Tests and Testing - Tests and Measurements - Lecture Notes, Study notes of Psychological Data

Tests and Testing, Psychological Testing and Assessment, Controversial Assumption, Sampling Techniques, Types of Norms, Developmental Norms, Norm Referenced, Pearson Formula, Graphing Correlation, Positively Correlated. You might have found many different lecture notes on Tests and Measurements regarding psychological disorder over internet but this set I am uploading is best you can find.

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2011/2012

Uploaded on 12/11/2012

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Tests and Testing
12 Assumptions in Psychological Testing and Assessment
Assumption 1: Psychological traits and states exist
Assumption 2: Psychological traits and states can be quantified and measured
Assumption 3: Various approaches to measuring aspects of the same thing can be useful
Assumption 4: Assessment can provide answers to some of life’s most momentous
questions
12 Assumptions in Psychological Testing and Assessment
Assumption 5: Assessment can pinpoint phenomena that require further attention or study.
Assumption 6: Various sources of data enrich and are part of the assessment process.
Assumption 7: Various sources of error are part of the assessment process.
12 Assumptions in Psychological Testing and Assessment
Assumption 8: Tests and other measurement techniques have strengths and weaknesses
Assumption 9: Test-related behavior predicts non–test-related behavior
Assumption 10: Present-day behavior sampling predicts future behavior
12 Assumptions in Psychological Testing and Assessment
Assumption 11: Testing and assessment can be conducted in a fair and unbiased manner
Assumption 12: Testing and assessment benefit society
Most Controversial Assumption?
Why?
What are Norms?
Derived typical test performance of a standardization sample
The test score distribution that provides the average or typical (normal) score level on a test
Standardization (normative) Sample
The normative sample is a representative subset drawn from the broader target population
Typically, a large random sample
Sample size should be large enough to obtain stable values.
Sampling Techniques
Randomization
every case has an equal chance of selection
Stratified
representative proportions of groups
e.g. age, socioeconomic level, ethnicity
Incidental
Convenience sampling
Not a desired procedure
Types of Norms
Developmental Norms
Indicates developmental level attained
Age equivalent norms (Mental Age)
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Tests and Testing

12 Assumptions in Psychological Testing and Assessment

  • Assumption 1: Psychological traits and states exist
  • Assumption 2: Psychological traits and states can be quantified and measured
  • Assumption 3: Various approaches to measuring aspects of the same thing can be useful
  • Assumption 4: Assessment can provide answers to some of life’s most momentous questions
  • 12 Assumptions in Psychological Testing and Assessment
  • Assumption 5: Assessment can pinpoint phenomena that require further attention or study.
  • Assumption 6: Various sources of data enrich and are part of the assessment process.
  • Assumption 7: Various sources of error are part of the assessment process.
  • 12 Assumptions in Psychological Testing and Assessment
  • Assumption 8: Tests and other measurement techniques have strengths and weaknesses
  • Assumption 9: Test-related behavior predicts non–test-related behavior
  • Assumption 10: Present-day behavior sampling predicts future behavior
  • 12 Assumptions in Psychological Testing and Assessment
  • Assumption 11: Testing and assessment can be conducted in a fair and unbiased manner
  • Assumption 12: Testing and assessment benefit society

Most Controversial Assumption?

  • Why?

What are Norms?

  • Derived typical test performance of a standardization sample
  • The test score distribution that provides the average or typical (normal) score level on a test

Standardization (normative) Sample

  • The normative sample is a representative subset drawn from the broader target population
  • Typically, a large random sample
  • Sample size should be large enough to obtain stable values.

Sampling Techniques

  • Randomization
    • every case has an equal chance of selection
  • Stratified
    • representative proportions of groups
    • e.g. age, socioeconomic level, ethnicity
  • Incidental
    • Convenience sampling
    • Not a desired procedure

Types of Norms

  • Developmental Norms
  • Indicates developmental level attained
  • Age equivalent norms (Mental Age)
  • e.g., a 7 year old who scores the same mean obtained by 10 year old children has a mental age of 10.
  • Grade equivalent norms
  • e.g., average score of 4th graders is 23, a child with a raw score of 23 is given a 4th grade age equivalence.

Norm-Referenced (Within group) Norms

  • Individual performance is evaluated in reference to a standardization group
  • The same test is used to compare other groups of test-takers
  • Deviation IQs

What is Correlation?

  • Index of linear association between two variables (X and Y)
  • Does not suggest cause and effect
  • Computed value is called a coefficient
  • Best example is the Pearson product-moment correlation coefficient (r)

Pearson Formula (definitional)

  • Co-variation between X and Y
  • Ratio of the variability between X and Y

Values of r

  • Coefficient values range between -1 and + 1
  • What does 0 mean?
    • The closer the coefficient value is to 0, the weaker the association between two variables
  • The further a coefficient moves from 0, the stronger the association between two variables
    • Coefficients of -1 and +1 have the same magnitude of association

Coefficient of Determination ( r^2 )

  • Correlation coefficient squared
  • The value indicates the proportion of the variation in Y scores that is a function of the X scores
  • i.e., the variance in X explained by Y

Graphing Correlation

  • Correlations between two variables can be displayed in a scatterplot
  • Individual scores are plotted on two-dimensional axes
    • X scores plotted on horizontal axis (abscissa)
    • Y scores plotted on vertical axis (ordinate)

Positively Correlated

  • As X increases, Y increases
  • Criterion (predictive) validity coefficients
    • Correlation between test scores and results of an independent criterion
  • Correlation between SAT and College GPA
  • Convergent validity coefficients
    • Correlation between scores on two conceptually similar tests
      • Correlation between self-esteem and self-concept

Regression

  • Degree of predictability between two variables
  • Extends the concept of correlation to the prediction of a test score (Y) based on a another test score (X)

Regression Equation Y’ = a + bX

  • X = predictor (test score)
  • Y’ = criterion (predicted score)
  • a = y-intercept (criterion score if the predictor score is 0)
  • b = slope (correlation between the predictor and criterion)

Regression Line

  • Line drawn through the scatter of scores
  • The regression line represents the Principle of Least Squares
  • least squared deviation from the line
  • The line demonstrates the best fit for all data points

Slope

  • Essentially, the correlation coefficient

Y-Intercept

  • Where the regression line crosses the Y-axis
  • Criterion score if the predictor score is 0

a = Y – bX

  • Y is the mean of the Y scores
  • X is the mean of the X scores

Regression Example Y’ = 2 + 0.67X

  • What is the predicted score (Y’) if X is 10?

Regression Line

  • Line drawn through the scatter of scores
  • The regression line represents the Principle of Least Squares
    • least squared deviation from the line
  • The line demonstrates the best fit for all data points
  • Regression Line Example

Residuals

  • Difference between the predicted (Y’) and observed criterion (Y) values
  • Y – Y’
  • Principle of Least Squares
  • Minimize the deviation between Y and Y’

Standard Error of Estimate

  • Error in the prediction estimate
    • Standard deviation of the residuals
      • The square root of the residual variance
  • The lower the standard deviation, the lower the degree of error in the regression equation

Inference from Measurement

  • Meta-Analysis
    • Statistical combination of studies
  • Culture and Inference
    • Individualists vs. collectivists