Block 1 Analysis & Write-Up Example

Learning Objectives

At the end of this lab, you will:

1. Understand how to write-up and provide interpretation of linear models with single and multiple predictors.

What You Need

1. Be up to date with lectures
2. Have completed Labs 1 - 4

Required R Packages

Remember to load all packages within a code chunk at the start of your RMarkdown file using library(). If you do not have a package and need to install, do so within the console using install.packages(" "). For further guidance on installing/updating packages, see Section C here.

For this lab, you will need to load the following package(s):

• tidyverse
• psych
• patchwork
• sjPlot
• kableExtra

Lab Data

You can download the data required for this lab here or read it in via this link https://uoepsy.github.io/data/FOMOdataset.csv

Section A: Write-Up

In this lab you will be presented with the output from a statistical analysis, and your job will be to write-up and present the results. We’re going to use a simulated dataset based on a paper (the same one you have worked on in lectures this week) concerning Fear of Missing Out (FoMO), socio-demographic factors, and the Big Five personality traits.

The aim in writing should be that a reader is able to more or less replicate your analyses without referring to your R code. This requires detailing all of the steps you took in conducting the analysis.
The point of using RMarkdown is that you can pull your results directly from the code. If your analysis changes, so does your report!

Make sure that your final report doesn’t show any R functions or code. Remember you are interpreting and reporting your results in text, tables, or plots, targeting a generic reader who may use different software or may not know R at all. If you need a reminder on how to hide code, format tables, etc., make sure to review the rmd bootcamp.

Important - Write-Up Examples & Plagiarism

The example write-up sections included below are not perfect - they instead should give you a good example of what information you should include within each section, and how to structure this. For example, some information is missing (e.g., description of data checks, interpretation of descriptive statistics), some information could be presented more clearly (e.g., variable names in tables, table/figure titles/captions, and rationales for choices), and writing could be more concise in places (e.g., discussion section could be more succinct and more focused on the research questions in places).

Further, you must not copy any of the write-up included below for future reports - if you do, you will be committing plagiarism, and this type of academic misconduct is taken very seriously by the University. You can find out more here.

Study Overview

Research Aim

Explore the associations among Fear of Missing Out (FoMO), age, social media networks, and the Big Five personality traits.

Research Questions

• RQ1: Does age predict FoMO?
• RQ2: Does the number of Instagram followers explain a significant amount of variance in FoMO over and above age?
• RQ3: Does personality predict FoMO?

FoMO data codebook.

Description

The data used for this write-up exercise are simulated, drawing on recent work on Fear of Missing Out (FoMO), socio-demographic factors, and the Big Five personality traits. The simulated data are based on the findings of this work, and acted to expand upon the methods and results reported in the following paper:

Rozgonjuk, D., Sindermann, C., Elhai, J. D., & Montag, C. (2021). Individual differences in Fear of Missing Out (FoMO): Age, gender, and the Big Five personality trait domains, facets, and items. Personality and Individual Differences, 171, 110546. https://doi.org/10.1016/j.paid.2020.110546

In the current study, participants were invited to an online study investigating the associations among FoMO, socio-demographic factors, and personality. The final sample comprised 3370 people. Participants completed a FOMO scale and a personality inventory. The 10-item FOMO scale measured the extent of experiencing apprehension regarding missing out on interesting events of others on a 5-point scale (1 = “not at all true of me” to 5 = “extremely true of me”), producing a possible range of scores between 10 and 50. The Big Five Inventory (BFI) is a 45-item personality assessment questionnaire (note that only 43 items were used to match the study above) that uses a five-point response scale (1 = “very inapplicable” to 5 = “very applicable”). The BFI consists of five domains: Neuroticism (8 items; possible range of scores 8-40), Extraversion (8 items; possible range of scores 8-40), Openness to Experience (10 items; possible range of scores 10-50), Agreeableness (8 items; possible range of scores 8-40), and Conscientiousness (9 items; possible range of scores 9-45). We extended the aforementioned study to include an extra socio-demographic variable - a measure of popularity on social media based on the number of followers. Unlike the original study, we do not have measures of gender, education level, or specific country of residence.

Data Dictionary

The data in FOMOdataset.csv contain eight attributes collected from a simulated sample of \(n=3370\) hypothetical individuals across the UK, and include:

Variable	Description
FOMO	FoMO Score (as measured by the 10-item FoMO scale)
Age	Age (in years)
N	Score on personality items assessing Neuroticism from the Big Five Inventory (BFI)
E	Score on personality items assessing Extraversion from the Big Five Inventory (BFI)
O	Score on personality items assessing Openness from the Big Five Inventory (BFI)
A	Score on personality items assessing Agreeableness from the Big Five Inventory (BFI)
C	Score on personality items assessing Conscientiousness from the Big Five Inventory (BFI)
TotalFollowers	Total Number of Instagram Followers

Preview

The first six rows of the data are:

FOMO	Age	N	E	O	A	C	TotalFollowers
28	38	26	30	32	27	38	98
26	30	23	27	33	32	30	192
23	33	14	30	36	27	24	177
18	44	26	21	37	28	34	119
19	43	32	21	41	35	40	278
24	43	22	25	31	30	24	184

Setup

Setup

1. Create a new RMarkdown file
2. Load the required package(s)
3. Read the FOMO dataset into R, assigning it to an object named fomo

Solution

#Loading the required package(s)
library(tidyverse)
library(patchwork)
library(sjPlot)
library(kableExtra)
library(psych)

#Reading in fomo data and storing in object named 'fomo'
fomo <- read_csv("https://uoepsy.github.io/data/FOMOdataset.csv")

#check first six rows
head(fomo)

# A tibble: 6 × 8
   FOMO   Age     N     E     O     A     C TotalFollowers
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>          <dbl>
1    28    38    26    30    32    27    38             98
2    26    30    23    27    33    32    30            192
3    23    33    14    30    36    27    24            177
4    18    44    26    21    37    28    34            119
5    19    43    32    21    41    35    40            278
6    24    43    22    25    31    30    24            184

Provided Analysis Code

Below you will find the code required to conduct the analysis to address the research questions. This should look similar (in most areas) to what you worked through in lecture.

Provided Analysis Code

Data Management

library(tidyverse) # for all things!
library(psych) # good for descriptive stats
library(patchwork) # grouping plots together
library(kableExtra) # useful for creating nice tables
library(sjPlot) #regression tables & plots

fomo <- read_csv("https://uoepsy.github.io/data/FOMOdataset.csv")

# standardise FoMO & personality scores for RQ3
fomo <- 
  fomo %>% 
    mutate(
      FOMOz = (FOMO-mean(FOMO))/sd(FOMO),
      Oz = (O-mean(O))/sd(O),
      Cz = (C-mean(C))/sd(C),
      Ez = (E-mean(E))/sd(E),
      Az = (A-mean(A))/sd(A),      
      Nz = (N-mean(N))/sd(N))
#alternatively, you could do FOMOz = scale(FOMO, center = TRUE, scale = TRUE)

Overall

#######
#Descriptive Stats
#######


# the describe() function is from the psych package, and kable() from kableExtra which is used to make a nice table where the values are rounded to 2 decimal places using digits = 2. 
# We are first renaming our variables to give more appropriate / informative names.
#Next we are selecting columns 2, 3, 4, 8, and 9 from the describe output (n, mean, sd, min, max)

fomo %>% 
    select(FOMO, Age, TotalFollowers, N, E, O, C, A) %>%
    rename("Fear of Missing Out" = FOMO, "Age (in years)" = Age, "Number of Instagram Followers" = TotalFollowers, "Neuroticism" = N, "Extraversion" = E, "Openness" = O, "Conscientiousness" = C, "Agreeableness" = A) %>%
    describe() %>%
    select(2:4, 8:9) %>%
    rename("N" = n, "Mean" = mean, "SD" = sd, "Minimum" = min, "Maximum" = max) %>%
        kable(., caption = "FoMO, Socio-Demographic Factors, and Personality Traits Descriptive Statistics", digits = 2) %>%
        kable_styling()   

FoMO, Socio-Demographic Factors, and Personality Traits Descriptive Statistics

	N	Mean	SD	Minimum	Maximum
Fear of Missing Out	3370	24.63	6.42	10	46
Age (in years)	3370	33.61	10.42	12	75
Number of Instagram Followers	3370	203.25	93.42	1	594
Neuroticism	3370	22.92	5.77	8	40
Extraversion	3370	25.88	5.94	8	40
Openness	3370	37.61	5.80	14	50
Conscientiousness	3370	31.04	5.49	13	45
Agreeableness	3370	30.89	4.93	13	43

#from above, no missing values and scores within range (look at min and max values)


# scatterplot matrix, hist, and corr of FoMO, Socio-Demographic Factors, and Personality Traits Descriptive Statistics
pairs.panels(fomo %>%
    select(-FOMOz, -Oz, -Cz, -Ez, -Az, -Nz)) 

RQ1

#######
#Descriptive Stats
#######

fomo %>% 
    select(FOMO, Age) %>%
    rename("Fear of Missing Out" = FOMO, "Age (in years)" = Age) %>%
    describe() %>%
    select(2:4, 8:9) %>%
    rename("N" = n, "Mean" = mean, "SD" = sd, "Minimum" = min, "Maximum" = max) %>%    
        kable(., caption = "FoMO and Age Descriptive Statistics", digits = 2) %>%
        kable_styling()    

FoMO and Age Descriptive Statistics

	N	Mean	SD	Minimum	Maximum
Fear of Missing Out	3370	24.63	6.42	10	46
Age (in years)	3370	33.61	10.42	12	75

# scatterplot
p1 <- ggplot(data = fomo, aes(x = Age, y = FOMO)) + 
    geom_point() + 
  geom_smooth(method = 'lm', se = FALSE, colour = 'red', linewidth=2) +
  labs(x = "(a) Age (in years)", y = "Fear of Missing Out")
p1

#######
#Model Building
#######

fomo_mdl1 <- lm(FOMO ~ Age, data = fomo)
summary(fomo_mdl1)

Call:
lm(formula = FOMO ~ Age, data = fomo)

Residuals:
     Min       1Q   Median       3Q      Max 
-17.1028  -4.2129  -0.1602   4.0551  22.2321 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 31.22239    0.35408   88.18   <2e-16 ***
Age         -0.19617    0.01006  -19.50   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.084 on 3368 degrees of freedom
Multiple R-squared:  0.1014,    Adjusted R-squared:  0.1011 
F-statistic: 380.1 on 1 and 3368 DF,  p-value: < 2.2e-16

confint(fomo_mdl1)

                 2.5 %     97.5 %
(Intercept) 30.5281496 31.9166293
Age         -0.2158992 -0.1764425

#######
#Table for Results
#######

tab_model(fomo_mdl1,
          dv.labels = "FoMO",
          pred.labels = c("Age" = "Age (in years)"), 
          title = "RQ1: Regression Table for FoMO Model")

RQ1: Regression Table for FoMO Model

	FoMO
Predictors	Estimates	CI	p
(Intercept)	31.22	30.53 – 31.92	<0.001
Age (in years)	-0.20	-0.22 – -0.18	<0.001
Observations	3370
R2 / R2 adjusted	0.101 / 0.101

RQ2

#######
#Descriptive Stats
#######

fomo %>% 
    select(FOMO, Age, TotalFollowers) %>%
    rename("Fear of Missing Out" = FOMO, "Age (in years)" = Age, "Number of Instagram Followers" = TotalFollowers) %>%
    describe() %>%
    select(2:4, 8:9) %>%
    rename("N" = n, "Mean" = mean, "SD" = sd, "Minimum" = min, "Maximum" = max) %>%    
        kable(., caption = "FoMO and Socio-Demographic Factors Descriptive Statistics", digits = 2) %>%
        kable_styling()    

FoMO and Socio-Demographic Factors Descriptive Statistics

	N	Mean	SD	Minimum	Maximum
Fear of Missing Out	3370	24.63	6.42	10	46
Age (in years)	3370	33.61	10.42	12	75
Number of Instagram Followers	3370	203.25	93.42	1	594

# scatterplots
p2 <- ggplot(data = fomo, aes(x = TotalFollowers, y = FOMO)) + 
    geom_point() + 
  geom_smooth(method = 'lm', se = FALSE, colour = 'purple', linewidth=2) +
  labs(x = "(b) Total Number of \nInstagram Followers", y = "Fear of Missing Out")

p2

#######
#Model Building
#######

fomo_mdl2 <- lm(FOMO ~ Age + TotalFollowers, data = fomo)
summary(fomo_mdl2)

Call:
lm(formula = FOMO ~ Age + TotalFollowers, data = fomo)

Residuals:
    Min      1Q  Median      3Q     Max 
-17.273  -4.066  -0.071   3.841  21.705 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)    26.521966   0.446021   59.46   <2e-16 ***
Age            -0.165111   0.009871  -16.73   <2e-16 ***
TotalFollowers  0.017989   0.001101   16.34   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 5.857 on 3367 degrees of freedom
Multiple R-squared:  0.1674,    Adjusted R-squared:  0.1669 
F-statistic: 338.6 on 2 and 3367 DF,  p-value: < 2.2e-16

confint(fomo_mdl2)

                     2.5 %      97.5 %
(Intercept)    25.64746694 27.39646570
Age            -0.18446526 -0.14575589
TotalFollowers  0.01583102  0.02014788

#######
#Model Comparison
#######

anova(fomo_mdl1 ,fomo_mdl2) %>%
    kable(caption = "Model Comparison - fomo_mdl1 vs fomo_mdl2", align = "c", digits = c(2,2,2,2,2,60)) %>%
    kable_styling(full_width = FALSE)

Model Comparison - fomo_mdl1 vs fomo_mdl2

Res.Df	RSS	Df	Sum of Sq	F	Pr(>F)
3368	124676.4	NA	NA	NA	NA
3367	115515.0	1	9161.44	267.04	7.98e-58

#######
#Table for Results
#######

tab_model(fomo_mdl1 ,fomo_mdl2,
          dv.labels = c("FoMO","FoMO"),
          pred.labels = c("Age" = "Age (in years)",
                          "TotalFollowers" = "Number of Instagram Followers"), 
          title = "RQ2 - Regression Table for FoMO Model")

RQ2 - Regression Table for FoMO Model

	FoMO			FoMO
Predictors	Estimates	CI	p	Estimates	CI	p
(Intercept)	31.22	30.53 – 31.92	<0.001	26.52	25.65 – 27.40	<0.001
Age (in years)	-0.20	-0.22 – -0.18	<0.001	-0.17	-0.18 – -0.15	<0.001
Number of Instagram Followers				0.02	0.02 – 0.02	<0.001
Observations	3370			3370
R2 / R2 adjusted	0.101 / 0.101			0.167 / 0.167

RQ3

#######
#Descriptive Stats
#######

fomo %>% 
    select(FOMO, N, E, O, C, A) %>%
    rename("Fear of Missing Out" = FOMO, "Neuroticism" = N, "Extraversion" = E, "Openness" = O, "Conscientiousness" = C, "Agreeableness" = A) %>%
    describe() %>%
    select(2:4, 8:9) %>%
    rename("N" = n, "Mean" = mean, "SD" = sd, "Minimum" = min, "Maximum" = max) %>%    
        kable(., caption = "FoMO and Personality Descriptive Statistics", digits = 2) %>%
        kable_styling()  

FoMO and Personality Descriptive Statistics

	N	Mean	SD	Minimum	Maximum
Fear of Missing Out	3370	24.63	6.42	10	46
Neuroticism	3370	22.92	5.77	8	40
Extraversion	3370	25.88	5.94	8	40
Openness	3370	37.61	5.80	14	50
Conscientiousness	3370	31.04	5.49	13	45
Agreeableness	3370	30.89	4.93	13	43

#######
#Model Building
#######

fomo_mdl3 <- lm(FOMOz ~ Nz + Ez + Oz + Cz + Az, data = fomo)
summary(fomo_mdl3)

Call:
lm(formula = FOMOz ~ Nz + Ez + Oz + Cz + Az, data = fomo)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.74061 -0.60648 -0.01036  0.59506  3.13402 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.453e-17  1.527e-02   0.000    1.000    
Nz           4.266e-01  1.643e-02  25.971  < 2e-16 ***
Ez           1.744e-02  1.570e-02   1.111    0.267    
Oz           1.117e-02  1.550e-02   0.721    0.471    
Cz          -3.079e-01  1.596e-02 -19.289  < 2e-16 ***
Az          -8.508e-02  1.544e-02  -5.511 3.83e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.8865 on 3364 degrees of freedom
Multiple R-squared:  0.2152,    Adjusted R-squared:  0.2141 
F-statistic: 184.5 on 5 and 3364 DF,  p-value: < 2.2e-16

confint(fomo_mdl3)

                  2.5 %      97.5 %
(Intercept) -0.02994194  0.02994194
Nz           0.39441959  0.45883445
Ez          -0.01334388  0.04823358
Oz          -0.01921470  0.04155523
Cz          -0.33916065 -0.27657348
Az          -0.11535145 -0.05481487

#plot model examining significant personality predictors
N_plot <- plot_model(fomo_mdl3, type = "eff",
           terms = c("Nz"),
           show.data = TRUE,
           axis.title = c("Neuroticsm \n(z-scored)","FoMO Score (z-scored)"),
           title = "FoMO & N")

C_plot <- plot_model(fomo_mdl3, type = "eff",
           terms = c("Cz"),
           show.data = TRUE,
           axis.title = c("Conscientiousness \n(z-scored)","FoMO Score (z-scored)"),
           title = "FoMO & C")

A_plot <- plot_model(fomo_mdl3, type = "eff",
           terms = c("Az"),
           show.data = TRUE,
           axis.title = c("Agreeableness \n(z-scored)","FoMO Score (z-scored)"),
           title = "FoMO & A")

N_plot | C_plot | A_plot

#create table for results - RQ3
tab_model(fomo_mdl3,
          dv.labels = "FoMO (Z-Scored)",
          pred.labels = c("Nz" = "Neuroticism (Z-Scored)",
                          "Ez" = "Extraversion (Z-Scored)",
                          "Oz" = "Openness (Z-Scored)",
                          "Az" = "Agreeableness (Z-Scored)",
                          "Cz" = "Conscientiousness (Z-Scored)"),
          title = "RQ3 - Regression Table for FoMO Model")

RQ3 - Regression Table for FoMO Model

	FoMO (Z-Scored)
Predictors	Estimates	CI	p
(Intercept)	-0.00	-0.03 – 0.03	1.000
Neuroticism (Z-Scored)	0.43	0.39 – 0.46	<0.001
Extraversion (Z-Scored)	0.02	-0.01 – 0.05	0.267
Openness (Z-Scored)	0.01	-0.02 – 0.04	0.471
Conscientiousness (Z-Scored)	-0.31	-0.34 – -0.28	<0.001
Agreeableness (Z-Scored)	-0.09	-0.12 – -0.05	<0.001
Observations	3370
R2 / R2 adjusted	0.215 / 0.214

The 3-Act Structure

We need to present our report in three clear sections - think of your sections like the 3 key parts of a play or story - we need to (1) provide some background and scene setting for the reader, (2) present our results in the context of the research question, and (3) present a resolution to our story - relate our findings back to the question we were asked and provide our answer.

Act I: Analysis Strategy

Question 1

Attempt to draft an analysis strategy section based on the above research question and analysis provided.

Analysis Strategy - What to Include***

Your analysis strategy will contain a number of different elements detailing plans and changes to your plan. Remember, your analysis strategy should not contain any results. You may wish to include the following sections:

• Very brief data and design description:
  • Give the reader some background on the context of your write-up. For example, you may wish to describe the data source, data collection strategy, study design, number of observational units.
  • Specify the variables of interest in relation to the research question, including their unit of measurement, the allowed range (for Likert scales), and how they are scored.
• Data management:
  • Describe any data cleaning and/or recoding.
    
  • Are there any observations that have been excluded based on pre-defined criteria? How/why, and how many?
    
  • Describe any transformations performed to aid your interpretation (i.e., mean centering, standardisation, etc.)
• Model specification:
  • Clearly state your hypotheses and specify your chosen significance level (i.e., \(\alpha\)).
    
  • What type of statistical analysis do you plan to use to answer the research question? (e.g., simple linear regression, multiple linear regression, binary logistic regression (which you will see later in this course), etc.)
    
  • In some cases, you may wish to include some visualisations and descriptive tables to motivate your model specification.
  • Specify the model(s) to be fitted to answer your given research question and analysis structure. Clearly specify the response and explanatory variables included in your model(s).

*** This is not yet a completed list – we will add more to this as we progress through the course (e.g., coding of categorical variables, checking assumptions, diagnostics, etc.)

As noted and encouraged throughout the course, one of the main benefits of using RMarkdown is the ability to include inline R code in your document. Try to incorporate this in your write up so you can automatically pull the specified values from your code. If you need a reminder on how to do this, see Lesson 3 of the Rmd Bootcamp.

Solution : Example Write-Up of Analysis Strategy Section

The dataset contained information on 3370 participants, including scores on 5 personality traits assessed by the a reduced version of the 45 item Big Five Inventory (BFI; Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism), where 43 items were used. Participants were also assessed on the FoMO scale - a 10 item measure which assessed the extent to which individuals experienced apprehension regarding missing out on interesting events of others. Items on both the BFI and FoMO scales were measured on a 5-point scale, meaning that scores on the BFI could range 8-40 for Neuroticism, 8-40 for Extraversion, 10-50 for Openness to Experience, 8-40 for Agreeableness, and 9-45 for Conscientiousness; and scores on the FoMO scale could range 10-50. For both of these measures, only total scores were available. Two socio-demographic questions were also asked of all participants - their age (in years) and total number of Instagram followers.

The aim of this report was to address three research questions:

1. Does age predict FoMO?
2. Does the total number of people one follows on Instagram predict FoMO over and above age?
3. Does personality predict FoMO?

To examine the associations among the variables of interest, a scatter plot of matrices (SPLOM) was used. This allowed for visual examination of marginal distributions via histograms, and bivariate associations between variables via scatter plots. The SPLOM also provided bivariate correlations between each of the variables.

To address RQ1, the following simple linear regression model was used:

\[ \text{FoMO} = \beta_0 + \beta_1 \cdot \text{Age} + \epsilon \]

where we tested whether there was a significant association between FoMO and age. Formally, this corresponded to testing whether the age coefficient was equal to zero:

\[ H_0: \beta_1 = 0 \]

\[ H_1: \beta_1 \neq 0 \]

To address RQ2, the two following models were used: \[ \text{FoMO} = \beta_0 + \beta_1 \cdot \text{Age} + \epsilon \]

\[ \text{FoMO} = \beta_0 + \beta_1 \cdot \text{Age} + \beta_2 \cdot \text{Number of Instagram Followers} + \epsilon \]

To determine whether the model with the number of Instagram followers and age was a significantly better fitting than the model with age alone, we used an incremental \(F\)-test to formally test the following hypothesis:

\[ H_0: \beta_2 = 0 \]

\[ H_1: \beta_2 \neq 0 \]

To address RQ3, the following multiple linear regression model was used, where all measures were \(z\)-scored:

\[ \text{FoMO} = \beta_0 + \beta_1 \cdot \text{N} + \beta_2 \cdot \text{E} + \beta_3 \cdot \text{O} + \beta_4 \cdot \text{C} + \beta_5 \cdot \text{A} + \epsilon \quad \\ \]

\[ \begin{align} & \text{Where:} \\ & \text{FoMO} = \text{FoMO, z-scored} \\ & \text{N} = \text{Neuroticism, z-scored} \\ & \text{E} = \text{Extraversion, z-scored} \\ & \text{O} = \text{Openness, z-scored} \\ & \text{C} = \text{Conscientiousness, z-scored} \\ & \text{A} = \text{Agreeableness, z-scored} \\ \end{align} \]

To investigate whether personality predicted FoMO, this corresponded to testing whether each of the Big 5 personality traits were equal to zero:

\[ H_0: \text{All}~~ \beta_j = 0 ~\text{(for j = 1, 2, 3, 4, 5)} \] \[ H_1: \text{At least one}~ \beta_j \neq \text{(for j = 1, 2, 3, 4, 5)} \]

All participant data was complete (no missing values), with scores on the FoMO and BFI scales within possible ranges. Throughout the report, effects were considered statistically significant at \(\alpha = .05\).

Act II: Results

Question 2

Attempt to draft a results section based on your detailed analysis strategy and the analysis provided.

Results - What To Include***

The results section should follow from your analysis strategy. This is where you would present the evidence and results that will be used to answer the research questions and can support your conclusions. Make sure that you address all aspects of the approach you outlined in the analysis strategy.

In this section, it is useful to include tables and/or plots to clearly present your findings to your reader. It is important, however, to carefully select what is the key information that should be presented. You do not want to overload the reader with unnecessary or duplicate information (e.g., do not present print outs of the head of a dataset, or the same information in tables and plots, etc.), and you also want to save space in case there is a page limit. Make use of figures with multiple panels where you can.

As a broad guideline, you want to start with the results of any exploratory data analysis, presenting tables of summary statistics and exploratory plots. You may also want to visualise associations between/among variables and report covariances or correlations. Then, you should move on to the results from your model.

*** This is not yet a completed list – we will add more to this as we progress through the course (e.g., evaluation of diagnostic plots, etc.)

Solution : Example Write-Up of Results Section

Descriptive statistics are displayed in Table 1.

Table 1: FoMO, Socio-Demographic Factors, and Personality Trait Descriptive Statistics

	N	Mean	SD	Minimum	Maximum
Fear of Missing Out	3370	24.63	6.42	10	46
Age (in years)	3370	33.61	10.42	12	75
Number of Instagram Followers	3370	203.25	93.42	1	594
Neuroticism	3370	22.92	5.77	8	40
Extraversion	3370	25.88	5.94	8	40
Openness	3370	37.61	5.80	14	50
Conscientiousness	3370	31.04	5.49	13	45
Agreeableness	3370	30.89	4.93	13	43

Bivariate correlations showed a moderate negative association between FoMO and age; moderate positive associations between FoMO and Neuroticism and between FoMO and number of Instagram followers; and weak associations between FoMO and the remaining Big 5 personality variables (see Figure 1).

Figure 1: Bivariate scatter plots (below diagonal), histograms (diagonal), and Pearson correlation coefficient (above diagonal) for FoMO, Socio-Demographic Factors, and Personality Traits

In relation to RQ1, full regression results, including 95% Confidence Intervals, are shown in Table 2. This model was significant (\(F(1, 3368)=380.1, p <.001)\), and results indicated that age was a significant predictor of FoMO \((\beta = -0.2, CI_{95} = [-0.22, -0.18], SE = 0.01, p <.001)\). Age explained approximately 10% of the variance in FoMO scores. For every one-year increase in age, participants’ FoMO scores decreased by 0.20 points. This suggested that older participants experienced less FoMO than younger participants (see Figure 2(a)).

Table 2: RQ1 - Regression Table for FoMO Model

	FoMO
Predictors	Estimates	CI	p
(Intercept)	31.22	30.53 – 31.92	<0.001
Age (in years)	-0.20	-0.22 – -0.18	<0.001
Observations	3370
R2 / R2 adjusted	0.101 / 0.101

Figure 2: (a) Association between FoMO and Age (b) Association between FoMO and Instagram Followers

As presented in Table 3, we found evidence that an individual’s total number of followers on Instagram and age provided a significantly better fitting model predicting FoMO over age alone \((F(1 ,3367) = 267.04, p<.001)\). Together, age and the number of Instagram followers explained approximately 17% of the variance in FoMO scores.

Table 3: RQ2 - Model Comparison - fomo_mdl1 vs fomo_mdl2

Res.Df	RSS	Df	Sum of Sq	F	Pr(>F)
3368	124676.4	NA	NA	NA	NA
3367	115515.0	1	9161.44	267.04	7.98e-58

In order to address the third research question, all scores were converted to \(z\)-scores. The Big 5 personality traits significantly predicted FoMO scores (\(F(5, 3364) = 184.53, p < .001\)). The personality traits collectively explained approximately 21% of variance in FoMO scores. Full model results are presented in Table 4. For every 1 standard deviation unit increase in Neuroticism scores, FoMO scores increased by 0.43 standard deviations after controlling for other personality traits. When accounting for other personality traits, for every 1 standard deviation unit increase in Conscientiousness scores, FoMO scores decreased by 0.31 standard deviations. For every 1 standard deviation unit increase in Agreeableness scores, FoMO scores decreased by 0.09 standard deviations after controlling for other personality traits. Neither Extraversion \((p = .27)\) nor Openness \((p = .47)\) were significant predictors of FoMO.

Table 4: RQ3 - Regression Table for FoMO Model with Personality

	FoMO (Z-Scored)
Predictors	Estimates	CI	p
(Intercept)	-0.00	-0.03 – 0.03	1.000
Neuroticism (Z-Scored)	0.43	0.39 – 0.46	<0.001
Extraversion (Z-Scored)	0.02	-0.01 – 0.05	0.267
Openness (Z-Scored)	0.01	-0.02 – 0.04	0.471
Conscientiousness (Z-Scored)	-0.31	-0.34 – -0.28	<0.001
Agreeableness (Z-Scored)	-0.09	-0.12 – -0.05	<0.001
Observations	3370
R2 / R2 adjusted	0.215 / 0.214

Associations between FoMO and the three significant personality traits (whilst holding the others constant) are shown in Figure 3.

Figure 3: Predicted FoMO Scores by Neuroticsm, Conscientiousness, and Agreeableness after accounting for other Big 5 Traits

Act III: Discussion

Question 3

Attempt to draft a discussion section based on your results and the analysis provided.

Discussion - What To Include

In the discussion section, you should summarise the key findings from the results section and provide the reader with a few take-home sentences drawing the analysis together and relating it back to the original question.

The discussion should be relatively brief, and should not include any statistical analysis - instead think of the discussion as a conclusion, providing an answer to the research question(s).

Solution : Example Write-Up of Discussion Section

Previous research had identified an association between an individual’s fear of missing out, various socio-demographic factors, and personality. In this analysis, we examined the associations of FoMO with age, total number of Instagram followers, and personality.

Our results suggested that: (1) age was a significant predictor of FoMO, where older age was associated with lower levels of FoMO; (2) including the total number of Instagram followers as well as age resulted in a significantly better fitting model for explaining FoMO scores (where more followers were associated with higher levels of FoMO); and (3) three of the Big Five personality traits - Neuroticism, Agreeableness, and Conscientiousness - were significant predictors of FoMO (specifically high Neuroticism, low Agreeableness, and low Conscientiousness). Therefore, we can reject the null hypotheses for each of our research questions.

However, it is important to note that we can make no causal claims on the directions of these associations from these data - we cannot conclude that being a young person who is highly Neurotic and low on Agreeableness and Conscientiousness with a large number of Instagram followers will cause feelings of FoMO.

Section B: Block 1 (Weeks 1 - 4) Recap

In the second part of the lab, there is no new content - the purpose of the recap section is for you to revisit and revise the concepts you have learned over the last 4 weeks.

We would encourage you to complete any outstanding work on these exercises (e.g., complete partial write-ups), and review solutions. Doing so will allow you to have good quality materials to refer to during the assessed report (released in Semester 2).

Given that we are now \(\frac{1}{4}\) of the way through the DAPR2 course, we would also strongly encourage you to start creating your revision materials in advance of the exam. You can access all the flashcards that you’ve been presented with in this block here. These will provide a good starting point for collating your notes together on the block 1 content. We also suggest that you review your weekly quiz feedback (as many of you have learned in Psychology 2A, it is important to provide feedback to allow learners to improve their learning and retention of information, as well as correct any misunderstandings!).