Lecture 1. INTRODUCTION
● Psychometrics:assessing the attributes of psychologicaltests
○ Interindividual = compare the behavior of different people
○ Intraindividual = compare the behavior of the same person at different points in
time
● Criterion-referenced tests: compare each score withapredeterminedcut-off point
● Norm-referenced tests:compare each score with areferencesampleandnorm
● Path diagram
○ Latent variable (unobservable)
○ Items (observable)
○ Error (unobservable LV)
● Psychological theory
○ Decides what is relevant to be measured
○ Informs statistics: make “distributional assumptions” based on theory
● Statistics: analysis of individual differences
● Causality
○ Relative items:item directly and causally relatedto the LV
(correlated)
○ Formative items: items are not causally dependenton the
index variable - items scores determine the test score
● Properties of Numeral
○ Property of identity: differentiate between categoriesof people (mutuallyexclusive&exhaustive)
○ Property of order:indicate therankorderof peoplerelative to each other along asingledimension
(implies transitivity)
○ Property of quantity: adds information concerningamountto the numeral expressed in numerical counts
of units
■ Absolute zero:absence of the attribute
■ Relative zero:assignments of zero to an arbitraryvalue
● Measurement Levels
○ Nominal scale: Numbers are simply ways to codecategoricalinformation
■ Property of identity
○ Ordinal scale:Numbers assigned have meaning in thatthey demonstrate arank orderof the classes
■ Property of identity & order
○ Interval scale: Provides a rank order of objects wheredifferences in scale values expressdifferences in
amount
■ Property of identity + order + amount
■ Zero isrelative(not absolute)
○ Ratio scale: Property of identity + order + amount+absolute zero
,Lecture 2. LINEAR REGRESSION
● Linear regression = conditionalMEAN.
o Conditional mean:mean score on a variable given thescore on another variable.
● If we have Y= b0 + b1 * x → no te olvides que es la formula predicted value!! (y=ȳ).
o b0 : intercept/constant: predicted value ofywhenx= 0.
o b
1 : slope :regression coefficient: relationshipbetweenxandy: change iny,
asxincreases by 1.
o No error.
o Predicted formula and not observed one.
o We look at the red line instead of a gray line.
● Conditional mean(of y) = Predicted mean(of y).
● Notation y I x
o y given x.
o Conditional mean
● Assumptionsdistribution of ylinear regression (3):
1. It needs to be linear regression.
2. yis normally distributed for all values ofx
o F or each value of x, y needs to be normally distributed, and the mean of normal distribution
equals the predicted score of y
o Therefore, thepredicted score of y=conditional mean.
3. Variation(SD) in scores onyis the same for allvalues ofx.
● No assumptions fordistribution of x.
● b1: represent also the difference between the scoresby the two variables. Ex: differences score men and woman.
● Is the relationship relevant?→we need to study→R2=varianceof yexplainedby x = measure effectsize.
o var (y) explained variance by x=b1 2 * var (x)
o var (y) not explained variance by x=standard errorof the estimate =var (e)
o Total variance (y)= b12* var (x)+var (e)
o R2=b12* var (x)/ (b1 2* var (x)+var (e))
● Psychological variables → standardize score → multiple ways to do this:
1. Z-score
o Z score - does NOT require assumption of normality (M and s)
o Normalize Z score (based on empirical percentile score) - requires assumption of normality.
2. Other distributions (t-score)
3. Percentile score
o Empirical
● Does not required normal distribution
● F rom empirical tonormalizez score also possible→ assuming normal distribution from
population
● Based on data
● Theoretical(table)
F rom z-score to percentile score
●
● Normally distributed
● Interpreting scores
○ Variance: how much the scores in a distribution deviatefrom the mean
○ Standard deviation:square root variance
, S kewed distribution:positive → right tail/ negative → left tail
○
○ Kurtosis: positive → taller / negative → shorter
○ Covariance: degree of association between thevariabilityintwo
distributions(positive/ negative)
■ Provides information aboutdirection
○ Correlation: Degree of association between two variables
(strong/ weak)
○ Correlation coefficient: number of correlation - from-1
to +1
○ Reflectsmagnitude: close to -1 or +1 means that the
association is very strong
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller bernarditarichards. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.02. You're not tied to anything after your purchase.
