Research Exam 1: Set 5

Front 1

confounding variables

Back 1

other variables which could influence the relationship between the independent and dependent variables and later the results of the study

Front 2

control

Back 2

researcher's ability to manipulate variables and make accurate measurements so as to minimize the influence of any related, extraneous or confounding factors on the relationship bt the variables of interest

Front 3

bias

Back 3

any tendency to influence the results of a study/trial other than by the experimental intervention

Front 4

error

Back 4

any diff bt a measured value of a quantity and its true value

Front 5

Hawthorne Effect

Back 5

observers came to check out efficiency, turns out that efficiency went up tons when the lights were on - next day, no lights, performance down - turns out that the observation itself was what was increasing performance

Front 6

randomization

Back 6

insuring all study subjects have an equal chance of being assigned to any group

Front 7

blinding

Back 7

keeping subjects ignorant of group assignments

Front 8

standardize procedures & processes

Back 8

maintaining consistency throughout entire research protocol

Front 9

minimize error variance

Back 9

standardize measurement process, equipment calibration, use only reliable and valid measurement tools/techniques

Front 10

why is measurement important in a clinical setting?

Back 10

virtually no clinical decisions or actions that are independent of some type of measurement process

Front 11

construct

Back 11

an abstract concept used to represent unobservable behaviors or ideas by incorporating a level or scale of measurement

(i.e. intelligence, strength, pain, mood, depression, etc)

Front 12

categorical data

Back 12

data lack mathematical equivalence, they DO NOT lend themselves to arithmetic operations such as addition and subtraction

Front 13

continuous data

Back 13

data have known equal distances between values, they DO lend themselves to arithmetic operations

Front 14

nominal data

Back 14

objects or people assigned to categories - codes have no quantitative value (i.e. 0 or 1)

(categorical)

Front 15

ordinal data

Back 15

categories rank-ordered in a "greater than: less than relationship" - intervals between ranks may not be consistent and/or may not be known - only represent a position within a distribution (i.e. 1st, 2nd, 3rd)

(categorical)

Front 16

interval data

Back 16

rank-order characteristics, also have known and equal distances/interval between units of measurement - relative difference and equivalence can be determined, can have negative values (i.e. temperature scales)

(continuous)

Front 17

ratio data

Back 17

absolute zero point that has empirical rather than arbitrary meaning - zero represents a total absence of whatever is being measured, no negative values possible (i.e. height, weight)

(continuous)

Front 18

reliability

Back 18

extent to which measurement is consistent and free from error

dependability or predictability of a specific measurement

Front 19

accuracy

Back 19

nearness of a measurement to the actual value of the variable being measured

Front 20

precision

Back 20

closeness of repeated measurements of the same quantity to each other

Front 21

measurement bias

Back 21

when difference between measured value and actual value is consistently inaccurate (i.e. 5 lbs too low)

Front 22

sources of error

Back 22

instrument/equipment
researcher/technician
subject/patient

Front 23

test-retest

Back 23

consistency of repeated measurements

Front 24

intra-rater

Back 24

consistency between different raters

Front 25

intra-subject

Back 25

consistency of a single subject