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
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
√ Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, Bancontact of creditcard voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper bernarditarichards. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €6,49. Je zit daarna nergens aan vast.
