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PSYCHASSESS: Statistics Refresher > Describing Data > Flashcards

Describing Data Flashcards

1
Q

Describing Data

A

Frequency Distributions

Measures of Central Tendency

Measures of Variability

Skewness

Kurtosis

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2
Q

set of test scores arrayed for recording or study.

A

distribution

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3
Q

straightforward, unmodified accounting of performance that is usually numerical.

reflect a simple tally, as in number of items responded to correctly on an achievement test.

A

raw score

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4
Q

the number of times each score occurred might be listed in tabular or graphic form

Table 3–2

A

frequency

distribution,

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5
Q

individual scores have been used and the data have not been grouped.

A

simple frequency distribution

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6
Q

frequency distribution used to summarize data

Table 3–3,

A

grouped frequency distribution

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7
Q

test-score intervals, also called ____ replace the actual test
scores

A

class intervals

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8
Q

diagram or chart composed of lines, points, bars, or other symbols

describe and illustrate data

A

graph

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9
Q

Three kinds of graphs used to illustrate frequency distributions

A

histogram,

bar graph,

frequency polygon

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10
Q

graph with vertical lines drawn at the true limits of each test score (or class interval), forming a series of contiguous rectangles.

test scores along the graph’s horizontal axis (also referred to as the abscissa or X-axis)

frequency of occurrence along the graph’s vertical axis (also referred to as the ordinate or Y-axis).

A

histogram

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11
Q

numbers indicative of frequency on the Y-axis

reference to some categorization (yes/no/maybe, male/female) on the X-axis.

rectangular bars typically are not contiguous.

A

bar

graph

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12
Q

expressed by a continuous line connecting the points where test scores or class intervals (on the X-axis) meet frequencies (on the Y-axis).

A

frequency polygon

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13
Q

Measures of Central Tendency

A

arithmetic mean
median
mode

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14
Q

the statistic that indicates the average or midmost score between the extreme scores in a distribution.

A

the measure of central tendency

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15
Q

most commonly used measure of central tendency

average

takes into account the actual numerical value of every score

A

arithmetic mean

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16
Q

standard statistical shorthand called

A

“summation notation”

summation meaning “the sum of”

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17
Q

the symbol used to signify “sum”

A

Greek uppercase letter sigma, Σ,

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18
Q

X represents

A

a test score

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19
Q

expression Σ X means

A

add all the test scores

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20
Q

denoted by the symbol X (and pronounced “X
bar”)

equal to the sum of the observations (test scores) divided by the number of observations.

the appropriate measure of central tendency for interval or ratio data when the distributions are believed to be approximately normal.

A

arithmetic mean

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21
Q

the formula for the arithmetic mean

A

X = Σ(X/n)

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22
Q

computed from a

frequency distribution. The formula is..

A

X = Σ(fX)

n

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23
Q

Σ( f X) means

A

multiply the frequency of each score by

its corresponding score and then sum

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24
Q

calculation

of the mean from a grouped frequency distribution

A

Table 3–4

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25
Q

middle score in a distribution

ordering
the scores in a list by magnitude, in either ascending or descending order

A

median

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26
Q

If the total number of

scores ordered is an odd number…..

A

median will be the score that is exactly in the middle

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27
Q

When the total number of scores ordered is an even number….

A

median can

be calculated by determining the arithmetic mean of the two middle scores

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28
Q

most frequently occurring score in a distribution of scores

tends not to be a very commonly used measure of central tendency

the modal score is not calculated

A

mode

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29
Q

there are two scores (51 and 66) that occur with the highest frequency (of two).

A

bimodal distribution

30
Q

is not calculated in a true sense, it is a
nominal statistic and cannot legitimately be used in further
calculations

A

mode

31
Q

the statistic that takes into account the order of scores and is itself ordinal in nature.

A

median

32
Q

interval-level statistic is generally the most stable and useful measure of central tendency.

A

mean

33
Q

Measures of Variability

A

range

interquartile and semi-interquartile ranges

average deviation

standard deviation

34
Q

indication of how scores in a distribution are scattered or dispersed

A

Variability

35
Q

Statistics that describe the amount of variation in a distribution

A

measures

of variability

36
Q

provides a quick but gross description of the spread of scores.

Figure 3–3

A

range

37
Q

distribution of test scores (or any data) can be divided into four parts

A

interquartile and semi-interquartile ranges

38
Q

e dividing points between the four quarters in the
distribution

Figure 3–5

A

quartiles

39
Q

quartiles are labeled as…

A

Q1, Q2, and Q3.

40
Q

refers to a specific point

A

quartile

41
Q

refers to an interval

A

quarter

42
Q

Q2 is the same as

A

median

43
Q

Q1 and Q3 are the..

A

quarter-points in a distribution of score

44
Q

measure of variability equal to the difference between Q3
and Q1.

an ordinal statistic

A

interquartile range

45
Q

is equal to the interquartile range divided by 2.

A

semi-interquartile range

46
Q

perfectly

symmetrical distribution

A

Q1 and Q3 will be exactly the same distance from the median.

47
Q

If

these distances are unequal then there is a lack of symmetry

A

skewness

48
Q

AD = ∑∣x∣

n

A

average deviation

49
Q

AD = ∑∣x∣
n

lowercase italic x in the formula signifies

A

score’s deviation from the mean

50
Q

AD = ∑∣x∣
n

value of x obtained by

A

subtracting the mean from the score (X − mean = x)

51
Q

AD = ∑∣x∣
n

bars on
each side of x indicate

A

absolute value of the deviation score (ignoring the positive
or negative sign and treating all deviation scores as positive)

52
Q

a measure of variability equal to the square

root of the average squared deviations about the mean.

A

standard deviation

53
Q

is equal to the arithmetic mean of the squares of the

differences between the scores in a distribution and their mean.

A

variance

54
Q

s2 = ∑x2

n

A

calculate
the variance (s
2) using deviation scores

55
Q

s2 = ∑x2
n

variance is calculated by

A

squaring and summing all the deviation scores and

then dividing by the total number of scores.

56
Q

s2 = ∑X2
n
− X2

Table 3–1

A

calculate the summation of the raw scores squared, divide

by the number of scores, and then subtract the mean squared.

57
Q

(square root with n)
Σ(X − M)2
n

X represents a sample mean

M a population mean

A

formula for the population

standard deviation

58
Q

nature and extent to which
symmetry is absent.

an indication of how the measurements in a distribution are
distributed

A

skewness

59
Q

when relatively few of the scores fall at the

high end of the distribution.

A

positive skew

60
Q

when relatively few of the scores fall at the low end of the distribution.

examination results may indicate that the test was too easy.

Figure 3–3

A

negative

skew

61
Q

Q3 − Q2 will be greater than the distance of Q2 − Q1.

A

positively skewed distribution

62
Q

Q3 − Q2 will be less than the distance of Q2 − Q1

A

negatively skewed distribution,

63
Q

distances from Q1 and Q3 to the median are the same

A

symmetrical

64
Q

refer to the steepness of a distribution in its center

A

kurtosis

65
Q

describe
the peakedness/flatness of three general types of curves

Figure 3–6

A

platy-, lepto-, or meso-

66
Q

Distributions are relatively flat

A

platykurtic

67
Q

Distributions are relatively peaked

A

leptokurtic

68
Q

Distributions are in the

middle

A

mesokurtic

69
Q

Distributions that have high kurtosis are

characterized by

A

high peak and “fatter” tails compared to a

normal distribution.

70
Q

lower kurtosis values indicate

A

distribution with a rounded peak and thinner tails.

71
Q

normal bell-shaped curve would have a

graph A from Figure 3–3

A

kurtosis value of 3

72
Q

normal distribution would have

A

kurtosis of 0

PSYCHASSESS: Statistics Refresher (5 decks)

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