Ch2 - Stats for Testing Flashcards

1
Q

Measurement

A

the use of certain devices/rules for assigning numbers to objects/events

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2
Q

Variables + types

A

anything that varies
• Visible/invisible
• Discrete (errors in counting)/continuous (measurement errors)
• Dichotomous / Polytomous (discrete variables assuming + than 2 values)

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3
Q

Nominal scales type of data

A

Categorical data: data related to variables such as gender, color, that derive from assigning people, objects or events in categories/classes
• The only property of the numbers given to define the categories is identity
• We can only count the frequencies within each category

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4
Q

Ordinal scales properties

A

Added property of rank order: the elements in a set can be lined up in a series arranged on the basis of a single variable (ex: birth order)
• Rank orders carry no information regarding the distance between positions

In psych, rank-ordered tests are reported as percentile rank scores (PR)
• Ordinal numbers from 1 to 100, rank indicates the % of individuals in a group who fall at or below a given level of performance
○ Ex: 70 - level of performance that equals or exceeds 70% of the group

Ordinal data can be manipulated like nominal data, but also with Spearman’s rho correlation coefficient

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5
Q

Interval scales properties

A

Difference between any 2 consecutive numbers is the same that the numbers represent
• Ex: if 2 days are 12 days apart, they are exactly 3 times as far apart as 2 days that are 4 days apart
○ *some months are longer than others so it does not apply to months
• There is no agreed upon starting point for the calendar, so no absolute 0 - can’t be interpreted as ratios

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6
Q

Ratio scales properties

A

Numbers achieve additivity: they can be added, substracted, multiplied, divided and the result will be a meaningful ratio
• Have a true/absolute 0 - represents NONE of what is measured
• Ex: an object of 16pounds is 2x as heavy as an 8pound object, and 0pounds indicates weightlessness

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7
Q

Problem with ratio IQs

A

Ratio IQs were obtained by:
• (Mental Age (result on S-B test) / Child’s chronological age) x 100 = ratio IQ
• Idea: average children would have an IQ of 100 (since their mental age would equal their actual age)
• BUT: did not work with adolescents/adults bc their development is less uniform / less intense
○ Mental age = ordinal-level measurement
○ Chronological age = ratio scale
○ Dividing the 2 cant lead to a meaningful number

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8
Q

2 types of stats

A
  • Descriptive: maths dedicated to organize / summarize / etc data
    • Inferential: used to estimate pop values based on sample values
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9
Q

Statistics def

A

relate to sample data

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10
Q

Parameter def

A

relate to population data
○ Mathematically exact numbers (or constants) that are not usually attainable unless a population is so fixed and circumscribed that all of its members can be accounted for

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11
Q

Frequency distributions

A

frequency with which each scores occurs in a distribution
• Can also include percentile rank scores (Cumulative Percent Column)
• Grouped frequency distributions: when the ranges are too large

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12
Q

Graphs (+ best types for discrete vs continuous data)

A

Frequency tables can be made into graphs for even easier reading
• Discrete/categorical data: pie charts or bar graphs
• Continuous/metric data: histograms/frequency polygons

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13
Q

• Measures of central tendency

A

○ Mode: + frequent value in a distribution (bimodal/multimodal = more than one variable with the same value)
○ Median: value that divides a distribution that has been arranged in order of magnitude into 2 halves
○ Mean: arithmetic average (u for pop and M for sample)

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14
Q

Range

A

distance between the 2 extreme points

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15
Q

Semi-interquartile range

A

1/2 of the interquartile range (IQR) - the middle 50% of a distribution

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16
Q

IQR

A

distance between the points that demarcate the tops of the first and third quarters of a distribution

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17
Q

Variance

A

sum of squared differences between each values and the mean of that distribution, divided by n (AKA Average sum of squares SS) - represents average variability in distribution

18
Q

Sum of squares

A

represents total amount of variability in a score distribution

19
Q

Standard deviation

A

square root of the variance - represents the average variability in a set of scores

20
Q

Properties of the Normal Curve Model

A
  • Its limits extend to +/- infinity
    • Is unimodal
    • Mean = median = mode = center
21
Q

Standard Normal Distribution

A

Normal Curve with a mean of 0 and a standard deviation of 1

22
Q

Uses of the Normal Curve

desctiptive/inferential

A
• Descriptive Uses
		○ Normalizing scores: transforming them so that they have the same meaning, in terms of their position, as if they were coming from a normal distribution
	• Inferential Uses
		○ Estimating population parameters
		○ Testing hypotheses about differences
23
Q

Sampling distribution

A

hypothetical distributions of values made on the assumption that an infinite number of samples of a given size could be drawn from a population - if this were done, the resulting distributions of statistics (sampling distributions) would be normal

24
Q

Standard Error

A

the standard deviation of the sampling distribution

25
Q

Z distribution

A

the standard normal distribution

○ Smaller samples: Student’s t-distribution

26
Q

Standard error of the mean

A

(SEm) -> s / √n
• S = standard deviation of the sample
• N = number of cases in the sample
Applying this to the mean 1x gives us a 68% confidence interval around the mean
To have larger confidence we need to apply it 2x (95% CI)

We can establish ranges within which the pop parameters are likely to be found
Assuming that
• The sample mean is the best estimate of the pop mean
• The st error of the mean = the st dev of the hypothetical sampling distribution of means, assumed to be normal

27
Q

Nonnormal distributions

A

Proportions under the curve no longer apply
• May all have the same frequencies
• May have +1 mode

28
Q

Kurtosis

A

• Kurtosis: stems from Greek for “convexity” - flatness of peakedness of a distribution
○ Platykurtic: more dispersion, more extended tails
○ Leptokurtic: least dispersion, not much extended tails
○ Mesokurtic: normal distribution

29
Q

Skewness

A

lack of symmetry

	○ Normal distribution - Sk  = 0
	○ Negatively skewed: Sk <0
            ○ Positively skewed: Sk >0
30
Q

Univariate vs bivariate stats

A

Univariate statistics: measures of a single variable
Bivariate or multivariate: at least 2 sets of measurements on the same groups of people or matched pairs for 2 sets of individuals

31
Q

Coefficients of determination (in correlations)

A

• Coefficients of determination: tell us how much the variance in Y can be explained by the variance in X (obtained by squaring the correlation coefficient)

32
Q

Regression towards the mean

A

extreme scores on one variable are associated with scores closer in the mean in another

33
Q

Regression line

A

slope represents the strength/magnitude of the relationship between 2 variables
○ Greater slope = greater relationship between the variables
○ Allowed us to predict a variable of Y based on a variable of X known to be related

34
Q

Conditions for using Pearson’s r

A
  • The pairs of observations are independent of one another
    • The variables to be correlated are continuous and measured on interval or ratio scales
    • The relationship between the variables is linear, that is, it approximates a straight-line pattern
35
Q

Heteroscedasticity

A

the dispersion in the scatterplot is not uniform throughout the range of values

36
Q

Homoscedasticity

A

uniform dispersion though the range

37
Q

Spearman’s rho (rs)

A

correlation for ordinal variables

38
Q

Eta (n)

A

correlation for curvilinear relationships

39
Q

Point biserial correlation (rpb)

A

when one variable is dichotomous

Dichotomous variables: often in true/false answer tests

40
Q

Phi or fourfold coefficient

A

when both variables are dichotomous

41
Q

Multiple correlation coefficient (R)

A

when a single dependent v is correlated with 2+ predictors