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Assessment > Ch. 3 - Statistics > Flashcards

Ch. 3 - Statistics Flashcards

1
Q

when two numbers are in the middle of a distribution… how do you find the median?

A

average the middle two

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2
Q

measurement

A

the act of assigning numbers or symbols to characteristics of things (people, events) according to rules

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3
Q

discrete scale

A

there are set categories (like Y/N)

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4
Q

continuous scale

A

categories that theoretically can be divided

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5
Q

measurement always involves ___

A

error

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6
Q

error

A

the collective influence of all of the factors relating to a measurement or test score beyond those the examiner meant to measure

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7
Q

examples of error

A

distractions (mood, hunger, environment), the selection of test items on that exam, inaccuracy of the measurement tool (crappy ruler)

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8
Q

in assessment, we measure characteristics in ___

A

quantifiable terms (though the definition of quantifiable is up for debate). there are 4 scales of measurement to help us define quantifiable

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9
Q

nominal scale

A

numbers are arbitrarily assigned to represent categories. can’t do stats on them. ex 1= yes 2=no

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10
Q

ordinal scale

A

magnitude or rank order is implied. but nothing is implied about how much greater one ranking is than another. has no absolute zero, limited stats. rank: chocolate, pizza, steak, onion rings

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11
Q

interval scale

A

establishes equal distances between measurements, but no absolute zero reference point. can average scores meaningfully (IQ scores - could be ordinal bc maybe not measuring actual intelligence with meaning)

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12
Q

ratio scale

A

has equal intervals AND a meaningful zero point. all math can be performed (weight, hand strength, time to finish a task)

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13
Q

measures of central tendency

A

tell you something about the “center” of a series of scores. mean, median, and mode. give dif info based on skewed vs normal curves

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14
Q

which of the measures of central tendency are used for interval or ratio data that is believed to be normally distributed?

A

mean (no using stats for nominal or ordinal data)

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15
Q

mode is useful…

A

with qualitative data (which words used most often in interviews). is a nominal statistic (can’t be used in further calculations)

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16
Q

median is useful…

A

when there are few scores at the high and low end. can be used for ordinal, interval, and ratio data

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17
Q

normal distribution (AKA Gaussian)

A

bell-shaped, smooth, mathematically defined curve that is higest at center and tapers to approach the X-axis asymptomatically. perfectly symmetrical with no skewness. most traits thought to approximate the normal curve in a pop. mean, median, and mode are the same.

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18
Q

negative skew

A

tail is going negative area! few scores fall at low end (easy test)

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19
Q

positive skew

A

tail is going in the positive area. few scores fall at high end (difficult test)

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20
Q

a distribution with less variability has…

A

a steeper curve. with more variability, the scores are more spread out (flatter curve)

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21
Q

raw scores are often

A

meaningless. must take the raw scores and do something with them to make meaning

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22
Q

(simple) frequency distribution

A

orders a set of scores from high to low and lists the corresponding frequency

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23
Q

grouped frequency distribution

A

(AKA class intervals) - tells you how many people scored within a group of scores (class)

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24
Q

kinds of graphs used to illustrate frequency distributions

A

histogram (bars touch, continuous data), bar graph (bars do not touch, discrete data), frequency polygon (i.e. line graph)

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25
Q

the mean is a ___-level statistic

A

interval. most stable and useful measure of central tendency

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26
Q

variance

A

spread of data around the mean. a way to capture the scale or degree of being spread out.

27
Q

measures of variability include:

A

range, interquartile range (Q3-Q1), semi-interquartile range (Q3-Q1/2), average deviation, standard deviation, variance

28
Q

range is suceptible to

A

outliers

29
Q

standard deviation

A

the average amount of deviation from the mean within a group of scores. AKA the square root of variance. tells you more about the range of scores, or how they differ from the mean. small s for sample SD and cursive o for population SD

30
Q

standard deviation is equal to

A

the square root of the variance

31
Q

a “normal” distribution has the greatest frequency of scores occuring

A

near the mean

32
Q

the greather the standard deviation…

A

the greater the spread of scores

33
Q

n-1 vs n

A

n-1 for sample, n for pop. we use n

34
Q

normal curve - X scores fall between +- 1, 2, 3 SDs of mean

A

68% +-1
95% +-2
99% +-3

35
Q

kurtosis

A

steepness of a distribution

36
Q

platykurtic

A

relatively flat distribution

37
Q

leptokurtic

A

relatively peaked distribution

38
Q

mesokurtic

A

between leptokurtic and platykurtic

39
Q

standard score

A

raw score that has been converted from one scale to another, with the latter scale having some set mean and SD. these have universal meaning

40
Q

examples of standard scores

A

z-score, T-score, stanines

41
Q

z-scores

A

mean of 0 and SD of 1; show how many standard deviation units the score is above or below the mean, z= x-Xbar / s

42
Q

T-scores

A

a z-score transformation, mean = 50; SD= 10; ranges from 5 SD above and below mean (0 to 100) cannot be negative! makes more intuitive sense than z-scores. T= 50

43
Q

stanines

A

“standard nine”, standard scores mean = 5, SD = ~2. Values from 1 to 9

44
Q

when do standard scores retain a direct numerical relationship to the orginal raw score?

A

when the standard score is obtained by a linear (vs nonlinear) transformation.

45
Q

when an original distribution goes through a nonlinear transformation, it is said to be

A

normalized

46
Q

normalizing involves

A

“stretching” a skewed distribution to fit the normal curve. technical worries here. better to fine tune an assessment so that the scores are normally distributed

47
Q

correlation

A

an expression of the degree (strength) and direction of correspondence between two things. little r (Pearson r). perfect positive and perfect negative. “high” values of both are impressive (-.9 and +.9)

48
Q

correlation has a range of values from

A

-1 to 1

49
Q

positive correlation means

A

both variables increase, or decrease, together

50
Q

negative correlation means

A

as one variable increases the other decreases

51
Q

correlation does not mean ___, but it does imply ___

A

causation, but it does mean prediction

52
Q

Pearson R should be used

A

when the relationship between variables is linear and the variables/data are continuous

53
Q

a scatterplot shows

A

the direction and magnitude of the relationship (if any) between two variables (AKA scatter diagram). helps you spot outliers and reveal presence of curvilinearity (can’t use r if curvilinearity)

54
Q

use Spearman Rho

A

vs. Pearson r when you have a small sample size (<30) and especially when you have ordinal or rank order data

55
Q

meta-analysis

A

family of techniques used to statistically combine information across studies to produce single estimates of the data under study. can give more weight to studies that have larger #s of subjects

56
Q

0 correlation means

A

no relationship; not correlated

57
Q

once you have an r, you need to…

A

figure out if the r is significant (depends on sample size, N). Significance at the .05 level is what you’ll usually see reported. only 5% or less chance that the correlation was due to chance.

58
Q

regression

A

the analysis of relationships among variables for the purpose of understanding how one variable predicts another

59
Q

simple regression

A

one independent variable (X) and one dependent variable (Y) - the outcome variable. results in a regression line, or line of best fit, that comes closest to the greatest # of points in a scatterplot

60
Q

regression formula

A

y = a+bx (a = y-intercept; b = slope)

61
Q

standard error of the estimate

A

the error when you predict y from x from a regression line of best fit

62
Q

the higher the correlation between X & Y…

A

the greater the accuracy of the prediction of y from x, and the smaller the standard error of the estimate

63
Q

we are more confident of predictions near the X of an interval

A

middle (mean) - more data points. we are less certain at the tails - fewer data points

64
Q

multiple regression

A

when you have sevaral variables predicting each other, predictors will be weighted, more variables = better prediction, but some are better predictors than others

Assessment (7 decks)

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