Samenvatting

Summary Tutorial and midterms of RStudio

0 keer verkocht

Vak
S__RMCS (S_RMCS)

Instelling
Vrije Universiteit Amsterdam (VU)

Tutorial and midterms with some additional information. I will keep it up to date every week before the midterms. You're allowed to keep this with you! ! tutorial 1 is missing, so the document is from tutorial 2 and later

[Meer zien]

Laatste update van het document: 2 jaar geleden

Voorbeeld 6 van de 65 pagina's

Bekijk voorbeeld

Geupload op 2 oktober 2022
Bestand laatst geupdate op 16 oktober 2022
Aantal pagina's 65
Geschreven in 2022/2023
Type Samenvatting

marketing
research methods
methods
onderzoeksmethoden
rstudio
research methods of communication science
r

Instelling
Vrije Universiteit Amsterdam (VU)
Studie
Master Communicatie Wetenschap
Vak
S__RMCS (S_RMCS)

Volgen

Vustudentt

Lid sinds 7 jaar 206 documenten verkocht

€6,49

In winkelwagen

Op verlanglijstje

100% tevredenheidsgarantie
Direct beschikbaar na je betaling
Lees online óf als PDF
Geen vaste maandelijkse kosten

Research Methods in Communication
Science
2022/2023 | Vrije Universiteit Amsterdam

Table of content
RESEARCH METHODS IN COMMUNICATION SCIENCE.......................................................................................................................... 1
COMMANDOS IN RSTUDIO................................................................................................................................................................. 2
TUTORIAL 2........................................................................................................................................................................................ 6
MIDTERM 2...................................................................................................................................................................................... 13
TUTORIAL 3...................................................................................................................................................................................... 19
MIDTERM 3...................................................................................................................................................................................... 29
TUTORIAL 4...................................................................................................................................................................................... 33
TUTORIAL 5...................................................................................................................................................................................... 43
MIDTERM 5...................................................................................................................................................................................... 52
TUTORIAL 6...................................................................................................................................................................................... 57

, Commandos in RStudio
Dataset1 = data set | Variables x1, x2, x3

Creating variable
Ong1$New_Var <-  R knows to make this part of dataframe
New_Var <- this is hanging out of it’s own. Not part of dataframe

Choose dataset Choose.files(dataset)  Apple: File.choose(dataset)
Working Directory at Session. Apple at desktop, Windows C drive
Getting started File --> new file --> RScript
Install package install.packages("x1")
Or on the right side at packages
Load package library(x1)
Open data set Dataset1 <- read_sav(‘’Dataset1”)
- Or: File>import dataset>From SPSS
Descriptive statistics summary(Dataset1)
describe(Dataset1)
Missing values summary(Dataset1)
Mean of variables describe(Dataset1) or specific mean(Dataset1$x1)
- If NA comes up  mean(Dataset1$x1, na.rm = T)
Save a mean mean_predicted_y <- mean(Dataset1$y_predicted)  to make
predicted value, see predicted value
Frequencies/mode of variables table(Dataset1$x1)
Median of variable describe(Dataset1) or specific median(Dataset1$x1)
Standarddeviation describe(Dataset1)
Sd(Dataset1$x1)
Labels of variables names/values view_df(Dataset1)
Number of variables rows nrow(Dataset1)
Number of variables columns ncol(Dataset1)
Histograms hist(Dataset1$x1)
Making new variable Dataset1$NEWNAME <- ….
Standardizing Dataset1$x1standardized <- scale(Dataset1$x1)
Dummy/recoding Dataset1$x1dummy <- ifelse(Dataset1$x1 < median(Dataset1$x1,
na.rm=T, 0, 1)
- Or: Dataset1$x1 <- ifelse(Dataset1$x1 < 0.5, 0, 1)
- (Condition, outcome if yes, outcome if no)
Continqency table/cross-tabu.. CrossTable(Dataset1$x1, Dataset1$x2)
- CrossTable(x-axis, y-axis)
- Crosstable(row, colomn)
Chi-squared test chisq.test(Dataset1$x1, Dataset1$x2)
- chisq.test(x-axis, y-axis)
- chisq.test(row, colomn)
Linear regression lm(x1 ~ x2, data = Dataset1)
- lm(dependent ~ independent, data = name data set)
- lm(y-axis ~ x-axis, data = name data set)
Standardized coefficients lm.beta(x1)
Confidence intervals confint(x1)
Mean square regression summary(aov(x1))
- Or: summary.aov(x1)
Regression table summary(lm(x1))
- Or: lm.aov(x1)

,Explained variance summary(x1)
Filter filter(Dataset1, x1 <80)
- Filter(Dataset 1, condition)
Scatter plot plot(x1 ~ x2, data = Dataset1)
- plot(dependent ~ independent, data = name data set)
- plot(y-axis ~ x-axis, data = name data set)
Regression line in scatter plot plot(x1 ~ x2, data = Dataset1) and then abline(x3)

New column Dataset1$NEWCOLUMNNAME <- …
- if you want a column which +10 another column (example x3:
o Dataset1$x2plus10 <- Dataset1$x2 + 10
Predicted value Dataset1$Ypredicted <- 9.09 – 0.2 * Dataset1$x1
- After <- is based on the Y=a + bX
Calculate residuals Dataset1$Residuals <- Dataset1$x1 – Dataset1$Ypredicted
- After <- is based on Y -
Mean of residuals mean(Dataset1$Rediduals, na.rm=T)
Mean of squared residuals Dataset1$squaredresiduals <- Dataset1$Residuals^2
- To calculate the mean: mean(…, na.rm=T)
Sum of squared residuals (SSE) sum(Dataset1$squaredresiduals, na.rm=T)
Squared difference between… Y and mean of predicted Y  Y-
1. meanY <- (Dataset1$x1)
2. Dataset1$squareddifference <- (Dataset1$x1 – meanY)^2
Predicted Y and mean of Y
1. For predicted Y see predicted value
2. Dataset1$squareddifferencepredy <- (Dataset1$Ypredicted – meanY)^2
Total sum of squares (TSS) sum(Dataset1$squareddifference, na.rm=T)
Regression sum of squares (RSS) sum(Dataset1$squareddifferencepredy, na.rm=t)
R squared R2 R2 = RSS / TSS
RSS <- sum(Dataset1$squareddifferencepredy, na.rm=t)
TSS <- sum(Dataset1$squareddifference, na.rm=T)
Hierarchical linear regression BlockA <- lm(dependent ~ independentv1 + independentv2 + … , data =
Ong1)
See linearity plot(BlockA, 1)
- Shows residuals vs fitted plot
Homoscedasticity plot(BlockA, 3)
- Stan. Residuals vs fitted values
Normality residuals plot(BlockA, 2) or hist(BlockA$residuals)
- Normal Q-Qplot
Independence of residuals durbinWatsonTest(BlockA)
- detect presence of autocorrelation
Multicollinearity vif(BlockA)
- Variance inflation factor
No influential observations Plot(BlockA, 4)
- leverage and residual size (Cook)
How are variables coded typeof(Dataset1$x1)
Test difference significance anova(BlockA, BlockB)
Comparing models anova(BlockA, BlockB)
Delete missing values Dataset1 <- na.omit(Dataset1)
Mean center Dataset1$x1_c <- scale(Dataset1$x1, scale = FALSE)
Process mediation process(data = YOUR_DATASET_NAME, y = "YOUR_Y_VARIABLE", x =
"YOUR_X_VARIABLE", m ="YOUR_MEDIATOR_VARIABLE", model = 4, total
=1, stand =1, normal = 1)
Process moderation process(data = YOUR_DATASET_NAME, y = "YOUR_Y_VARIABLE", x =

, "YOUR_X_VARIABLE", w ="YOUR_MODERATOR_VARIABLE", model = 1, plot
=1, center =1, intprobe = 1, moments = 1, jn=1, hc=0)
Conditional effects sim_slopes((REGRESSION_OBJECT, pred = DEPENDENT_VARIABLE, modx =
MODERATOR, johnson_neyman = FALSE)
Graph conditional effects GraphAggress <- sim_slopes(Reg2_c, pred = Vid_Game_c, modx = CaUnTs_c)
plot (GraphAggress)
Johnson Neyman johnson_neyman(REGRESSION_OBJECT, pred = DEPENDENT_VARIABLE,
modx = MODERATOR, alpha = .05)
Adding up YOUR_DATASET$S2 <- YOUR_DATASET$V11S2 + YOUR_DATASET$V12S2 + ...
+ YOUR_DATASET$V16S2
Where: V11 till V16 and S2,4,5,6)
Taking columns/subset data_practicum5A_SUBSET <- select(data_practicum5A, ppnr,S2,S4,S5,S6)
Or: data_practicum5A_SUBSET <- subset(data_practicum5A, select =
c(ppnr,S2,S4,S5,S6))
From wide to long format data_practicum5A_LONG <-melt(data_practicum5A_SUBSET, id="ppnr",
value.name = "emotie", variable.name= "schilderij")
Run model repeated measures YOUR_MODEL1 <- aov_car(emotie ~ schilderij + Error(ppnr/schilderij),
data=data_practicum5A_LONG)
Estimated Marginal Means emmeans(YOUR_MODEL1, "schilderij")
Visualize Estimated Marginal Means emmip(YOUR_MODEL1, ~ schilderij, CIs = TRUE)
Post-hoc comparisons pairwise.t.test(data_practicum5A_LONG$emotie,
data_practicum5A_LONG$schilderij, p.adj = "bonf")
Coding as categories/factors 1. Test  is.factor(data_practicum5B_LONG$stadium)
2. Perform  YOUR_DATASET$YOUR_VARIABLE <-
as.factor(YOUR_DATASET$YOUR_VARIABLE)
Create subset t0 <- filter(data_practicum5B_LONG, stadium == "hcul0")
Descriptives different groups describeBy(YOUR_DATA, YOUR_DATA $INDEPENDENT_VARIABLE)
Boxplot ggplot(data_practicum6, aes(x=INDEPENDENT_VAR, fill=INDEPENDENT_VAR,
y=DEPENDENT_VAR1)) + geom_boxplot()
MANOVA Model1 <- manova(cbind(DEPENDENT_VAR1, DEPENDENT_VAR2,
DEPENDENT_VAR3) ~ INDEPENDENT_VAR, data = YOUR_DATA)
Wilks’s Lamda summary(Model1, test="Wilks")
Hotelling’s Trace summary(Model1, test="Hotelling")
Roy’s largest Root summary(Model1, test="Roy")
Pillai’s trace summary(Model1)
ANOVA summary summary.aov(Model1)
Data frame format Dataset <- as.data.frame(dataset)
Box’s test box <- boxM(data_practicum6[, c("DEPENDENT_VAR1",
"DEPENDENT_VAR2", "DEPENDENT_VAR3")], data_practicum6[,
"INDEPENDENT_VAR"])
summary(box, cov=TRUE)
Covariance summary(box, cov=TRUE)
Cross-products SSCP.fn(YOUR_MODEL1)
Sum of Squares + Cross products crossproducts <-SSCP.fn(Model1)
crossproducts$SSCPR/crossproducts$SSCPE
crossproducts$SSCPR/crossproducts$SSCPE*100
Discriminant analysis candisc(Model1)
Correlation of the variates output_candisc = candisc(YOUR_MODEL1)
output_candisc$scores[2]
output_candisc$scores[3]
cor(output_candisc$scores[2], data_practicum6$salary)
cor(output_candisc$scores[3], data_practicum6$salary)
cor(output_candisc$scores[2], data_practicum6$work)
etc…

,Raw coefficients candisc coef(output_candisc, type = c("raw"))
Standardized coefficients candisc coef(output_candisc, type = c("std"))
Plot candisc plot(output_candisc)

, Tutorial 2

#Installeren packages
install.packages("ggplot2")
install.packages("QuantPsyc")
install.packages("car")
install.packages("lm.beta")
install.packages("haven")
install.packages("psych")
install.packages("gmodels")
install.packages("tidyverse")
install.packages("sjPlot")
install.packages("broom")
install.packages("jtools")
install.packages("huxtable")
install.packages("pwr")

#Laden packages
library("ggplot2")
library("QuantPsyc")
library("car")
library("lm.beta")
library("haven")
library("psych")
library("gmodels")
library("tidyverse")
library("sjPlot")
library("broom")
library("jtools")
library("huxtable")
library("pwr")

Checking if regression assumptions are met:
1. linearity,
2. homoscedasticity,
3. independence of the residuals,
4. normal distribution of the residuals,
5. no multicollinearity, and
6. no influential observations

Hierarchical linear regressions
BlockA <- lm(Ong1$FB_Status ~ Ong1$Age + Ong1$Gender, data = Ong1)
BlockB <- lm(Ong1$FB_Status ~ Ong1$Age + Ong1$Gender + Ong1$NEO_FFI, data = Ong1)
BlockC <- lm(Ong1$FB_Status ~ Ong1$Age + Ong1$Gender + Ong1$NEO_FFI + Ong1$NPQC_R, data = Ong1)

Question 1 – check for linearity
plot(BlockC, 1)
A horizontal line, without distinct patterns is an indication for a linear relationship

Linearity is met when in the plot of residuals vs fitted values no particular pattern of divergence from the
horizontal 0-line emerges. Any such patters would mean that residuals tend to be e.g. (highly) positive for some
fitted values and (highly) negative for other fitted values. Your evaluation of the plot can be assisted by the red
lowess line that is printed. This is a smoothed line of the conditional means of the residuals for all fitted

Dit zijn jouw voordelen als je samenvattingen koopt bij Stuvia:

Bewezen kwaliteit door reviews

Studenten hebben al meer dan 850.000 samenvattingen beoordeeld. Zo weet jij zeker dat je de beste keuze maakt!

In een paar klikken geregeld

Geen gedoe — betaal gewoon eenmalig met iDeal, Bancontact of creditcard en je bent klaar. Geen abonnement nodig.

Focus op de essentie

Studenten maken samenvattingen voor studenten. Dat betekent: actuele inhoud waar jij écht wat aan hebt. Geen overbodige details!

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 Vustudentt. 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.

Is Stuvia te vertrouwen?

4,6 sterren op Google & Trustpilot (+1000 reviews)

Afgelopen 30 dagen zijn er 69411 samenvattingen verkocht

Opgericht in 2010, al 15 jaar dé plek om samenvattingen te kopen

Start met verkopen

Samenvatting

Summary Tutorial and midterms of RStudio

Document informatie

Onderwerpen

Geschreven voor

Verkoper

Ontvangen beoordelingen

Voorbeeld van de inhoud