PR -terms #3 Flashcards

1
Q

The degree to which scores from the first iteration of a test or evaluation are correlated to subsequent
iterations of the same test or evaluation.

A

Test–retest reliability

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2
Q

The degree to which two tests in a given subject area, given to one group of test takers, are correlated

A

Equivalent forms reliability

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3
Q

The degree to which scores or evaluations from two or more people are correlated. A large reliability coefficient suggests a higher

A

Interrater reliability

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4
Q

The degree to which scores from one-half of a test or evaluation are correlated to scores or evaluations from the second half.

A

Split-half reliability

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5
Q

are values such as IQ and personality that we know exist but are not tangible.

A

constructs

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6
Q

we’re investigating the degree to which an instrument measures what it claims to measure.

A

Construct Validity

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7
Q

investigates whether the construct you’re measuring is highly correlated with a similar construct.

A

Convergent validity

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8
Q

demonstrates the construct validity of the instrument we are evaluating.

A

discriminant validity,

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9
Q

asks the question “Is the entire content area I want to measure covered?”

A

Content Validity

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10
Q

It would be necessary to evaluate each question to make sure it is relevant to the subject matter taught.

A

Item validity

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11
Q

is concerned with how well the results of a survey or test you develop correlate with a previously validated instrument

A

Criterion validity

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12
Q

as it is sometimes called, categorical or discrete data, is simply a value that we count. (For example, if we are interested in determining the number of males and females we work with, the data value “gender” is nominal in nature.)

A

Nominal data

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13
Q

it is sometimes called, rank
data. (An example we are all familiar with is the use of grade point averages to assign positions within a class)

A

Ordinal Data

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14
Q

It is the first of two types of data that are called quantitative or continuous data. By this, we mean that a data value can hypothetically fall anywhere on a number line within the range of a given data set.

A

Interval Data (interval-level data include temperature, aptitude scores, and intelligence quotients.)

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15
Q

differs from interval data because it does have an absolute zero point and the various points on the scale can be used to make comparisons between one another.

A

Ratio data

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16
Q

it investigates and reports on the current
status of a population based on numeric data you’ve collected

A

Survey research

17
Q

This a numeric and graphical statistical tools that help us describe the data Ex. the mean or data set or a pie chart

A

Descriptive statistics

18
Q

More advanced research designs call for tools such as t tests, ANOVAs, and regression analysis that allow us to make decisions about the data we have collected and the hypotheses we’ve stated.

A

inferential statistics,

19
Q

If the computed p value is greater than or equal to alpha (accept or reject)

A

accept null hypothesis, fail to support research hypothesis

20
Q

If p is less than alpha (accept or reject)

A

reject null hypothesis and support research hypothesis