Independent variable

Back 81

predictor or treatment variable - variable that is being manipulated by the reseacher (categorical variable)

Front 82

Parameter

Back 82

population's characteristic

Front 83

Statistic

Back 83

estimate of populations characteristic based on sample from population

Front 84

Distribution

Back 84

total set of variable scores and the shape of those scores