Chapter 2-norms & basic stats Flashcards

1
Q

Measurement def

A

Process of obtaining a NUMERICAL description of the degree to which an individual possesses a particular characteristic
-> Assigning numbers to obj

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Outcome of measurement

A

Assign an individual/obj to a category (number)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

A measurement scale determines the kinds of: (2)

A

(1) STATISTICAL procedures that can be applied to the scores produced by the measure
(2) COMPARISONS we can make among individuals using that scale

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Nominal scale def

A

Numbers are assigned to represent labels or categories of data only

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Appropriate Statistics for Nominal scale (2)

A

Frequency, mode

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Ordinal scale def

A

Numbers are assigned to rank-ordered data. The distances between numbers are NOT equal

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Appropriate stats for Ordinal scale (5)

A

Frequency, mode, median, percentile, rank-order correlation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Interval scale def

A

(1) Numbers are assigned to rank-ordered data, and (2) the DISTANCE between numbers is EQUAL. There is NO absolute zero point (i.e., a number indicating the complete absence of what is measured).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Appropriate stats for Interval scale (6)

A

Frequency, mean, mode, median, percentile, Pearson correlation, t test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Ratio scale def

A

(1) Numbers are assigned to rank- ordered data, (2) the distance between numbers is EQUAL, and (3) there IS an absolute zero point.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Appropriate stats for Ratio scale (7)

A

Frequency, mean, mode, median, percentile, Pearson correlation, proportion, t test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Norm def

A

Test scores achieved by a defined group of individuals (i.e., norm sample).

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Norm-based interpretation

A

Compare an individual’s score to the norm group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Types of Norms (3)

A

(1) DEVELOPMENTAL Norms
(2) WITHIN-GROUP Norms
Norms without a Norm Sample
(3) CRITERION-REFERENCED Norms

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Developmental Norms def

A

Typical level of performance in each of the AGE group or grade levels that the test’s target population comprises.
-> Age-equivalent or grade-equivalent scores are assigned based on the MEDIAN RAW SCORE for that chronological age or grade level.
-> Median = TYPICAL score = norm

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Within-Group Norms (3)

A

(1) Percentiles
(2) Z-scores
(3) Transformed standard scores

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Percentiles def

A

Percentage of individuals falling below a test score

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Standard Deviation def

A

A measure of the average distance of scores from the mean.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Transformed Standard Score formula

A

Bz + A
B = desired SD
A = desired Mean

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Percentiles advantages (3)

A

(1) Easy to grasp
(2) Easy to compute
(3) Interpretation doesn’t change with the shape of the distribution

21
Q

Percentiles disadvantages (2)

A

(1) Magnifies differences near mean; minimizes differences at extremes
(2) Some common statistical analyses are NOT possible with percentiles

22
Q

Standard scores advantages (2)

A

(1) Gives a sense of distance from the mean
(2) Can be used in most common statistical analyses

23
Q

Standard score disadvantages (2)

A

(1) Unfamiliar to many non-specialists
(2) Interpretation difficult when distribution not normal

24
Q

Criterion-Referenced Norms def

A

Evaluate performance relative to an absolute criterion or standard rather than performance of other individuals.
-> An absolute vs relative evaluation

24
Q

Within-Group Norms: Criticisms (2)

A

(1) Only meaningful if the standardization (norm) sample is representative
(2) Within-group comparisons encourage competition

25
Q

Requirement for Criterion-Referenced Norms

A

Define content of domain narrowly and specifically.
E.g. Driving skills, 8th grade math curriculum

26
Q

Criterion-Referenced Norms: Issues (3)

A

(1) Can elements of performance be specifically defined?
-> Hard to clearly define what “good” or “bad” performance looks like.
-> Criterion-referenced norms require a clear standard (e.g., scoring 80% on a test to pass), but creating these standards can be challenging because it’s hard to decide what knowledge or skills are essential.
(2) Focus on minimum standards
-> e.g., “Did you pass?”
-> Ignore how much better one person is compared to others.
(3) Absence of relative knowledge
-> You don’t know how someone performs compared to others.

27
Q

Developmental norms cons

A

Often interpreted inappropriately
-> Overgeneralization, misinterpreting median…

28
Q

Developmental & within-groups norms are dependent on _______________

A

the quality of norm sample

29
Q

Criterion-referenced norms have limited ___________

A

applicability

30
Q

Norm-referenced testing convert raw score to ___________ score

A

standardized

31
Q

What is an elevated score?

A

2 z-scores

32
Q

Characteristics of a GOOD norm sample (3)

A

(1) Sufficiently LARGE (avoid sampling error)
(2) Representative of the largest population
(3) Contemporary

33
Q

Properties of scales (3)

A

(1) Magnitude
(2) Equal Intervals
(3) Absolute 0

34
Q

Propriety of Magnitude def

A

A scale has the property of magnitude if we can say that a particular instance of the attribute represents more, less, or equal amounts of the given quantity than does another instance.

35
Q

Propriety of Equal Intervals def

A

A scale has the property of equal intervals if the difference between two points at any place on the scale has the same meaning as the difference between two other points that differ by the same number of scale units.

36
Q

Propriety of Absolute 0

A

An absolute 0 is obtained when nothing of the property being measured exists.

37
Q

Frequency distribution def

A

Displays scores on a variable/measure to reflect how frequently each value was obtained.

38
Q

The greater the percentile, the more ______________

A

la personne est bonne!!!

39
Q

McCall’s T

A

Same as standard scores (Z scores), except that the M=50 and SD=10.

40
Q

Standardization vs normalization

A

McCall’s T, z-scores (…) - Do not change the characteristics of the distributions.
If a distribution of scores is skewed before the transformation is applied, it will also be skewed after the transformation has been used.

41
Q

Transformations _________ but do not _______.

A

standardize; normalize

42
Q

Interquartile range

A

Interval of scores bounded by the 25th and 75th percentiles.
-> bounded by the range of scores that represents the middle 50% of the distribution.

43
Q

Deciles

A

Use points that mark 10%.
-> Thus, the top decile, or D9, is the point below which 90% of the cases fall. The next decile (D8) marks the 80th percentile, and so forth.

44
Q

Stanine system

A

Converts any set of scores into a transformed scale, which ranges from 1 to 9.
M = 5, SD = 2

45
Q

Overselection

A

Selecting a higher percentage from a particular group than would be expected on the basis of the representation of that group in the applicant pool.

46
Q

Overselection is a problem with ______

A

Within-Group Norms

47
Q

Tracking (developmental norms)

A

Tendency to stay at about the same level relative to one’s peers.

48
Q

Big Data

A

Revolution in social science research.
= Data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.