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Psych 440 Midterm 1

Terms in this set (97)

Psychometrics
"measuring the mind"
-no direct access to mental activity
-psyche: greek for mind
-metric: latin for measurement
2 types of measurement
scaling & classification
Scaling
-represent quantity of an attribute numerically
ex: height, weight, age
Classification
define when objects fall into the same or different categories with regards to an attribute
ex: sex, college majors,
Examples of Psychological Scales
Psychological scales are often measures of individual differences
-Interests
-Personality
-The Graduate Record Examination
-Risk Taking Behavior
Characteristics of an Effective Test
-reliability
-validity
-adequate norms
Reliability
Does the test produce consistent
measurement results?
Validity
Does the test measure effectively what it purports to measure?
Adequate norms
Was the test developed using samples similar to the people taking the test?
Advantages of Standardized Measurements
Objectivity
Quantification Communication
Economy
Scientific
Generalizability
Testing
the process of measuring variables by means of devices or procedures designed to obtain a sample of behavior
Assessment
the process of gathering and integrating data for the purpose of making an evaluation
Techniques of Psychological Assessment
Tests
Interviews
Case History Data
Behavioral Observation
Role-Playing
Computer-Based Instruments
Other techniques
Test Developers
Psychologists required to adhere to
ethical standards (APA, AERA)
Test Users
Counselors, other therapists, teachers, human resources, researchers
Assessment Assumptions
1. Psychological States or Traits exist, and can be quantified and measured
2. Different approaches to measuring aspects of the same thing can be useful
3. Various sources of error are part of the assessment process
4. Test-related behavior can predict behavior in other settings
5. Present-Day behaviors can predict future behaviors
Patrick's Key Points
1. All psychological tests involve collecting a sample of behavior
-Often used to predict behaviors that are very different from tested behaviors
2. Tests are evaluated on the basis of reliability, validity, and adequate norms
3. Testing is just one part of Assessment
Origins of Testing: Psychology Precursors
Chinese civil service exams initiated in the Chang Dynasty over 3000 years ago
History suggests that scientific methods can be manipulated to
achieve desired results
Origins of Testing: Early Psychology
-1859 Darwin's Origin of the Species raised issue
-Wilhelm Wundt was a German medical doctor who studied how individuals were similar instead of different
Wundt
studied how individuals were similar instead of different
Sir Frances Galton
Developed first correlation coefficient
Alfred Binet
Developed first intelligence test in 1905 with Theodore Simon
James McKeen Cattell
First American to systematically study assessment of individual differences
Culture
History suggests cultural bias in testing can have an adverse impact:
-Immigration restrictions -Forced sterilization
Culture and Testing
-Many early tests had NO minority individuals in standardization samples
-Translation problems: no corresponding object/word, changes in meaning
Federal Testing Legislation
National Defense Education Act (1958) provided money for aptitude testing in attempt to identify gifted children
-Increased use of tests led to concerns about value and effect of psychological testing on students
Professional Standards
APA 1895
APA 1954
Collab of APA and others (AERA)
Measurement
is the process of assigning numbers or symbols to a characteristic or attribute according to a set of rules
Variable
-Characteristics or attributes of objects (people, places, things, animals, etc.) in a population that are not constant
Scale
A set of numbers whose properties model empirical properties of the variables to which the numbers are assigned
Discrete scale
categorical labels or integers, no meaningful middle grounds between categories
Continuous scale
numbers do not represent categories, middle ground between units possible
Descriptive Statistics
Procedures for organizing, summarizing, and describing quantitative information
-horsepower
Inferential Statistics
Methods for making inferences about a population of objects based on information from a sample from that population
-correlation and regression
Scales of Measurement
Scales, or levels, of measurement help determine what statistical analyses are appropriate
Nominal Scale
Ordering is not important, only the label attached to designate a mutually exclusive and exhaustive category
Ordinal Scale
Individuals or things are ranked or ordered on the basis of some criteria
Interval Scale
Numbering includes order, but intervals between each successive level represents equal differences
-no absolute zero
Ratio Scale
Includes ordering, equal intervals AND an absolute zero
Sources of error variance
-test construction
-test administration
-test scoring and interpretation
Error
Deviation for some measurement from the true standing of an individual on some characteristic
Measures of Central Tendency
-Mode (most frequently observed)
-Mean (average score) -Median (50th percentile score)
Nominal scale values
1= good, 2= bad, 3=ugly
Central Tendency
measures are used to describe the typical response seen in a sample of observations
Variability
measures are used to describe how much fluctuation in scores there are in a sample of observations
3 types of measures of variability
range
deviation scores
variance/standard deviation
Range
the difference between the highest and lowest scores
-sensitive to outliers
Deviation Scores
Measure of how far the raw score is from the mean of its distribution (X - u)
Variance and Standard Deviation
-Reflects the variability of scores about the mean of the group
Variance
the average of the sum of the squared deviations of each score from the mean
Standard deviation
the square root of the variance
-Is expressed in the same units of measurement as the original scores
The normal distribution
-asymmetrical
-highest at the center
-asymptotic
-mean=mode=median
Why is the normal distribution so important to psychometrics?
-Many psychological and educational variables are distributed approx. normally
-Allows for the use of many types of inferential statistics
Quartiles
Dividing points between the four quarters of a distribution of test scores
Skewness
caused by outliers
Kurtosis
Describe the steepness of a distribution in its center
-Platykurtic= flat
-Leptokurtic= peaked
-Mesokurtic: somewhere in between
Standard scores
-A raw score that has been converted from one scale to a new (standardized) scale with a prescribed mean and SD
-Typically expressed in terms of number of standard deviations from the mean
Why use standard scores?
-more easily interpretable than raw scores
-We can tell where a score falls in relation to other scores using a standardized scale
-Allow for easier comparisons of both similar and dissimilar scores
Z-Scores
A standard score where the mean of the scores is set at zero (0) and standard deviations are set at intervals of one (1)
Z- Score advantages
-Indicates each person's standing as compared to the group mean
-Can easily be converted to percentiles
Z- Score disadvantages
-Negative z values can be difficult to work with and explain
-Dealing with fractional z values can be a hassle
Percentiles
A raw score that has been converted into the percentage of a distribution that falls below that particular raw score
Z-Scores and Percentile Ranks
Z-scores can be used to calculate percentiles when raw scores have a normal distribution
Interpreting percentiles
A percentile difference of 10 near the middle of the group often represents a smaller difference in performance than a difference of 10 near the tails
T-scores
T-scores represent one transformation of z which overcomes the disadvantage of working with negative scores
Z-Score table can be used to
to translate observed scores into standardized scores
Standardized scores allow for the more
effective interpretation of test results
Test interpretation is often dependent on
assumption of normality - but not always
Correlation
A statistical technique which allows us to make inferences about how two (or more) variables relate (co-relate) to each other (linearly)
Positive relations
Strong relation (r=.7 or higher)
Moderate/weak relation (r=.4 or lower)
Negative relations
Strong negative (r=-.7)
Moderate/weak negative relation (r=-.4)
Coefficient of determination
Accurate interpretation of correlation coefficients requires another statistic, the coefficient of determination
-Calculated by squaring the correlation coefficient (r^2)
Spearman's Rho (P or p)
-Used if sample sizes are small OR
-If ordinal scale data is used
Prediction
-Correlation is descriptive
-Regression is predictive
-Simple linear regression is used when one variable to predict another
-Multiple regression is used when multiple predictors are used
-Logistic regression is used when the variable bring predicted is dichotomous (gender)
Issues with prediction
-How do we deal with the fact that the predictors (X) and the variable to be predicted (Y) are often on different scales of measurement?
-Prediction technique must take into account both the scales of measurement and the correlation between the two variables
-Linear regression does just that
Simple linear regression
-Describes the relationship between one Independent Variable (X) and one Dependent Variable (Y)

-Regression line is the straight line which comes closest to the greatest number of points on the scatterplot of X and Y
Error and prediction
-Standard error of the estimate (SE)
-Indicates magnitude of errors in estimation
-Higher correlations produce smaller SE
-Lower correlations produce larger SE
Multiple regression
-Can be used when more than one predictor variable is available
-Multiple regression takes into account the correlation between each of the predictor scores and what is being predicted
Norm-referenced evaluation
-A way of interpreting test scores by comparing an individual's results to the scores of a group of test takers
-Interpretation is relative
-Alternative is criterion-referenced evaluation
Test interpretations
Norm-referenced & Criterion-referenced
Norm-referenced
interpretation is based on an individual's relative standing in some known group
Criterion-referenced
interpretation is based on measuring an individual's skill level in relation to a clearly specified standard
Normative sample
-A group of people whose performance on a particular test is analyzed for reference in evaluating the performance of individual test-takers.
-Sample must be representative or typical of the intended population of interest
Inadequate norms makes it difficult to make proper interpretations
Types of samples
o Stratified
o Random
o Purposive
o Convenience
Stratified samples
-Sampling individuals from subgroups in the population in the same proportion as the population they are part of
-Best when population includes subgroups that differ
Random
each individual from the population has an equal chance of being included in the sample
Purposive
arbitrarily selecting a sample because it is believed to represent some population
Convenience
a sample that is convenient or available for use
Age and grade norms
-Average performance of test-takers at various ages/grades
-Scores do not represent equal units of measurement
-Scores often (incorrectly) used as evaluative standards
-Not effective with very young or adult test takers
National and anchor norms
-National norms are derived from "representative" samples of a country
-Often developed using stratified sampling methods
Sub-group
norms are created by selecting sub-groups from the normative sample
Local norm
Most useful in cases where the national norms may not represent the local population
Limitations
-It is important to understand that normed scores do not represent standards or goals to be achieved by students
-Criterion Reference Scores may have little or no application at the upper end of the knowledge/skill continuum
Mean
-can be pulled by outliers
-interval and ratio
Median
-middle
-nominal and ordinal
Mode
-most common
-nominal and ordinal
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