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Psychometrics > Lecture 2 > Flashcards

Lecture 2 Flashcards

1
Q

Inferential vs. Descriptive stats

A
  • Inferential: Inferences between two different groups
    • Understanding the impression/noise between groups
  • Descriptive: Just about this particular group
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2
Q

What can cause noise?

(and how can we reduce it)

A
  1. Measurement error
  2. Population used
  3. Low sample size
  • Reduced by increasing N, and using random assignment.
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3
Q

4 Types of Scores/Scales

(and examples)

A
  1. Nominal: A name, no value.
    1. Group A, Group B
    2. Can create frequency distributions
  2. Ordinal: Has magnitude but no equal intervals or absolute 0
    1. Ranking from highest to lowest (tallest to shortest rank)
    2. Likert scale is ordinal
    3. Doesn’t say anything about distance between them
    4. Can be manipulated using arithmetic
  3. Interval: Equal intervals but no absolute 0
    1. Most psychological tests are interval
    2. ex. Celsius and Farenheight
    3. Can apply any arithmetic operation to the differences between scores
  4. Ratio: Has a true 0
    1. Calvin
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4
Q

Define:

  1. Inferences
  2. Measurement
  3. Covariance
  4. Dichotomous (Artificial/True) variables
A
  1. Inferences: Logical deductions about events that cannot be observed directly.
  3. Covariance: How two measures covary or vary together
  4. Dichotomous (artificial): made up variables, (true): actual objective differences in variables (e.x. right/wrong)
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5
Q

3 Properties of a Scale

A
  1. Magnitude: Attribute that must be able to be more or less
    1. Team 1 and 2 do not have a magnitude.
  2. Equal Intervals: Difference between two points
    1. Most tests do not have equal intervals (e.x.IQ)
    2. When it does have equal intervals, it can be described with linear equations
  3. Absolute 0
    1. Difficult/impossible to achieve in psychology
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6
Q

Percentile Rank

A
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7
Q

Variance

And Standard Deviation

A
  • Standard Deviation = sqrt of Variance
  • Measure variation is similar to finding average deviation around the mean
  • Variance is the avg squared deviation around the mean
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8
Q

Standard Deviation

A
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9
Q

Sample Standard Deviation

A
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10
Q

Z-Score

A
  1. Find th emean of the raw scores
  2. Find the standard deviation
  3. Change the raw scroes to z-scores
    1. Z-scores can now be converted into any other metric
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11
Q

Normal Distribution Graph

(and conversions)

A
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12
Q

McCall’s T

A
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13
Q

Quartiles and Deciles

A
  1. 1st Quartile = 25%
  2. 2nd Quartile = 50% median
  3. 3rd Quartile = 75%
  • Deciles = 10% increments
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14
Q

Norm-referenced test (and problems) vs. Criterion-Referenced test

(and Age-related norms)

A
  • The performances by defined groups on particular tests
  • Compare a score to some other distrubtion (standardized)
  • Problem: They changed over time and you cant test everyone. Determining what the right group of people is is also difficult
  • Age-related: used in IQ tests (compare mental age to actual age)
  • Criterion: describes the specific types of skills/task/knowledge that a test taker can demonstrate (e.x. math skills)
    • Results of test would not be used to make comparison between others.
    • Emphasizes the diagnostic use of tests to identify problems
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15
Q

Box: Within-Group Norming Controvery

A
  • Different racial and ethnic groups do not have the same average level of perfromance on tests
  • Overselection: selecting a higher percentage from a particular group than would be expected.
    • Separating norms for different ethnic groups elminates the problem but created new practical ones (and it became illegal to do this)
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16
Q

Tracking

A
  • The tendency to stay at about the same level relative to one’s peers.
  • E.x. 3rd percentil height infant tend to be around the 3rd percentile later in life
  • Worked well in medicine, but controversial in education
17
Q

Box: Within High-School Norms for University Admission

A
  • Admissions did not reflect demographic characteristics of the state
  • Developed a program which guarantees eligibility for top 4% of high school graduates and not on SAT test
    • Latino acceptance rates dropped from 68-45 and African from 58-35%
18
Q

Box: No Child Left Behind and (STAR) system in California

A
  • Congress passed legistlation to require greater accountability for school performance (made info about school distracts public.
  • Each child was required to test proficiency in reading and math in grade 3-8.
  • Problems: Tests effect school funding, passing is defined by arbitrary cut point and caused teachers to “teach the test” not the concept.

STAR system

  • Evaluates performance for programs like no child left behind
  • Not many 3rd graders are advanced, but many more 4th graders are advanced
    • Explanation: Definition of advanced is arbitrary. A test too hard for 3rd graders may be too easy for 4th graders.
19
Q

Correlation Coefficient

(Pearson Product Moment Correlation)

A
  • Correlation is regression with the scores normalized around -1 and 1 with no intercept
20
Q

Regression

(What does it try to do and why?)

A
  • Try to predict one variable from another
  • Why? Because x may be easier to calculate than y
  • Found by using principle of least squares
21
Q

Correlation Types

A
22
Q

Principle of Least Squares

A
  • How close is the value to what you predict?
23
Q

Sum of Cross Products

(Don’t need to know equation)

A
24
Q

Intercept of Regression

A
25
Q

Types of Correlation Coefficients

A
  1. Pearson’s
  2. Bi-serial R
  3. Point biserial R
  4. Tetrochoric R
  5. Phi
  • Use depends on if data is continuous, dichotomous (artifical/true)
26
Q

Residuals

A
  • Residuals: How wrong you are. Residual = 0 is perfect correlation
27
Q

Standard Error of Estimate

A
28
Q

Coefficient of Determination and Alienation

A
  • Variability around a line
  • What percentage of variation is being accounted for.
  • If you didn’t have x and wanted to predict y, you should always predict the mean
  • You can account for variance if you have x, which is r2
  • Coefficient of alienation = to what extent are 2 variables not associated with each other (inverse of r2)
29
Q

Multivariate Models:

Linear Combination

A
  • Examines more than one variable influencing a result
30
Q

Factor Analysis

(And steps)

A
  • Know how to read a factor anlysis table and understand it
  • Trying to find common factors among a large amount of variables or measurements
  • Can make as many factors as there are items, but scree plot determines how many factors you should using point infleciton
  • Can then get r2 for any of these factors to explain what they account for
  1. Find (orthogonal) lines through a cloud of data that explains the most variance
  2. Each line defined in a linear combination
  3. For each item see how highly it correlates with that linear combination
  4. Define the factors
31
Q
A

Psychometrics (2 decks)

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