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Complete samenvatting ARMS/VOS
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  • Course
  • ARMS Advanced Research Methods And Statistics
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  • Universiteit Utrecht (UU)
  • Book
  • Discovering Statistics Using IBM SPSS

Deze samenvatting bevat aantekeningen van colleges, Grasple lessen en werkgroepen, inclusief voorkennis informatie. De samenvatting gaat over de General part van ARMS bij Psychologie en kan ook gebruikt worden voor het vak VOS bij andere opleidingen binnen de sociale wetenschappen.

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  • No
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  • January 17, 2022
  • 47
  • 2021/2022
  • Summary

Subjects

  • arms
  • vos
  • statistiek
  • advanced
  • statistics
  • psychologie
  • sociale wetenschappen

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  • Universiteit Utrecht (UU)
  • Psychologie
  • ARMS Advanced Research Methods And Statistics
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Notes ARMS General part

Mid-term exam


General


Measurement levels

 Nominal level of measurement: when the numbers do not represent

quantities but different categories, categorical variables.

o Example:

 variable: favourite colour

 values: 1= red, 2= blue, ..., 6= purple

 Ordinal level of measurement: when the numbers indicate that one value

is more / larger / higher / stronger than another, but not by how much

o Example:

 variable: clothing size

 Value: 1= xs, 2= s, 3= m, 4= l, 5= xl

 Interval measurement level: when the differences between numbers are

the same, but

- the value 0 is not an indication of the absence of the measured variable

- a value 2 or 3 times greater does not indicate that it is 2 or 3 times

more/longer/stronger

o Example:

 variable: IQ score

 values: min= 60, max= 140

 Ratio measurement level: when the differences between numbers are the

same and the value 0 is an indication of the absence of the measured

variable

o Example:

,  variable: body height of the participant

 values: between 80 and 210 cm

Lectures


Lecture 1


Simple linear regression > involves 1 outcome (Y) and 1 predictor (X).


 Outcome = DV = dependent variable (= e.g. IQ)

 Predictor = IV = independent variable (= e.g. Birth order)


yi = bo + b1Xi + ei


Multiple linear regression > involves 1 outcome and multiple predictors


yi = bo + b1Xi + b2X2i + b3X3i + ei


The relevance of a predictor:


 R2 = the amount of variance explained (i.e. the sizes of the residuals)




 b1 = the slope of the regression line (regression coefficient)

, A. The model


yi = b0 + b1X1i + b2X2i + ei


 b0 = intercept

 b1X1i = slope of x1

 b2X2i = slope of x2

 ei = residual


Observed outcome is a prediction based on the model and some error in the

prediction > yi = ^yi + ei


ei is the error in the prediction and is different for everyone. The i stands for

variables where people vary on. The terms without the i is a summary over a

whole group. The relation between X1 and y is b1.


Additive linear model > assumed is that the effects of each predictor is additive.


^yi = b0 + b1X1i + b2X2i


B. Types of variables


Formal distinction in 4 measurement levels


 Nominal

 Ordinal

 Interval

 Ratio

, For choice of analysis distinguish

Nominal + Ordinal > categorical/qualitative

Interval + Ratio > continuous/quantitative/numerical


 MLR requires continuous outcome and continuous predictors

 But categorical predictors can be included as dummy (D) variables

(variable with only two values (0 and 1), e.g. male (0) and female (1). Clear

interpretation.

Grade = B0 + B1*Dfemale.i

Grade male = B0

Grade female = B0 + B1

B1 is the difference in prediction between male and female.

 Categorical predictors with more than 2 levels, e.g. variable colour >

create multiple dummys > red/not, blue/not, green/not, yellow as

reference (B0)

^yi = b0 + b1Redi + b2Bluei + b3Greeni (NOT ^yi = b0 + b1Colouri)




C. MLR and hierarchical MLR


Research question 1: Can Life Satisfaction be predicted from age (x 1) and years

of education (x2).


Research question 2: Are social network factors (as measured by child support

(x3) and spouse support (x4) improving the prediction of Life Satisfaction, if the

effects of age and years of education are already accounted for.


Model 1: yi = b0 + biX1i +b2x2i + ei

Model 2: yi = b0 + b1X1i + b2X1i + b3X3i + b4X4i + ei


Hypotheses:

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