Vocab Flashcards
1
Q
measurement:
A
process of assigning numerals to variables to represent quantities of characteristics according to certain rules
2
Q
continuous variable:
A
A quantitative variable that can theoretically take on values along a continuum
3
Q
discrete variable:
A
A variable that can only be measured in separate units and that cannot be measured in intervals of less than 1
4
Q
dichotomy (dichotomous variable):
A
A nominal variable having only two categories, such as yes/no and male/female; a binomial variable
5
Q
precision:
A
exactness of measure
6
Q
constructs:
A
concepts that represent nonobservable behaviors or events
7
Q
level of measurement/scales:
A
The precision of a scale based on how a characteristic is measured; nominal, ordinal, interval and ratio levels
8
Q
nominal
A
classificatory scale, qualitative (gender, blood type, diagnosis). These are mutually exclusive in that you cannot belong to more than one group
9
Q
ordinal rank:
A
ordered on the basis of an operationally defined characteristic or property. (manual muscle tests, function, pain)
10
Q
interval numbers
A
have equal intervals but no true zero (calendar years, C or F)
11
Q
ratio
A
numbers represent units with equal intervals, measured from true zero
12
Q
reliability
A
The degree of consistency with which an instrument or rater measures a variable. .5 is poor. .5-.75 moderate. .75+ good.
13
Q
validity:
A
- The degree to which an instrument measures what it is intended to measure.
- The degree to which a research design allows for reasonable interpretations from the data, based on controls (internal validity), appropriate definitions (construct validity), appropriate analysis procedures (statistical conclusion validity), and generalizability (external validity).
14
Q
measurement error:
A
The difference between an observed value for a measurement and the theoretical true score; may be the result of systematic or random effects.
15
Q
systematic error:
A
A form of measurement error, where error is constant across trials.
16
Q
random errors:
A
measurement are due to chance and can affect a subjects score in an unpredictable way from trial to trial
17
Q
regression toward the mean:
A
A statistical phenomenon in which scores on a pretest are likely to move toward the group mean on a posttest because of inherent positive or negative measurement error; also called statistical regression.
18
Q
variance:
A
A measure of variability in a distribution, equal to the square of the standard deviation.
19
Q
reliability coefficient:
A
true score variance/ (true score variance + error variance)=T/ (T+E)
20
Q
correlation:
A
The tendency for variation in one variable to be related to variation in a second variable; those statistical procedures used to assess the degree of covariation between two variables
21
Q
agreement:
A
measure used in conjunction with correlation. The degree to which values from two measurements are the same not just correlated
22
Q
test-retest reliability:
A
The degree to which an instrument is stable, based on repeated administrations of the test to the same individuals over a specified time interval
23
Q
testing effect:
A
The effect that occurs when a test itself is responsible for observed changes in the measured variable.
24
Q
interrater reliability:
A
• The degree to which two or more raters can obtain the same ratings for a given variable
25
alternate forms reliability:
Reliability of two equivalent forms of a measuring instrument
26
internal consistency:
A form of reliability, assessing the degree to which a set of items in an instrument all measure the same trait. Typically measured using Cronbach's alpha.
27
homogeneity:
internal consistency
28
split-half reliability:
A reliability measure of internal consistency based on dividing the items on an instrument into two halves and correlating the results.
validate a test to look which part of the test was done better
29
Cronbach’s alpha:
Reliability index of internal consistency, on a scale of 0.00 to 1.00.
30
item-to-total correlation:
Correlation of individual items in a scale with the total scale score; an indication of internal consistency.
31
generalizability theory- generalizability:
1. The quality of research that justifies inference of outcomes to groups or situations other than those directly involved in the investigation.
2. The concept of reliability theory in which measurement error is viewed as multidimensional and must be interpreted under specific measurement conditions.
32
facets:
In generalizability theory, specific conditions under which reliability of a measurement can be generalized.
33
minimal detectable difference (MDD):
That amount of change in a variable that must be achieved to reflect a true difference; the smallest amount of change that passes the threshold of error. Also called minimal detectable change (MDC)
34
population-specific reliability:
reliability that is established on subjects from one population cannot automatically be attributed to other populations
35
measurement validity:
concerns the extent to which an instrument measures what it is intended to measure
36
face validity:
The assumption of validity of a measuring instrument based on its appearance as a reasonable measure of a given variable.
lowest level (there is NO HIGH LEVEL), you look at it and it “looks ok”. Ex) standing on one leg would predict good gait.
37
content validity:
A type of measurement validity; the degree to which the items in an instrument adequately reflect the content domain being measured.
what we are saying represents the right thing
38
criterion-related validity:
• A type of measurement validity; the degree to which the outcomes of one test correlate with outcomes on a criterion test; can be assessed as concurrent validity or predictive validity.
39
concurrent validity:
A type of measurement validity; a form of criterion-related validity; the degree to which the outcomes of one test correlate with outcomes on a criterion test, when both tests are given at relatively the same time.
40
predictive validity:
A form of measurement validity in which an instrument is used to predict some future performance.
this test will predict this
41
construct validity:
1. A type of measurement validity; the degree to which a theoretical construct is measured by an instrument.
2. Design validity related to operational definitions of independent and dependent variables.
42
content validity:
• A type of measurement validity; the degree to which the items in an instrument adequately reflect the content domain being measured.
43
gold standard:
A measurement that defines the true value of a variable. In criterion-related validity, an instrument that is considered a valid measure and that can be used as the standard for assessing validity of other instruments. In diagnostic testing, a procedure that accurately identifies the true disease condition (negative or positive) of the subject.
44
reference standard:
A value used as a standard against which to judge a criterion; may or may not be a gold standard. Used to judge criterion-related validity or diagnostic accuracy.
45
concurrent validity:
A type of measurement validity; a form of criterion-related validity; the degree to which the outcomes of one test correlate with outcomes on a criterion test, when both tests are given at relatively the same time.
46
topic:
general subject
47
research problem:
once you have the topic of the study more research is done to see what we know, what we don’t know etc and then the research problem is identified
48
research question:
once you have the topic and then the research problem the research question can be developed starting out broad and working more narrow. This can be addressed in a single study
49
target population:
aka reference population; the larger population to which results of a study will be generalized
50
research rationale:
will support the research question, guide decisions in designing the study and provide a basis for interpreting the results. Presents a logical argument that shows how and why the question was developed. Includes references to previous research as well as logical assumptions that can be made from current theory
51
theoretical framework:
explains the constructs and mechanisms behind the question and helps us understand why the question makes sense
52
variables:
are building blocks of the research question and they are property that can differentiate members of a group or set
53
factor:
1. A variable.
| 2. A set of interrelated variables in a factor analysis.
54
independent variable:
• The variable that is presumed to cause, explain or influence a dependent variable; a variable that is manipulated or controlled by the researcher, who sets its “values” or levels.
55
dependent variable:
A response variable that is assumed to depend on or be caused by another (independent) variable.
56
levels:
in comparative studies, independent variables are given values or levels representing group or conditions that will be compared
57
operational definition:
Definition of a variable based on how it will be used in a particular study; how a dependent variable will be measured, how an independent variable will be manipulated.
58
Hypotheses:
plural hypothesis
59
Specific aim:
guiding questions that help organize data and discuss findings in a meaningful way
60
Purpose:
aim/objective
61
research objectives:
specification of the ultimate reason for carrying out research in the first place. They help in developing a specific list of information needs. Only when the researcher knows the problem that management wants to solve can the research project be designed to provide the pertinent information.
62
guiding questions:
is the fundamental query that directs the search for understanding. Everything in the curriculum is studied for the purpose of answering it." Guiding questions help provide focus and coherence for units of study
63
hypothesis:
A declarative statement that predicts the relationship between the independent and dependent variables, specifying the population that will be studied
64
research hypothesis:
A statement of the researcher’s expectations about the relationship between variables under study.
positive to the null, not just the positive
65
deductive hypothesis:
are based on a theoretical premise, allowing the clinician to predict what outcomes would be expected under a given set of conditions
66
inductive hypothesis:
based on trends, regularities, patterns or relationships that are observed in clinical practice
67
null hypothesis:
a statement of no difference or no relationship between variables; the statistical hypothesis
68
nondirectional hypothesis:
A research hypothesis (or alternative hypothesis) that does not indicate the expected direction of the relationship between variables.
69
directional hypothesis:
A research hypothesis (or alternative hypothesis) that predicts the direction of a relationship between two variables.
often research hypothesis
70
simple hypothesis:
one independent variable and one dependent variable
71
complex hypothesis:
contains more than one independent or dependent variable
72
review of literature:
includes two phases, preliminary and full and provides a detailed and complete understanding of the relevant background to assist in formulating the research question
73
assumptions:
concepts or principles that are assumed to be true, based on documented evidence of accepted theoretical premises
74
primary source:
Reference source that represents the original document by the original author.
75
secondary source:
Reference source that represents a review or report of another’s work.
76
population:
the larger group to which research results are generalized
77
sample:
a researcher chooses a subgroup of the population. Serves as the reference group for estimating characteristics of or drawing conclusions about the population
78
sampling bias:
Bias that occurs when individuals who are selected for a sample overrepresent or underrepresent the underlying population characteristics.
not necessarily what a researcher does, can both be conscious/inconscious and intentional/unintentional
79
target population:
The larger population to which results of a study will be generalized.
80
accessible population:
The actual population of subjects available to be chosen for a study. This group is usually a nonrandom subset of the target population.
81
inclusion criteria:
primary traits of the target and accessible populations that will qualify someone as a subject
82
exclusion criteria:
those factors that would preclude someone from being a subject
83
random selection/ random sampling:
• Probability method of selecting subjects for a sample, where every subject in the population has an equal chance of being chosen.
84
probability sample:
A sample chosen using randomized methods.
85
statistic:
A measured characteristic of a sample
86
parameter:
A measured characteristic of a population.
87
sampling error:
• The difference between an observed statistic from a sample and the population parameter.
88
simple random sampling:
sampling without replacement, as a unit is selected it has no further chance of being selected
89
systematic sample:
A probability sampling method where subjects are chosen from lists of population members using specified intervals, such as every 10th person.
90
sampling interval:
interval between selected elements
91
stratified random sampling:
involves identifying relevant population characteristics, and partitioning members of a population into homogenous, nonoverlapping subsets, or strata based on those characteristics
92
strata:
are homogeneous nonoverlapping subsets
93
proportional stratified sample:
separate the population into the four classes then drawing random or systematic samples from each class in the proportion that exists in the population
94
disproportional sampling:
we select random samples of adequate size from each category
95
cluster sampling:
A form of probability sampling in which large subgroups (clusters) are randomly selected first, and then smaller units from these clusters are successively chosen; also called multistage sampling. Also called multistage
96
Area probability sample:
A form of cluster sampling in which geographic areas serve as the units of analysis.
97
random digit sampling:
involves the random selection of phone numbers based on multistage sampling of area codes and telephone exchanges.
98
nonprobability samples:
created when samples are chosen on some basis other than random selection
99
convenience sampling:
A nonprobability sampling procedure, involving selection of the most available subjects for a study.
100
accidental sample:
• Accidental sampling is a method of sampling where by the sampler picks the sample based on the fact that the elements that he/she picks are conveniently close at the moment.
101
consecutive sampling:
A form of nonprobability sampling, where subjects are recruited as they become available.
102
quota sampling:
Nonprobability sampling method in which stratification is used to obtain representative proportions of specific subgroups
103
purposive sample:
A nonprobability sample in which subjects are specifically selected by the researcher on the basis of subjective judgment that they will be the most representative.
104
snowball sampling:
A nonprobability sampling method in which subjects are successively recruited by referrals from other subjects.
105
power:
The ability of a statistical test to find a significant difference that really does exist; the probability that a test will lead to rejection of the null hypothesis.
106
reference population
aka target population; overall group of people to which the researcher intends to generalize findings. Ex) a study of motor skills defined as all children with learning disabilities in the US today.
107
self-selection
For example when researchers look for subjects in certain areas like hospitals, street corners, magazines. The major limitation to this method is the POTENTIAL OF SELF-SELECTION.
