Psychometrics

-      Assessment

Four Pillars of Psychological Assessment

-      Formal Test

-      Interviews

-      Formal Test

o   Published and standardized and researched you know how good it is because of research

-      Interviews

-      Observations

o   Art to knowing what is relevant and what to observe, both restricted and free environments

o   Things you assess while searching for something else, may lead from one to another

Assessment

-      Anything we use to help us gain information, earliest done in China bur around since we studied behavior

World War I (1914-1918)

-      Where assessments took hold in this country, referred to as modern era of assessment

-      Using psychological tests to classify recruits, use info to get people into appropriate jobs

-      Device was developed for this:

o    Army Alpha, group given personality test

-      Interest in psychological tests is peaked at this time, psychologists used them for everyone – everywhere

-      This led to rights getting abused when done rampantly without a goal or plan in mind

o   group given personality test

o   grandparent of the current classification test (verbal)

o   Found a huge illiteracy rate, entrants couldn’t read or write to take the test

o   Developed Army Beta (nonverbal) still group administered

Civil Rights Act (1964)

-      Encompassing for everyone everywhere

-      Used as foundation for further legislature for psychological testing

Diana versus Board of Education (1971)

-      Plaintiffs were all Hispanic with English as second language with tests in English and scoring badly, so they were put into special education and though to be retarded

-      Violated by giving test without parents permission, then it being in English, then segrating them.

-      Changed by setting had to have parents permission for testing, then tests in first language, then if found deficit parents had to permit them to be in a special class

-      Applied to everyone in California any nationality then other states addressed similar issues and got to a critical mass

-      Federal Law to get parents permission in first language, and parent permission for placement

-      Gave a lot of jobs to psychologists

-      Created comparable legislature in jobs, institutions, etc.

Grigg’s Decision:

-      Anything that accompanies a job must be relevant to the job

-      Example: Interview, tests, applications, etc.

-      EEOC the watchdog of the industry (Equal Employment Opportunity)

Legislatures

-      Happened because of individual cases repeating over different states

-      Laid the foundation

Case of Larry P. (1970’s)

-      In California a class action suit parents of African American child

-      Intelligence testing were bias and put the children at a disadvantage because it was inappropriately normed

-      Law passed that you couldn’t use conventional intelligence tests for these children in California

-      Then in Illinois they didn’t pass the law

Good test must have:

-      Reliability

o   How consistent a test is, if not consistent it is worthless

-      Validity

o   Does it measure what we want it to, the accuracy of the test

-      they are interrelated , but different

-      must have reliability to have validity

-      Discrete Data (Dichotomous)

o   Fits into a specific category

-      Naturally occurring traits or characteristics can be either

-      Continuous Data

o   Exists in some degree

o   Example: height, weight, speed, intelligence, etc.

o   More complex data is continuous

-      Naturally occurring traits or characteristics can be either

Parametric tests

-      Used for continuous data

-      Some degree

Non-parametric tests

-      Used for discrete data

-      Specific Category

Statistical Scales

-      Nominal

-      Ordinal

-      Nominal

o   Non parametric/Continuous Data

o   Example: Name, social security number, etc.

o   not very descriptive, but a good start, can use for anything with a name

-      Ordinal

o   Non parametric/Continuous Data

o   Names and ranks order

o   Tell a little more than nominal

-      Interval

o   Parametric/Discrete Data

o   Name, rank, and numbers

o   Qualitative jump, can tell amounts and intervals

o   Can’t give absolute rates

-      Ratio

o   Parametric/Discrete Data

o   Absolute zero

o   If you’re going to measure something can’t conceive zero

Bar Graph

-      Discrete, nominal, and ordinal

Bell Curve

-      Continuous, ratio, interval

Population

-      Entails everything that contains a certain attribute we are measuring

-      Very large, but can be small as well

-      All members that have a certain trait

Sample

-      Same population small section representative of the population

-      Must reflect the population

Two qualities the sample must have to reflect the population

-      Variety/Hetergeneous/Diverse

-      Large (largeness alone will not ensure diversity)

Statistics

-      Allows us to collapse our data

-      Take a lot of data and make it more manageable

Mean of Central Tendency

-      One number that best represents the data/sample

-      Mean/average, median, and mode

Mean

-      Average

-      Used often

-      Is very susceptible to high or low scores

-      Example: 1, 2, 2, 1, 3 average would include and reflect scores

-      Example: 1, 2, 2, 1, 30 average would not reflect central tendency

Median

-      Fifty percent of scores above and fifty percent of score below

-      Not really used in statistics

Mode

-      Not used in statistics

Measure of Variability/Dispersion

-      Shows the diversity in the sample

-      Standard deviation, range, variance

Standard deviation

-      Scores show central tendency and how much dispersion

-      Measure of error

Range

-      Easy to get can’t do anything with it

Normal Distrubution

-      Bell shaped

-      Evenly distributed

-      If sample is large and diverse and SD is low you should have normal distribution

-      Mesokurtic

Skewed distribution

-      Refers to the shape

-      Positively Skewed:

-      Negatively Skewed:

-      Bimodal Distrubution:

-      Positively Skewed: