IRT-M Vignette (Synthetic Data)
Margaret J Foster
2025-04-16
Packages that we will use for data prep, IRT-M estimation, and data
visualization:
## Data prep:
library(tidyverse) # version: tidyverse_2.0.0
library(dplyr) #version: dplyr_1.1.4
library(stats) # version: stats4
library(fastDummies) # version: fastDummies_1.7.3
library(reshape2) #version: reshape2_1.4.4
## IRT-M estimation:
#devtools::install_github("dasiegel/IRT-M")
library(IRTM) #version 1.00
## Results visualization:
library(ggplot2) # version: ggplot2_3.4.4
library(ggridges) #version: ggridges_0.5.6
library(RColorBrewer) #version: RColorBrewer_1.1-3
library(ggrepel) # version: ggrepel_0.9.5
In this document, we walk through using the IRT-M package.
The IRT-M framework may be used for a wide variety of cases. This
vignette focuses on a single, hypothetical, use case, in which a
research team seeks empirical support for a hypothesis: in this case,
the threat-response hypothesis that anti-immigration attitudes in Europe
are associated with perceptions of cultural, economic, and security
threats. We illustrate data preparation, IRT-M estimation,
visualization, and analysis with a small synthetic dataset (N=3000)
based on the Eurobarometer 94.3 wave. The real data can be accessed via:
.
This example illustrates one of the strengths of the IRT-M model: the
(very common) situation in which researchers have a theoretical question
and related data that does not directly address the substantive question
of interest. In this case, we work through a research question derived
from the literature on European attitudes towards immigration. The
threat-response hypothesis is supported by the literature~, but was not
a specific focus of the 2020-2021 Eurobarometer wave. Consequentially,
the survey did not directly ask questions about the three threat
dimensions of interest. However, the survey did contain several
questions adjacent to threat perception. We can use those questions to
build what we call an M-Matrix (a matrix of constraints for the item
discrimination loadings present in item response theory models), and
estimate latent threat dimensions.
Regardless of the use case, there are two steps involved in preparing
data for the IRT-M Package:
- First, one needs to acquire and appropriately format a dataset
comprising a series of items, the responses to which one believes are
related to the set of underlying theoretical concepts that one wants to
measure. One input of IRT-M is this
N x K data frame of
N responses to K items. Column names of the
data frame should provide the names of the items. We’ll refer to this
data frame as Y_in in what follows.
- Second, one must create a key that specifies the theoretical
connection between each item in one’s data and each underlying concept.
A second input of IRT-M, when run with constraints, is a
K x (1+d) data frame of K items and their
pre-specified theoretical connections to each of d
dimensions. Those connections must be provided in the last d columns;
the first column should contain item names, which must match the column
names of the data matrix. We’ll refer to this data frame as
M_matrix in what follows.
Loading the package; Dependencies
Gfortran is a necessary to load the package. Gfortran can be readily
downloaded and the version installed can be checked. In Windows, enter
into the command line “$ gfortran –version GNU Fortran.” The easiest way
to make sure this works in Windows is to install Rtools, making sure
that its version matches the version of R you are running. On a Mac,
enter into the terminal “which gfortran.” Some machines running Mac OS
encounter a well-known problem with R finding gfortran. The
steps for solving this are documented online, and typically entail a
combination of installing Xcode, using
homebrew to install gcc, and/or installing
gfortran directly from Github. After
gfortran has been successfully installed, it is important
to also have GCC (GNU Compiler Collection) installed. In
addition to gcc allowing its installation, the IRT-M
package depends on functions developed in other libraries, and the
Rfortran dependency requires gcc to be
installed.
Formatting the Observed Data
Formatting the Input Data
Though we are working to extend it to other forms of data, at
present, IRT-M operates only with binary (dichotomous) items. Thus, it
requires that users convert continuous or categorical variables into a
series of binary variables. Those binary variables will be the items,
and responses to those items will be used to discern positions on each
latent theoretical dimension one is attempting to measure.
The most straightforward way to convert to binary from a categorical
variable is to use a library, such as fastDummies, to
expand the entire set of variables into one-hot (binary) encoding.
Similarly, one could use packages such as arulesCBA or
arules to convert continuous variables to categorical
variables, and then fastDummiesto convert those categorical
variables in binary variables. One’s input data frame, Y_in, should have
N dichotomous responses to each of K binary
items; in addition to 0 and 1, NA
is also acceptable.
It is good practice, and will make one’s life easier later, to
capture the column headers of Y_in (which will be item
codes) and export them for use in constructing the
M_matrix. Doing so will avoid mismatches between the items
in the input data and the items one will be coding.
The Y_in and M_matrix data frames described
earlier are all one needs to use IRT-M. How one obtains them will vary
by use case. We’ll describe here how we obtained ours for this vignette,
though some steps will be specific to our use case, in which we start
with an M_matrix for the real, and complete, Eurobarometer 94.3 wave,
but use input data that is a synthetic subset of the real data. (Morucci
et. al 2024 used the real data.)
To begin, we reformat the input data so that each possible answer
becomes a separate binary item (One Hot encoding). In preparing the
data, we used the dummy_cols() utility from the
fastdummies package. If running this exact code on your own
system, please ensure that the dataPath variable is
adjusted for your local file structure.
synth_questions <- NULL # Initialize to avoid R CMD check notes
load("./vdata/synth_questions.rda")
## Convert numeric ordinal responses to factors
ebdatsub <- lapply(ebdatsynth[,], factor) ## that's a list now
## converts the list back into a dataframe:
Y <- dummy_cols(.data=ebdatsub,
remove_selected_columns=TRUE)
## remove the .data that dummy_cols adds to the column names
colnames(Y) <- gsub(".data.", '', colnames(Y))
## remove the data objects:
rm(ebdatsub)
rm(ebdatsynth)
Creating the M-Matrix
Creating the M_matrix
The core step in using the IRT-M package is to map the theoretical
connections between items in the data and the underlying theoretical
constructs of interest into an M_Matrix of dimension
K x (1+d). To do so, we go through every item in the data
(here: every response to every question in the survey) and decide
whether the item relates to zero, one, or more of our latent theoretical
dimensions. For those items that we believe relate to a theoretical
dimension of interest, we can code whether we expect it to positively
load—meaning that higher values on that dimension are associated with
greater responses to the item (here: greater means more likely to choose
a given response to a given question)—or negatively load. We code
positive “loading” as 1 for that item-dimension pair in the
M_matrix, negative “loading” as -1. We can also denote that
the item has no relationship with the theoretical dimension of interest,
in which case we code 0 for that item-dimension pair. If we
are unsure, we can use an NA value in the
M_matrix for the item-dimension pair, and the model will
assume relatively diffuse priors on that loading.
If one has followed this vignette’s advice and exported a column of
item codes when formatting the input data frame, Y_in, then
the easiest way to specify the M_matrix is to open a
spreadsheet with the first column (labeled QMap in the
vignette) being the item codes, and the remaining d columns
the theoretical dimensions to be measured (with appropriately named
column headers). We have also found it to be worthwhile to insert a
column with human-readable notes next to the item codes. This step adds
some overhead, but—much like commenting code in general—is invaluable
for debugging and analysis. You will need to remove that column before
running the M_matrix through IRT-M, but there is also a
spot in one of our analysis functions, described below, that can
incorporate that information in your analysis.
For this vignette, we are interested whether respondents to the
2020-2021 Eurobarometer reported feeling social, cultural, and/or
economic threats. Depending on the data, you may need to do some
additional processing at this step to ensure that each question coded is
a binary response that has a single relationship to each dimension being
coded. Many surveys present questions formatted into feeling
thermometers, matrices, or with many nested sub-questions. These may not
load straightforwardly into a single dimension. For example, Question
377 (qa1a_4) in our data asks for respondents’ feelings about various
current situations, compactly presented via a feeling thermometer for
several sub-questions. In this example we will code the fourth, “How
would you judge the current situation in [….] your personal job
situation.” The respondent’s answer is coded as a categorical variable
ranging from 1-4, with a response of 1 corresponding to “Very Good” and
4 corresponding to “Very Bad.” We imagine that this question directly
relates to the “economic threat” underlying dimension that we
hypothesize, with the low end of the scale indicating feelings of
economic threat and the high end suggesting no threat.
Thus, we expand the levels of the question prompt to become four
separate yes/no questions:
- qa1a_4_1. Situation of personal job: very good.
- qa1a_4_2 Situation of personal job: rather good
- qa1a_4_3 Situation of personal job: rather bad
- qa1a_4_4. Situation of personal job: very bad
Note that if you had followed our advice above, these items would
have already been separated for you and would occur as rows of the
spreadsheet you were using to specify your M_matrix. Also
note that we do not have an omitted category in this coding. That is
deliberate: even in a yes/no question, it is possible that responses of
“yes” implicate latent theoretical dimensions differently from responses
of “no”. For example, a “yes” might suggest a particular action or
thought process, but many actions or thought processes could be
responsible for a “no”.
Once the M_matrix contains a column of item codes for
binary items, we can specify our theoretical loadings for those items.
In the M_matrix, we add a 1 to the column for
the Economic Threat dimension for qa1a_4_3 and qa1a_4_4. Substantively,
this means that responses to the qa1a_4 with a value in the data of “3”
or “4” are coded as individual respondents who likely would score high
on the “economic threat” dimension. We can also code the inverse and
give qa1a_4_1 and qa1a_4_2 the value of -1 in the
M_matrix because we expect that survey respondents who
answer that they feel that their personal job situation is “very good”
or “rather good” likely would not score highly on economic threat.
We repeat that process for each of the items that we think relate in
any fashion (positively, negatively, or in an unclear way) to any of our
underlying theoretical dimensions. Note that an item may relate in some
way to no dimensions, one dimension, or more than one dimension, and
that these relationships may be dependent or independent. Also note that
if you think an item may relate but are not sure how exactly, the safest
option is an NA.
We can also use this same technique to discern latent outcome
variables in the data, and include them among our latent dimensions. In
other words, we can code items that relate to potential dependent
variables as well, and have those dependent variables be additional
dimensions in our M_matrix, as long as we are careful not
to use the same items in measuring latent dimensions intended to be
independent and dependent variables in the same correlational model
later. This vignette additionally highlights the ability of the IRT-M
model to handle inductive theoretical exploration. While coding the
M_matrix for the three dimensions of threat noted above, we
observed that the survey itself featured a number of questions about
feelings of threat related to the ongoing Covid-19 pandemic. IRT-M
easily supports inductive coding, and we added another dimension for the
emergent category of “health threat.”
The coding step is the only really time consuming step in the
process, but it is what allows us to make theoretical claims about the
substantive meaning of the latent dimensions. Fortunately, though the
input data may have many items potentially to be coded, the actual
number to code is likely to be substantially smaller than the total, as
we only need to code those items that relate to the latent dimensions.
We can ignore the rest, since the constrained IRT will not use any item
which has loadings of 0 for every latent dimension.
For our vignette, we thus end up with a K x (1+d)
matrix, where K is the number of theoretically-salient
binarized questions and d is the combined number of
underlying latent explanatory dimensions (4) and latent outcomes (2).
For convenience, we processed the constraint coding in a separate table,
which we import. We reduce its size by getting rid of rows of zeroes.
The MCodes matrix that we important has question codes,
item codes to match to the input data matrix, a column of human-readable
comment, and then loadings for each item-dimension pair across six
dimensions.
load('./vdata/mcodes.rda')
## Only keep M-Codes with loadings or outcomes:
MCodes$encoding <- rowSums(abs(MCodes[,4:9]))
MCodes <- MCodes[which(MCodes$encoding > 0),]
Processing with IRT-M
The last step before we can start our analysis is to produce the
Y_in and M_matrix data frames we need. To get
these, we must ensure that the columns of Y_in (and their
headers) match the rows of M_matrix (and the item codes in
its first column). If we had followed our own advice, this would already
be true, and if you have followed our advice, you can skip to the next
step. Sometimes, however, one might have cause to code the constraint
matrix before one hot encoding the input data, in which case some effort
might be needed to ensure a match due to different column names produced
by dummy_cols. In our case, as our data are synthetic and
only a subset of the real data, we’ll need to eliminate a bunch of items
to ensure a match. We do that by merging the data and loadings,
resulting in a K x R matrix where K is the
number of binary questions that we have codes for and R is
the number of instrument responses, before then reversing the
combination. This is accomplished by the following code snippet:
## Produce a K-coded questions x R-responses data frame:
d <- 6 #number of coded dimensions
mcolumns <- c("QMap", "D1-Culture threat",
"D2-ReligionThreat",
"D3-Economic Threat",
"D4-HealthThreat",
"O1-OutcomeSupportImmigration", "O2-OutcomeSupportEU")
combine <- MCodes[,mcolumns] %>% ## question codes and loadings
inner_join(
Y %>%
t() %>%
as.data.frame(stringsAsFactors = FALSE) %>%
type_convert() %>%
rownames_to_column(var = "question"),
by = c("QMap" = "question" )
)
M_matrix <- as.data.frame(combine[, 1:(d+1)])
#Reverse the earlier transposition of the observations:
Y_in <- combine[, (d+2):ncol(combine)]%>%
t() %>%
as.data.frame()
Y_in <- as.data.frame(sapply(Y_in, as.numeric))
## Take the question names and
## convert to column names
question <- combine[,1] %>%
as.data.frame()
colnames(Y_in) <- question[,1]
rm(combine)
rm(question)
Troubleshooting note: it is important that the Y_in
object be numeric and a matrix. You can check the data types by running
dplyr’s summary(response_object) [or similar]
and adjust the data type if needed. Likewise, if you run the IRT-M
sampler and it returns the message
Error: Not compatible with requested type: [type=list; target=double]
you probably either passed a non-numeric data type in or didn’t pass a
matrix to the Y_in argument.
##Running IRT-M
Running IRT-M
Once we have the input data, Y_in, and the constraint
matrix, M_matrix, we can now proceed to run IRT-M. The
package provides a wrapper to make running the IRT-M sampler easier: the
irt_m() function. It takes as arguments Y_in
and M_matrix, as well as the number of latent dimensions,
d. If no constraint matrix is provided, it runs an
unconstrained IRT model. If M_matrix is provided and not
NULL, the first two things irt_m() does is
check to make sure that the column headers of Y_in match
the item codes in the first column of M_matrix. It quits
with an error if they do not. This is to ensure that the analyst is
using the expected set of items in the computation. Then,
irt_m() checks to make sure that the number of dimensions
provided by the analyst (in d) is one fewer than the number
of columns of M_matrix. After those checks,
irt_m() instantiates a K x d x d array of
diagonal matrices, built from M_matrix, that will be used
for the sampler, and checks one more time to make sure that all the
dimensions are correct, quitting with an error if not.
Next, irt_m creates a set of 4d(d-1) (for
d>1) or 2 (for d=1) synthetic
anchor points via the package function pair_gen_anchors().
Those points are mathematically optional for the purposes of identifying
the model, but are substantively helpful in that they aid in providing a
clear substantive interpretation and consistent scale of the underlying
space. For every pair of latent dimensions, four “fake” respondents are
created. Those respondents are assumed to have each possible combination
of maximum/minimum positions along each dimension in the pair. Then, the
M_matrix is used to infer how those fake respondents would
have responded to each item. For example, a respondent with maximal
levels of economic and cultural threat would respond 1 to
items that were coded positively relative to both latent dimensions and
0 to items that were coded negatively relative to both
latent dimensions. irt_m() adds these fake respondents to
Y_in and then passes this new matrix, Y_all,
to the sampler, which returns a list with the estimated
Theta, Lambda, b,
Sigma, and Omega posterior distributions for
the model. By default, the fake respondents are placed at the beginning
of Y_all; however, before returning its list,
irt_m() removes the fake respondents, so that their
presence does not hinder further analyses. It does this via two objects
that help to keep track of the introduced anchor points:
d_which_fix and d_theta_fix. Those record the
indices of the synthetic extreme points that were created in
pair_gen_anchors().
In addition to Y_in, M_matrix, and
d, irt_m() also has four additional arguments
that may be used if desired. Three are technical variables related to
the sampler. These specify the number of burn-in MCMC iterations
(nburn, Default=1000), the number of sampling
MCMC iterations (nsamp, Default=1000), and the
number of thinning MCMC samples (thin,
Default=10). The fourth is a logical variable
(learn_loadings, Default=FALSE). IRT-M
defaults to learning the covariance matrix of the latent dimensions, but
setting this variable to TRUE has it instead learn the
covariance matrix of the item loadings.
One does not need to use irt_m() if one does not want:
one can directly use the sampler instead via the
M_constrained_irt() function. Doing so requires creating
the array of constraint matrices and anchor points oneself, as those
need to be passed to the sampler as arguments.
Moving to the output list of irt_m(), the
theta array gives us samples from the posterior
distribution of respondents’ positions along each of the latent
dimensions. This object is an array of dimension
N x d x nsamp/thin, or number of respondents x number of
latent dimensions x number of saved simulations. The lambda
array, of dimension K x d x nsamp/thin, contains samples
from the posterior distribution of item loadings (discrimination
parameters) for each item-dimension pair. The b output
array, of dimension K x nsamp/thin, contains samples from
the posterior distribution of item difficulty parameters. The
Sigma array, of dimension d x d x nsamp/thin,
contains samples from the posterior distribution of the covariance
matrix of latent dimensions. It is only learned if
learn_loadings=FALSE, which is the default. The
Omega array, of dimension d x d x nsamp/thin,
contains samples from the posterior distribution of the covariance
matrix of item loadings. It is only learned if
learn_loadings=TRUE.
Note that in the following code we’ve reduced the sampling settings a
bit from the default, since the goal of the vignette is merely to be
instructive and running quickly (less than a minute) is helpful for
that. One can also increase to nsamp=10000 and
nburn=2000 if one desires and has the time, though using
the default thin=10 (or greater) is recommended in that case. Running an
unconstrained IRT, useful when the analyst wants to evaluate the impact
of coding decisions, can be done by instead using
M_matrix=NULL or simply leaving it out.
d=6
irt <- irt_m(Y_in = Y_in, d = d, M_matrix = M_matrix, nsamp = 1000, nburn=20, thin=1)
Interpretation and Analysis
Interpretation and analysis
We include samples from the posterior distribution as the analyst may
desire to use them in capturing measurement uncertainty. For our purely
descriptive purposes here, however, we’ll want to take the means of the
relevant posterior distributions. The most important mean we’ll take, in
order to produce a population-level distribution of positions on the
latent dimensions, is that of the theta array. In
helpers_analysis.R, we provide a function,
theta_av(), to do this. It takes as input the
theta array and returns an N x d dimensional
matrix of estimated positions for each respondent, for each
dimension.
avgthetas <- theta_av(irt$theta)
We can now begin our analysis, and we start by bringing in additional
variables from the original dataset into the analysis. The IRT-M code
itself does not keep unique ids for responses; however, it retains the
order of the rows of the input data. This allows one to attach other
variables to the matrix of theta estimates,
avgthetas. After bringing in the additional variables, we
then specify all column names of this new data frame (`thetas’). This
provides us a data frame that can be used for inferential and
descriptive analysis.
In the context of the synthetic survey that forms this vignette, we
can bring in both metadata and survey instruments themselves. More
broadly, we could connect the estimated thetas to any respondent-level
metadata to which we had access, though that might require assigning
codes to each respondent and merging data.
Here we read in a data frame with demographic data from the survey,
and bind that to the theta averages. `synth_idvs’ is a table with a
synthetic version of independent variables from the underlying survey
data. It takes the large survey dataset and extracts respondent-level
questions that we’re interested in for the visualizations: respondent
unique identifiers and country, their self-reported socioeconomic class,
and their self-reported political orientation. In the following code, we
import the data and attach it to our averaged respondent-level theta
estimates. We also make the column names more human-readable. At this
stage, we convert several of the metadata columns to factors, to
facilitate the visualizations.
We finish by calling R’s head() method to preview the
data.
## load idvs:
load("./vdata/synth_idvs.rda")
thetas <- cbind(avgthetas, synthidvs)
## Rename columns for readability:
colnames(thetas)[1:6] <- paste0("Theta", 1:6)
colnames(thetas)[colnames(thetas)=="qb7_2"] <- "MoreBorderControl"
## Cast into factors:
thetas$mediatrust <- as.factor(thetas$mediatrust)
thetas$class <- as.factor(thetas$class)
thetas$polorient <- as.factor(thetas$polorient)
head(thetas)
#> Theta1 Theta2 Theta3 Theta4 Theta5 Theta6 X
#> 1 -1.27488798 -0.08720856 -0.27555933 -0.1050969 0.08056352 0.3730307 0
#> 2 0.03275518 1.23030084 0.01103364 -0.6998475 -0.03368952 -0.3191716 1
#> 3 0.89973046 0.29592248 0.75433706 0.7540692 -0.32664011 -1.8705352 2
#> 4 -1.01275214 -0.21884063 -0.11577947 -0.1861527 0.08190306 0.3826651 3
#> 5 -1.59683016 -0.35384016 -1.21221471 -0.9600912 -0.14766654 0.9622527 4
#> 6 1.34208332 1.40219043 1.39816688 0.3623526 -0.29598982 0.0089270 5
#> uniqid d63 class polorient mediatrust trustnom trustallm
#> 1 320387826 3 MiddleClass FarRight Other 0 0
#> 2 394380111 4 WorkingClass Center TrustAll 0 0
#> 3 139929284 1 MiddleClass Center TrustTrad 0 0
#> 4 424648035 3 LowerMiddle Left TrustAll 0 0
#> 5 478547108 3 WorkingClass Center TrustTrad 0 0
#> 6 180669540 3 MiddleClass Center Other 0 0
#> trustwebonly trusttradm qa6a_1 qa6a_2 qa6a_5 qa6a_3 qa6at qa6a_4 d63.1 d15a
#> 1 0 1 1 1 2 1 2 2 3 -1
#> 2 0 1 1 1 2 2 2 1 4 3
#> 3 0 1 1 2 1 3 2 2 1 19
#> 4 0 1 1 1 2 1 3 1 3 10
#> 5 0 1 1 1 1 1 2 1 3 15
#> 6 0 1 2 1 2 2 3 3 3 4
#> d1r2 d1 d11r1 d10 d1r1 d15a_r1 d11 d11r2 d15a_r2
#> 1 1 9 3 1 9 1 59 3 1
#> 2 2 8 2 1 3 1 81 3 2
#> 3 1 6 3 1 2 3 32 2 7
#> 4 3 5 4 2 3 2 95 5 2
#> 5 2 7 3 2 3 2 48 4 7
#> 6 5 10 4 1 9 2 28 4 7
Correlation matrices
One difference between IRT-M and other conventional IRT approaches is
that IRT-M assumes that the latent dimensions may be correlated, as is
true for many theoretical concepts, and it learns the covariance matrix
for the latent dimensions. It can be informative, as an initial
descriptive analysis, to examine the correlation matrix between the
latent dimensions. The function dim_corr() does that. It
takes as input the array of covariance matrices output by IRT-M (either
in Sigma or in Omega), averages their elements
across samples, and computes and returns a correlation matrix of
dimension d x d. The function also takes an optional vector
input, dim_names, comprising names of each dimension. If
that input is present and of length d, it adds corresponding row and
column names to the correlation matrix before returning it.
#Compute correlation matrix of latent dimensions
theta_names <- c("Culture Threat", "Religion Threat", "Economic Threat", "Health Threat", "Support Immigration", "Support EU")
theta_corr <- dim_corr(irt$Sigma, theta_names)
theta_corr
#> Culture Threat Religion Threat Economic Threat
#> Culture Threat 1.0000000 0.18700514 0.5537404
#> Religion Threat 0.1870051 1.00000000 0.1900605
#> Economic Threat 0.5537404 0.19006049 1.0000000
#> Health Threat 0.5269244 0.18736978 0.5504976
#> Support Immigration -0.1937076 -0.06892263 -0.1950458
#> Support EU -0.4689122 -0.18556258 -0.5315167
#> Health Threat Support Immigration Support EU
#> Culture Threat 0.5269244 -0.19370756 -0.4689122
#> Religion Threat 0.1873698 -0.06892263 -0.1855626
#> Economic Threat 0.5504976 -0.19504579 -0.5315167
#> Health Threat 1.0000000 -0.18349189 -0.4931389
#> Support Immigration -0.1834919 1.00000000 0.1773904
#> Support EU -0.4931389 0.17739045 1.0000000
Visualizations
At this point, the analyst will likely want to visualize
distributions over each latent dimension, possibly subset by other
variables. The IRT-M package makes this easy to do with the function
irt_vis(). This function takes as input the number of
latent dimensions, d, and a data frame (Y_out)
constructed above, containing the average thetas plus any additional
variables supplied by the analyst. It returns a ridge plot illustrating
the population distribution over each latent dimension. The function
also takes two optional arguments. The first, sub_name,
takes the name of another variable in Y_out and provides
subplots for each level in the variable sub_name. The
second, out_file, takes a file name; if one is provided,
the function writes the plot to a file with that name and places it in
the working directory. Both default to NULL if not
provided.
The following code presents examples of visualizations for the latent
dimension estimations. We focus on the subgroup distribution
visualizations presented in Morucci et al. 2024. Earlier, we loaded
packages to assist in visualization. irt_vis() makes use of
a melt() call from the dplyr library, which
converts the data from wide to long format. Treating each
theta identifier as a factor makes it easy to use the
ggridges library to visualize ridge plots for each latent
dimension. Before running irt_vis(), we rename the columns
for the latent dimensions of thetas (its first
d columns), since we are plotting the constrained model
only. We first call irt_vis() without using a
sub_name variable to produce aggregate distributions. We do
pass a name for an output file.
library(ggplot2) #version: ggplot2_3.4.4
library(ggridges) #version: ggridges_0.5.6
library(RColorBrewer) #version: RColorBrewer_1.1-3
library(dplyr) #version: dplyr_1.1.4
library(ggrepel) # version: ggrepel_0.9.5
library(reshape2) #version: reshape2_1.4.4
## Rename for interpretability:
## Mapping:
## Theta1-Culture threat
## Theta2-ReligionThreat
## Theta3-Economic Threat
## Theta4-HealthThreat
## Theta5-OutcomeSupportImmigration
## Theta6-OutcomeSupportEU
colnames(thetas)[1:6] <- recode(colnames(thetas)[1:6],
"Theta1" = "Culture Threat",
"Theta2" = "Religion Threat",
"Theta3" = "Economic Threat",
"Theta4" = "Health Threat",
"Theta5" = "Support Immigration",
"Theta6" = "Support EU")
#Save aggregate plot
ggbase <- irt_vis(d = d, T_out = thetas, sub_name = NULL, out_file = "ebirtm-synth.png")
Visualization of estimated Theta distributions,
Eurobarometer Data
Now we turn to more sophisticated visualizations. For example, we may
have a theory in which we expect that the underlying dimensions we find
will be moderated by demographic variables. We can use the metadata that
we retained for the visualizations. The following example code presents
ridge plot visualizations for different levels of respondent-reported
media trust patterns (column 17 in the data):
#Save plot subset by media trust
ggmt <- irt_vis(d = d, T_out = thetas, sub_name = "mediatrust", out_file = "theta-media-synth.png")
Visualization of the distribution of Thetas by
media trust.
Lambda
After estimating and exploring the theta distributions, one might
turn to questions of validation. The IRT-M model produces output that
can help with this task: the lambda array records how
closely associated each item is to each latent dimension, including any
outcome latent dimensions.
IRT-M comes with a utility, get_lambdas(), to make using
the information in that array a bit easier. It takes three inputs: the
lambda array from the IRT model, a vector of item names,
and a vector of latent dimension names. The utility returns a list with
two objects, both of which are data frames. The first,
av_lams, contains averages (across samples) of item
discrimination parameters. The second, high_lams, is a
K x d matrix, with each column an ordered list of the items
(by name) that most strongly load on each latent dimension. The utility
also accepts an optional argument, item_elab, which is a
vector of item descriptions. If that argument is not null (its default),
then the utility appends those descriptions to the item names in its
output.
For our vignette, lambda is an array where the first
dimension is the number of one-hot encoded survey questions, the second
dimension is the number of latent dimensions, and the third dimension is
the number of thinned samples produced by the IRT-M sampler. Our MCodes
matrix also contains substantive notes for each item. The code below
returns useful information about model loadings.
#Extract relevant substantive notes and create data frame with them and item codes
filtered_MCodes <- MCodes[MCodes[[2]] %in% M_matrix$QMap, , drop = FALSE]
M_df <- data.frame(QMap = M_matrix$QMap, sn = filtered_MCodes[[3]])
#Explore item loadings
lambdas <- get_lambdas(irt$lambda, item_names = M_df$QMap, dim_names = theta_names, item_elab = M_df$sn)
average_lambdas <- lambdas[[1]]
highest_lambdas <- lambdas[[2]]
average_lambdas
#> QMap
#> 1 qa1a_1_1: Situation of country in general Very good
#> 2 qa1a_1_2: Situation of country in general rather good
#> 3 qa1a_1_3: Situation of country in general rather bad
#> 4 qa1a_1_4: Situation of country in general Very Bad
#> 5 qa1a_2_1: Situation of national economy in general Very good
#> 6 qa1a_2_2: Situation of national economy in general rather good
#> 7 qa1a_2_3: Situation of national economy in general rather bad
#> 8 qa1a_2_4: Situation of national economy in general Very Bad
#> 9 qa1a_3_1: Situation of EU economy in general Very good
#> 10 qa1a_3_2: Situation of EU economy in general rather good
#> 11 qa1a_3_3: Situation of EU economy in general rather bad
#> 12 qa1a_3_4: Situation of EU economy in general Very Bad
#> 13 qa1a_4_1: Situation of personal job Very good
#> 14 qa1a_4_2: Situation of personal job rather good
#> 15 qa1a_4_3: Situation of personal job rather bad
#> 16 qa1a_4_4: Situation of personal job Very Bad
#> 17 qa1a_5_1: Situation of household finances Very good
#> 18 qa1a_5_2: Situation of household finances rather good
#> 19 qa1a_5_3: Situation of household finances rather bad
#> 20 qa1a_5_4: Situation of household finances Very Bad
#> 21 qa1a_6_1: Situation of national employment Very good
#> 22 qa1a_6_2: Situation of national employment rather good
#> 23 qa1a_6_3: Situation of national employment rather bad
#> 24 qa1a_6_4: Situation of national employment Very Bad
#> 25 qa1a_7_1: Situation of public services Very good
#> 26 qa1a_7_2: Situation of public services rather good
#> 27 qa1a_7_3: Situation of public services rather bad
#> 28 qa1a_7_4: Situation of public services Very Bad
#> 29 qa2a_1_1: life in general better in next 12 months
#> 30 qa2a_1_2: life in general worse in next 12 months
#> 31 qa2a_2_1: national situation better in next 12 months
#> 32 qa2a_2_2: national situation worse in next 12 months
#> 33 qa2a_3_1: national economic situation better in next 12 months
#> 34 qa2a_3_2: national economic situation worse in next 12 months
#> 35 qa2a_4_1: household finances better in next 12 months
#> 36 qa2a_4_2: household finances worse in next 12 months
#> 37 qa2a_5_1: National employment better in next 12 months
#> 38 qa2a_5_2: National employment worse in next 12 months
#> 39 qa2a_6_1: personal job situation better in next 12 months
#> 40 qa2a_6_2: personal job situation worse in next 12 months
#> 41 qa2a_7_1: EU economics better in next 12 months
#> 42 qa2a_7_2: EU economics worse in next 12 months
#> 43 qa3a_1_1: most important national issue: crime. Yes
#> 44 qa3a_2_1: most important national issue: econ situation
#> 45 qa3a_4_1: most important national issue: taxation
#> 46 qa3a_5_1: most important national issue: unemployment
#> 47 qa3a_6_1: most important national issue: terrorism
#> 48 qa3a_8_1: most important national issue: govt debt
#> 49 qa3a_9_1: most important national issue: immigration
#> 50 qa3a_10_1: most important national issue: health
#> 51 qa3a_11_1: most important national issue: education
#> 52 qa3a_12_1: most important national issue: pensions
#> 53 qa3a_13_1: most important national issue: environment/climate
#> 54 qa3a_15_1: most important national issue: cyprus
#> 55 qa4a_1_1: most important personal issue: crime
#> 56 qa4a_2_1: most important personal issue: national economy
#> 57 qa4a_3_1: most important personal issue: inflation
#> 58 qa4a_4_1: most important personal issue: taxation
#> 59 qa4a_5_1: most important personal issue: unemployment
#> 60 qa4a_6_1: most important personal issue: terrorism
#> 61 qa4a_7_1: most important personal issue: housing
#> 62 qa4a_9_1: most important personal issue: immigration
#> 63 qa4a_10_1: most important personal issue: health
#> 64 qa4a_11_1: most important personal issue: education
#> 65 qa4a_12_1: most important personal issue: environment/climate
#> 66 qa4a_13_1: most important personal issue: pensions
#> 67 qa4a_14_1: most important personal issue: working conditions
#> 68 qa4a_15_1: most important personal issue: living conditions
#> 69 qa4a_16_1: most important personal issue: cyprus
#> 70 qa6b_1_1: trust parties
#> 71 qa6b_1_2: do not trust parties
#> 72 qa6b_3_1: trust police
#> 73 qa6b_3_2: do not trust police
#> 74 qa6b_8_1: trust national gvt
#> 75 qa6b_8_2: do not trust national gvt
#> 76 qa6b_10_1: trust EU
#> 77 qa6b_10_2: do not trust EU
#> 78 qa8_1_1: trust Euro parliament
#> 79 qa8_1_2: not trust Euro parliament
#> 80 qa8_2_1: trust Euro Commission
#> 81 qa8_2_2: not trust Euro Commission
#> 82 qa10_1_1: Very Satisfied with National covid response
#> 83 qa10_1_2: Fairly satisfied with National covid response
#> 84 qa10_1_3: Not very satisfied with National covid response
#> 85 qa10_1_4: Not at all satisfied with National covid response
#> 86 qa10_2_1: Very Satisfied with local covid response
#> 87 qa10_2_2: Fairly satisfied with local covid response
#> 88 qa10_2_3: Not very satisfied with local covid response
#> 89 qa10_2_4: Not at all satisfied with local covid response
#> 90 qa10_3_1: Very Satisfied with EU covid response
#> 91 qa10_3_2: Fairly satisfied with EU covid response
#> 92 qa10_3_3: Not very satisfied with EU covid response
#> 93 qa10_3_4: Not at all satisfied with EU covid response
#> 94 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 95 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 96 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 97 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 98 qa13_2_1: Totally agree covid had serious national economic consequences
#> 99 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 100 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 101 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 102 qa14_1: national economy already recovered from covid
#> 103 qa14_2: national economy will recover from covid in 2021
#> 104 qa14_3: national economy will recover from covid in 2022
#> 105 qa14_4: national economy will recover from covid in 2023+
#> 106 qa14_5: national economy will never recover from covid
#> 107 qa16_1: covid restrictions very justified
#> 108 qa16_2: covid restrictions fairly justified
#> 109 qa16_3: covid restrictions not very justified
#> 110 qa16_4: covid restrictions not at all justified
#> 111 qa16_4: covid restrictions endanger mental physical health
#> 112 qa18_1_1: totally agree: vaccines developed too fast
#> 113 qa18_1_2: tend to agree: vaccines developed too fast
#> 114 qa18_1_3: tend to disagree: vaccines developed too fast
#> 115 qa18_1_4: totally disagree: vaccines developed too fast
#> 116 qa18_2_1: totally agree: vaccines could have side effects
#> 117 qa18_2_2: tend to agree: vaccines could have side effects
#> 118 qa18_2_3: tend to disagree: vaccines could have side effects
#> 119 qa18_2_4: totally disagree: vaccines could have side effects
#> 120 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 121 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 122 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 123 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 124 qa19_1: Want vax asap
#> 125 qa19_2: want vvax in 2021
#> 126 qa19_4: want vax never
#> 127 sd18a_1: very satisfied with democracy in country
#> 128 sd18a_2: fairly satisifed with democracy in country
#> 129 sd18a_3: not very satisfied with democracy in country
#> 130 sd18a_4: not at all satisfied with democracy in country
#> 131 qc1a_3_1: Very attached: EU
#> 132 qc1a_3_2: Fairly attached: EU
#> 133 qc1a_3_3: Not very attached: EU
#> 134 qc1a_3_4: Not at all attached: EU
#> 135 qd8_1_1: Totally agree: often see fake news
#> 136 qd8_1_2: Tend to agree: often see fake news
#> 137 qd8_3_1: Totally agree: fake news a problem in country
#> 138 qd8_3_2: Tend to agree: fake news a problem in country
#> 139 qd8_3_3: Tend to disagree: fake news a problem in country
#> 140 qd8_3_4: Totally disagree: fake news a problem in country
#> 141 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 142 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 143 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 144 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 145 d60_1: difficulties to pay bills: most of time
#> 146 d60_2: difficulties to pay bills: time to time
#> 147 d60_3: difficulties to pay bills: Almost never
#> 148 d70_1: Very satisfied with life
#> 149 d78_1: EU association very positive
#> 150 d78_2: EU association fairly positive
#> 151 d78_4: EU association fairly negative
#> 152 d78_5: EU association very negative
#> 153 d73_2_1: EU going in right direction
#> Culture Threat Religion Threat Economic Threat Health Threat
#> 1 -0.228944730 0.0000000 0.000000000 0.000000000
#> 2 -0.371209167 0.0000000 0.000000000 0.000000000
#> 3 0.320488904 0.0000000 0.000000000 0.000000000
#> 4 0.377164299 0.0000000 0.000000000 0.000000000
#> 5 0.000000000 0.0000000 -0.364171034 0.000000000
#> 6 0.000000000 0.0000000 -0.654325167 0.000000000
#> 7 0.000000000 0.0000000 0.114588329 0.000000000
#> 8 0.000000000 0.0000000 0.725127216 0.000000000
#> 9 0.000000000 0.0000000 -0.276280612 0.000000000
#> 10 0.000000000 0.0000000 -0.588749786 0.000000000
#> 11 0.000000000 0.0000000 0.366726379 0.000000000
#> 12 0.000000000 0.0000000 0.515893203 0.000000000
#> 13 0.000000000 0.0000000 -0.536110811 0.000000000
#> 14 0.000000000 0.0000000 -0.342533845 0.000000000
#> 15 0.000000000 0.0000000 0.504389985 0.000000000
#> 16 0.000000000 0.0000000 0.480822888 0.000000000
#> 17 0.000000000 0.0000000 -0.375783512 0.000000000
#> 18 0.000000000 0.0000000 -0.303346232 0.000000000
#> 19 0.000000000 0.0000000 0.524404075 0.000000000
#> 20 0.000000000 0.0000000 0.611536870 0.000000000
#> 21 0.000000000 0.0000000 -0.405084752 0.000000000
#> 22 0.000000000 0.0000000 -0.520991378 0.000000000
#> 23 0.000000000 0.0000000 0.147716819 0.000000000
#> 24 0.000000000 0.0000000 0.579775291 0.000000000
#> 25 -0.255483126 0.0000000 0.000000000 0.000000000
#> 26 -0.359667784 0.0000000 0.000000000 0.000000000
#> 27 0.337908787 0.0000000 0.000000000 0.000000000
#> 28 0.510753931 0.0000000 0.000000000 0.000000000
#> 29 -0.270856896 0.0000000 0.000000000 0.000000000
#> 30 0.399207266 0.0000000 0.000000000 0.000000000
#> 31 -0.372183929 0.0000000 0.000000000 0.000000000
#> 32 0.344537128 0.0000000 0.000000000 0.000000000
#> 33 0.000000000 0.0000000 -0.757529285 0.000000000
#> 34 0.000000000 0.0000000 0.475317490 0.000000000
#> 35 0.000000000 0.0000000 -0.365528308 0.000000000
#> 36 0.000000000 0.0000000 0.495667110 0.000000000
#> 37 0.000000000 0.0000000 -0.761276438 0.000000000
#> 38 0.000000000 0.0000000 0.559725087 0.000000000
#> 39 0.000000000 0.0000000 -0.299602128 0.000000000
#> 40 0.000000000 0.0000000 0.509025844 0.000000000
#> 41 0.000000000 0.0000000 -0.561902479 0.000000000
#> 42 0.000000000 0.0000000 0.360377248 0.000000000
#> 43 0.139090368 0.0000000 0.199958081 0.000000000
#> 44 0.000000000 0.0000000 0.206125697 0.000000000
#> 45 0.000000000 0.0000000 0.214761562 0.000000000
#> 46 0.000000000 0.0000000 0.266479259 0.000000000
#> 47 0.000000000 0.7492480 0.000000000 0.000000000
#> 48 0.000000000 0.0000000 0.018672025 0.000000000
#> 49 0.000000000 0.0000000 0.000000000 0.000000000
#> 50 0.000000000 0.0000000 0.000000000 0.335011499
#> 51 0.117105052 0.0000000 0.000000000 0.000000000
#> 52 0.011723167 0.0000000 0.010585552 0.000000000
#> 53 0.229120523 0.0000000 0.333693983 0.000000000
#> 54 0.387404624 0.0000000 0.000000000 0.000000000
#> 55 0.254596571 0.0000000 0.261027094 0.000000000
#> 56 0.000000000 0.0000000 0.242880255 0.000000000
#> 57 0.000000000 0.0000000 0.242264645 0.000000000
#> 58 0.000000000 0.0000000 0.316976600 0.000000000
#> 59 0.000000000 0.0000000 0.501079630 0.000000000
#> 60 0.000000000 0.9161896 0.000000000 0.000000000
#> 61 0.000000000 0.0000000 0.214863830 0.000000000
#> 62 0.000000000 0.0000000 0.000000000 0.000000000
#> 63 0.000000000 0.0000000 0.000000000 0.271213229
#> 64 0.142264798 0.0000000 0.000000000 0.000000000
#> 65 0.181479977 0.0000000 0.204886512 0.000000000
#> 66 0.007170695 0.0000000 0.005303104 0.000000000
#> 67 0.243669879 0.0000000 0.246999496 0.000000000
#> 68 0.117937377 0.0000000 0.104638767 0.000000000
#> 69 0.194854631 0.0000000 0.000000000 0.000000000
#> 70 -6.924595641 0.0000000 0.000000000 0.000000000
#> 71 9.487307759 0.0000000 0.000000000 0.000000000
#> 72 -0.603411288 0.0000000 0.000000000 0.000000000
#> 73 0.594061527 0.0000000 0.000000000 0.000000000
#> 74 -0.522796046 0.0000000 0.000000000 0.000000000
#> 75 0.453940547 0.0000000 0.000000000 0.000000000
#> 76 -0.319978751 0.0000000 0.000000000 0.000000000
#> 77 0.223761726 0.0000000 0.000000000 0.000000000
#> 78 -0.350193413 0.0000000 0.000000000 0.000000000
#> 79 0.226590559 0.0000000 0.000000000 0.000000000
#> 80 -0.291348247 0.0000000 0.000000000 0.000000000
#> 81 0.200416730 0.0000000 0.000000000 0.000000000
#> 82 0.000000000 0.0000000 0.000000000 -0.415625204
#> 83 0.000000000 0.0000000 0.000000000 -0.544207851
#> 84 0.000000000 0.0000000 0.000000000 0.159999946
#> 85 0.000000000 0.0000000 0.000000000 0.895118348
#> 86 0.000000000 0.0000000 0.000000000 -0.410529658
#> 87 0.000000000 0.0000000 0.000000000 -0.590631877
#> 88 0.000000000 0.0000000 0.000000000 0.536814297
#> 89 0.000000000 0.0000000 0.000000000 0.443957893
#> 90 0.000000000 0.0000000 0.000000000 -0.114204844
#> 91 0.000000000 0.0000000 0.000000000 -0.331446983
#> 92 0.000000000 0.0000000 0.000000000 0.079565800
#> 93 0.000000000 0.0000000 0.000000000 0.511138199
#> 94 0.000000000 0.0000000 0.544694314 0.000000000
#> 95 0.000000000 0.0000000 0.334420834 0.000000000
#> 96 0.000000000 0.0000000 -0.197718201 0.000000000
#> 97 0.000000000 0.0000000 -0.637541750 0.000000000
#> 98 0.000000000 0.0000000 0.519738998 0.000000000
#> 99 0.000000000 0.0000000 0.002005512 0.000000000
#> 100 0.000000000 0.0000000 -0.339820319 0.000000000
#> 101 0.000000000 0.0000000 -0.500432336 0.000000000
#> 102 0.000000000 0.0000000 -0.495854356 0.000000000
#> 103 0.000000000 0.0000000 -0.360765595 0.000000000
#> 104 0.000000000 0.0000000 0.132159810 0.000000000
#> 105 0.000000000 0.0000000 0.006356261 0.000000000
#> 106 0.000000000 0.0000000 0.670712546 0.000000000
#> 107 0.000000000 0.0000000 0.000000000 0.004217907
#> 108 0.000000000 0.0000000 0.000000000 0.008503514
#> 109 0.000000000 0.0000000 0.000000000 -0.005200686
#> 110 0.000000000 0.0000000 0.000000000 -0.005192154
#> 111 0.000000000 0.0000000 0.000000000 0.506368587
#> 112 0.000000000 0.0000000 0.000000000 0.671451343
#> 113 0.000000000 0.0000000 0.000000000 0.055616375
#> 114 0.000000000 0.0000000 0.000000000 -0.406874470
#> 115 0.000000000 0.0000000 0.000000000 -0.512507777
#> 116 0.000000000 0.0000000 0.000000000 0.981806988
#> 117 0.000000000 0.0000000 0.000000000 0.031414154
#> 118 0.000000000 0.0000000 0.000000000 -0.828599913
#> 119 0.000000000 0.0000000 0.000000000 -0.269457452
#> 120 0.000000000 0.0000000 0.000000000 -0.370100594
#> 121 0.000000000 0.0000000 0.000000000 -0.174524574
#> 122 0.000000000 0.0000000 0.000000000 0.196238273
#> 123 0.000000000 0.0000000 0.000000000 0.576643628
#> 124 0.000000000 0.0000000 0.000000000 0.058249234
#> 125 0.000000000 0.0000000 0.000000000 0.128090246
#> 126 0.000000000 0.0000000 0.000000000 -0.008116572
#> 127 -0.273373857 0.0000000 0.000000000 0.000000000
#> 128 -0.256101596 0.0000000 0.000000000 0.000000000
#> 129 0.251877812 0.0000000 0.000000000 0.000000000
#> 130 0.354374456 0.0000000 0.000000000 0.000000000
#> 131 0.000000000 0.0000000 0.000000000 0.000000000
#> 132 0.000000000 0.0000000 0.000000000 0.000000000
#> 133 0.000000000 0.0000000 0.000000000 0.000000000
#> 134 0.000000000 0.0000000 0.000000000 0.000000000
#> 135 0.244491958 0.0000000 0.000000000 0.000000000
#> 136 0.011067959 0.0000000 0.000000000 0.000000000
#> 137 0.187673819 0.0000000 0.000000000 0.000000000
#> 138 0.167370317 0.0000000 0.000000000 0.000000000
#> 139 -0.286790698 0.0000000 0.000000000 0.000000000
#> 140 -0.306619620 0.0000000 0.000000000 0.000000000
#> 141 0.162083401 0.0000000 0.000000000 0.000000000
#> 142 0.040062714 0.0000000 0.000000000 0.000000000
#> 143 -0.285928518 0.0000000 0.000000000 0.000000000
#> 144 -0.463657266 0.0000000 0.000000000 0.000000000
#> 145 0.000000000 0.0000000 0.732650442 0.000000000
#> 146 0.000000000 0.0000000 0.470942594 0.000000000
#> 147 0.000000000 0.0000000 -0.960920247 0.000000000
#> 148 -0.323547803 0.0000000 0.000000000 0.000000000
#> 149 0.000000000 0.0000000 0.000000000 0.000000000
#> 150 0.000000000 0.0000000 0.000000000 0.000000000
#> 151 0.000000000 0.0000000 0.000000000 0.000000000
#> 152 0.000000000 0.0000000 0.000000000 0.000000000
#> 153 0.000000000 0.0000000 0.000000000 0.000000000
#> Support Immigration Support EU
#> 1 0.0000000 0.0000000
#> 2 0.0000000 0.0000000
#> 3 0.0000000 0.0000000
#> 4 0.0000000 0.0000000
#> 5 0.0000000 0.0000000
#> 6 0.0000000 0.0000000
#> 7 0.0000000 0.0000000
#> 8 0.0000000 0.0000000
#> 9 0.0000000 0.0000000
#> 10 0.0000000 0.0000000
#> 11 0.0000000 0.0000000
#> 12 0.0000000 0.0000000
#> 13 0.0000000 0.0000000
#> 14 0.0000000 0.0000000
#> 15 0.0000000 0.0000000
#> 16 0.0000000 0.0000000
#> 17 0.0000000 0.0000000
#> 18 0.0000000 0.0000000
#> 19 0.0000000 0.0000000
#> 20 0.0000000 0.0000000
#> 21 0.0000000 0.0000000
#> 22 0.0000000 0.0000000
#> 23 0.0000000 0.0000000
#> 24 0.0000000 0.0000000
#> 25 0.0000000 0.0000000
#> 26 0.0000000 0.0000000
#> 27 0.0000000 0.0000000
#> 28 0.0000000 0.0000000
#> 29 0.0000000 0.0000000
#> 30 0.0000000 0.0000000
#> 31 0.0000000 0.0000000
#> 32 0.0000000 0.0000000
#> 33 0.0000000 0.0000000
#> 34 0.0000000 0.0000000
#> 35 0.0000000 0.0000000
#> 36 0.0000000 0.0000000
#> 37 0.0000000 0.0000000
#> 38 0.0000000 0.0000000
#> 39 0.0000000 0.0000000
#> 40 0.0000000 0.0000000
#> 41 0.0000000 0.0000000
#> 42 0.0000000 0.0000000
#> 43 0.0000000 0.0000000
#> 44 0.0000000 0.0000000
#> 45 0.0000000 0.0000000
#> 46 0.0000000 0.0000000
#> 47 0.0000000 0.0000000
#> 48 0.0000000 0.0000000
#> 49 0.6764481 0.0000000
#> 50 0.0000000 0.0000000
#> 51 0.0000000 0.0000000
#> 52 0.0000000 0.0000000
#> 53 0.0000000 0.0000000
#> 54 0.0000000 0.0000000
#> 55 0.0000000 0.0000000
#> 56 0.0000000 0.0000000
#> 57 0.0000000 0.0000000
#> 58 0.0000000 0.0000000
#> 59 0.0000000 0.0000000
#> 60 0.0000000 0.0000000
#> 61 0.0000000 0.0000000
#> 62 -0.2657453 0.0000000
#> 63 0.0000000 0.0000000
#> 64 0.0000000 0.0000000
#> 65 0.0000000 0.0000000
#> 66 0.0000000 0.0000000
#> 67 0.0000000 0.0000000
#> 68 0.0000000 0.0000000
#> 69 0.0000000 0.0000000
#> 70 0.0000000 0.0000000
#> 71 0.0000000 0.0000000
#> 72 0.0000000 0.0000000
#> 73 0.0000000 0.0000000
#> 74 0.0000000 0.0000000
#> 75 0.0000000 0.0000000
#> 76 0.0000000 2.6920619
#> 77 0.0000000 -2.0869861
#> 78 0.0000000 0.7931308
#> 79 0.0000000 -0.5424695
#> 80 0.0000000 0.8009997
#> 81 0.0000000 -0.6004199
#> 82 0.0000000 0.0000000
#> 83 0.0000000 0.0000000
#> 84 0.0000000 0.0000000
#> 85 0.0000000 0.0000000
#> 86 0.0000000 0.0000000
#> 87 0.0000000 0.0000000
#> 88 0.0000000 0.0000000
#> 89 0.0000000 0.0000000
#> 90 0.0000000 0.1351749
#> 91 0.0000000 0.3156860
#> 92 0.0000000 -0.1982020
#> 93 0.0000000 -0.3432446
#> 94 0.0000000 0.0000000
#> 95 0.0000000 0.0000000
#> 96 0.0000000 0.0000000
#> 97 0.0000000 0.0000000
#> 98 0.0000000 0.0000000
#> 99 0.0000000 0.0000000
#> 100 0.0000000 0.0000000
#> 101 0.0000000 0.0000000
#> 102 0.0000000 0.0000000
#> 103 0.0000000 0.0000000
#> 104 0.0000000 0.0000000
#> 105 0.0000000 0.0000000
#> 106 0.0000000 0.0000000
#> 107 0.0000000 0.0000000
#> 108 0.0000000 0.0000000
#> 109 0.0000000 0.0000000
#> 110 0.0000000 0.0000000
#> 111 0.0000000 0.0000000
#> 112 0.0000000 0.0000000
#> 113 0.0000000 0.0000000
#> 114 0.0000000 0.0000000
#> 115 0.0000000 0.0000000
#> 116 0.0000000 0.0000000
#> 117 0.0000000 0.0000000
#> 118 0.0000000 0.0000000
#> 119 0.0000000 0.0000000
#> 120 0.0000000 0.0000000
#> 121 0.0000000 0.0000000
#> 122 0.0000000 0.0000000
#> 123 0.0000000 0.0000000
#> 124 0.0000000 0.0000000
#> 125 0.0000000 0.0000000
#> 126 0.0000000 0.0000000
#> 127 0.0000000 0.0000000
#> 128 0.0000000 0.0000000
#> 129 0.0000000 0.0000000
#> 130 0.0000000 0.0000000
#> 131 0.0000000 0.3344426
#> 132 0.0000000 0.3216718
#> 133 0.0000000 -0.1047849
#> 134 0.0000000 -0.7239508
#> 135 0.0000000 0.0000000
#> 136 0.0000000 0.0000000
#> 137 0.0000000 0.0000000
#> 138 0.0000000 0.0000000
#> 139 0.0000000 0.0000000
#> 140 0.0000000 0.0000000
#> 141 0.0000000 0.0000000
#> 142 0.0000000 0.0000000
#> 143 0.0000000 0.0000000
#> 144 0.0000000 0.0000000
#> 145 0.0000000 0.0000000
#> 146 0.0000000 0.0000000
#> 147 0.0000000 0.0000000
#> 148 0.0000000 0.0000000
#> 149 0.0000000 0.2420676
#> 150 0.0000000 0.5956120
#> 151 0.0000000 -0.3006924
#> 152 0.0000000 -0.4197890
#> 153 0.0000000 0.2178868
highest_lambdas
#> Culture Threat
#> 1 qa6b_1_2: do not trust parties
#> 2 qa6b_1_1: trust parties
#> 3 qa6b_3_1: trust police
#> 4 qa6b_3_2: do not trust police
#> 5 qa6b_8_1: trust national gvt
#> 6 qa1a_7_4: Situation of public services Very Bad
#> 7 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 8 qa6b_8_2: do not trust national gvt
#> 9 qa2a_1_2: life in general worse in next 12 months
#> 10 qa3a_15_1: most important national issue: cyprus
#> 11 qa1a_1_4: Situation of country in general Very Bad
#> 12 qa2a_2_1: national situation better in next 12 months
#> 13 qa1a_1_2: Situation of country in general rather good
#> 14 qa1a_7_2: Situation of public services rather good
#> 15 sd18a_4: not at all satisfied with democracy in country
#> 16 qa8_1_1: trust Euro parliament
#> 17 qa2a_2_2: national situation worse in next 12 months
#> 18 qa1a_7_3: Situation of public services rather bad
#> 19 d70_1: Very satisfied with life
#> 20 qa1a_1_3: Situation of country in general rather bad
#> 21 qa6b_10_1: trust EU
#> 22 qd8_3_4: Totally disagree: fake news a problem in country
#> 23 qa8_2_1: trust Euro Commission
#> 24 qd8_3_3: Tend to disagree: fake news a problem in country
#> 25 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 26 sd18a_1: very satisfied with democracy in country
#> 27 qa2a_1_1: life in general better in next 12 months
#> 28 sd18a_2: fairly satisifed with democracy in country
#> 29 qa1a_7_1: Situation of public services Very good
#> 30 qa4a_1_1: most important personal issue: crime
#> 31 sd18a_3: not very satisfied with democracy in country
#> 32 qd8_1_1: Totally agree: often see fake news
#> 33 qa4a_14_1: most important personal issue: working conditions
#> 34 qa3a_13_1: most important national issue: environment/climate
#> 35 qa1a_1_1: Situation of country in general Very good
#> 36 qa8_1_2: not trust Euro parliament
#> 37 qa6b_10_2: do not trust EU
#> 38 qa8_2_2: not trust Euro Commission
#> 39 qa4a_16_1: most important personal issue: cyprus
#> 40 qd8_3_1: Totally agree: fake news a problem in country
#> 41 qa4a_12_1: most important personal issue: environment/climate
#> 42 qd8_3_2: Tend to agree: fake news a problem in country
#> 43 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 44 qa4a_11_1: most important personal issue: education
#> 45 qa3a_1_1: most important national issue: crime. Yes
#> 46 qa4a_15_1: most important personal issue: living conditions
#> 47 qa3a_11_1: most important national issue: education
#> 48 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 49 qa3a_12_1: most important national issue: pensions
#> 50 qd8_1_2: Tend to agree: often see fake news
#> 51 qa4a_13_1: most important personal issue: pensions
#> 52 qa1a_2_1: Situation of national economy in general Very good
#> 53 qa1a_2_2: Situation of national economy in general rather good
#> 54 qa1a_2_3: Situation of national economy in general rather bad
#> 55 qa1a_2_4: Situation of national economy in general Very Bad
#> 56 qa1a_3_1: Situation of EU economy in general Very good
#> 57 qa1a_3_2: Situation of EU economy in general rather good
#> 58 qa1a_3_3: Situation of EU economy in general rather bad
#> 59 qa1a_3_4: Situation of EU economy in general Very Bad
#> 60 qa1a_4_1: Situation of personal job Very good
#> 61 qa1a_4_2: Situation of personal job rather good
#> 62 qa1a_4_3: Situation of personal job rather bad
#> 63 qa1a_4_4: Situation of personal job Very Bad
#> 64 qa1a_5_1: Situation of household finances Very good
#> 65 qa1a_5_2: Situation of household finances rather good
#> 66 qa1a_5_3: Situation of household finances rather bad
#> 67 qa1a_5_4: Situation of household finances Very Bad
#> 68 qa1a_6_1: Situation of national employment Very good
#> 69 qa1a_6_2: Situation of national employment rather good
#> 70 qa1a_6_3: Situation of national employment rather bad
#> 71 qa1a_6_4: Situation of national employment Very Bad
#> 72 qa2a_3_1: national economic situation better in next 12 months
#> 73 qa2a_3_2: national economic situation worse in next 12 months
#> 74 qa2a_4_1: household finances better in next 12 months
#> 75 qa2a_4_2: household finances worse in next 12 months
#> 76 qa2a_5_1: National employment better in next 12 months
#> 77 qa2a_5_2: National employment worse in next 12 months
#> 78 qa2a_6_1: personal job situation better in next 12 months
#> 79 qa2a_6_2: personal job situation worse in next 12 months
#> 80 qa2a_7_1: EU economics better in next 12 months
#> 81 qa2a_7_2: EU economics worse in next 12 months
#> 82 qa3a_2_1: most important national issue: econ situation
#> 83 qa3a_4_1: most important national issue: taxation
#> 84 qa3a_5_1: most important national issue: unemployment
#> 85 qa3a_6_1: most important national issue: terrorism
#> 86 qa3a_8_1: most important national issue: govt debt
#> 87 qa3a_9_1: most important national issue: immigration
#> 88 qa3a_10_1: most important national issue: health
#> 89 qa4a_2_1: most important personal issue: national economy
#> 90 qa4a_3_1: most important personal issue: inflation
#> 91 qa4a_4_1: most important personal issue: taxation
#> 92 qa4a_5_1: most important personal issue: unemployment
#> 93 qa4a_6_1: most important personal issue: terrorism
#> 94 qa4a_7_1: most important personal issue: housing
#> 95 qa4a_9_1: most important personal issue: immigration
#> 96 qa4a_10_1: most important personal issue: health
#> 97 qa10_1_1: Very Satisfied with National covid response
#> 98 qa10_1_2: Fairly satisfied with National covid response
#> 99 qa10_1_3: Not very satisfied with National covid response
#> 100 qa10_1_4: Not at all satisfied with National covid response
#> 101 qa10_2_1: Very Satisfied with local covid response
#> 102 qa10_2_2: Fairly satisfied with local covid response
#> 103 qa10_2_3: Not very satisfied with local covid response
#> 104 qa10_2_4: Not at all satisfied with local covid response
#> 105 qa10_3_1: Very Satisfied with EU covid response
#> 106 qa10_3_2: Fairly satisfied with EU covid response
#> 107 qa10_3_3: Not very satisfied with EU covid response
#> 108 qa10_3_4: Not at all satisfied with EU covid response
#> 109 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 110 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 111 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 112 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 113 qa13_2_1: Totally agree covid had serious national economic consequences
#> 114 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 115 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 116 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 117 qa14_1: national economy already recovered from covid
#> 118 qa14_2: national economy will recover from covid in 2021
#> 119 qa14_3: national economy will recover from covid in 2022
#> 120 qa14_4: national economy will recover from covid in 2023+
#> 121 qa14_5: national economy will never recover from covid
#> 122 qa16_1: covid restrictions very justified
#> 123 qa16_2: covid restrictions fairly justified
#> 124 qa16_3: covid restrictions not very justified
#> 125 qa16_4: covid restrictions not at all justified
#> 126 qa16_4: covid restrictions endanger mental physical health
#> 127 qa18_1_1: totally agree: vaccines developed too fast
#> 128 qa18_1_2: tend to agree: vaccines developed too fast
#> 129 qa18_1_3: tend to disagree: vaccines developed too fast
#> 130 qa18_1_4: totally disagree: vaccines developed too fast
#> 131 qa18_2_1: totally agree: vaccines could have side effects
#> 132 qa18_2_2: tend to agree: vaccines could have side effects
#> 133 qa18_2_3: tend to disagree: vaccines could have side effects
#> 134 qa18_2_4: totally disagree: vaccines could have side effects
#> 135 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 136 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 137 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 138 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 139 qa19_1: Want vax asap
#> 140 qa19_2: want vvax in 2021
#> 141 qa19_4: want vax never
#> 142 qc1a_3_1: Very attached: EU
#> 143 qc1a_3_2: Fairly attached: EU
#> 144 qc1a_3_3: Not very attached: EU
#> 145 qc1a_3_4: Not at all attached: EU
#> 146 d60_1: difficulties to pay bills: most of time
#> 147 d60_2: difficulties to pay bills: time to time
#> 148 d60_3: difficulties to pay bills: Almost never
#> 149 d78_1: EU association very positive
#> 150 d78_2: EU association fairly positive
#> 151 d78_4: EU association fairly negative
#> 152 d78_5: EU association very negative
#> 153 d73_2_1: EU going in right direction
#> Religion Threat
#> 1 qa4a_6_1: most important personal issue: terrorism
#> 2 qa3a_6_1: most important national issue: terrorism
#> 3 qa1a_1_1: Situation of country in general Very good
#> 4 qa1a_1_2: Situation of country in general rather good
#> 5 qa1a_1_3: Situation of country in general rather bad
#> 6 qa1a_1_4: Situation of country in general Very Bad
#> 7 qa1a_2_1: Situation of national economy in general Very good
#> 8 qa1a_2_2: Situation of national economy in general rather good
#> 9 qa1a_2_3: Situation of national economy in general rather bad
#> 10 qa1a_2_4: Situation of national economy in general Very Bad
#> 11 qa1a_3_1: Situation of EU economy in general Very good
#> 12 qa1a_3_2: Situation of EU economy in general rather good
#> 13 qa1a_3_3: Situation of EU economy in general rather bad
#> 14 qa1a_3_4: Situation of EU economy in general Very Bad
#> 15 qa1a_4_1: Situation of personal job Very good
#> 16 qa1a_4_2: Situation of personal job rather good
#> 17 qa1a_4_3: Situation of personal job rather bad
#> 18 qa1a_4_4: Situation of personal job Very Bad
#> 19 qa1a_5_1: Situation of household finances Very good
#> 20 qa1a_5_2: Situation of household finances rather good
#> 21 qa1a_5_3: Situation of household finances rather bad
#> 22 qa1a_5_4: Situation of household finances Very Bad
#> 23 qa1a_6_1: Situation of national employment Very good
#> 24 qa1a_6_2: Situation of national employment rather good
#> 25 qa1a_6_3: Situation of national employment rather bad
#> 26 qa1a_6_4: Situation of national employment Very Bad
#> 27 qa1a_7_1: Situation of public services Very good
#> 28 qa1a_7_2: Situation of public services rather good
#> 29 qa1a_7_3: Situation of public services rather bad
#> 30 qa1a_7_4: Situation of public services Very Bad
#> 31 qa2a_1_1: life in general better in next 12 months
#> 32 qa2a_1_2: life in general worse in next 12 months
#> 33 qa2a_2_1: national situation better in next 12 months
#> 34 qa2a_2_2: national situation worse in next 12 months
#> 35 qa2a_3_1: national economic situation better in next 12 months
#> 36 qa2a_3_2: national economic situation worse in next 12 months
#> 37 qa2a_4_1: household finances better in next 12 months
#> 38 qa2a_4_2: household finances worse in next 12 months
#> 39 qa2a_5_1: National employment better in next 12 months
#> 40 qa2a_5_2: National employment worse in next 12 months
#> 41 qa2a_6_1: personal job situation better in next 12 months
#> 42 qa2a_6_2: personal job situation worse in next 12 months
#> 43 qa2a_7_1: EU economics better in next 12 months
#> 44 qa2a_7_2: EU economics worse in next 12 months
#> 45 qa3a_1_1: most important national issue: crime. Yes
#> 46 qa3a_2_1: most important national issue: econ situation
#> 47 qa3a_4_1: most important national issue: taxation
#> 48 qa3a_5_1: most important national issue: unemployment
#> 49 qa3a_8_1: most important national issue: govt debt
#> 50 qa3a_9_1: most important national issue: immigration
#> 51 qa3a_10_1: most important national issue: health
#> 52 qa3a_11_1: most important national issue: education
#> 53 qa3a_12_1: most important national issue: pensions
#> 54 qa3a_13_1: most important national issue: environment/climate
#> 55 qa3a_15_1: most important national issue: cyprus
#> 56 qa4a_1_1: most important personal issue: crime
#> 57 qa4a_2_1: most important personal issue: national economy
#> 58 qa4a_3_1: most important personal issue: inflation
#> 59 qa4a_4_1: most important personal issue: taxation
#> 60 qa4a_5_1: most important personal issue: unemployment
#> 61 qa4a_7_1: most important personal issue: housing
#> 62 qa4a_9_1: most important personal issue: immigration
#> 63 qa4a_10_1: most important personal issue: health
#> 64 qa4a_11_1: most important personal issue: education
#> 65 qa4a_12_1: most important personal issue: environment/climate
#> 66 qa4a_13_1: most important personal issue: pensions
#> 67 qa4a_14_1: most important personal issue: working conditions
#> 68 qa4a_15_1: most important personal issue: living conditions
#> 69 qa4a_16_1: most important personal issue: cyprus
#> 70 qa6b_1_1: trust parties
#> 71 qa6b_1_2: do not trust parties
#> 72 qa6b_3_1: trust police
#> 73 qa6b_3_2: do not trust police
#> 74 qa6b_8_1: trust national gvt
#> 75 qa6b_8_2: do not trust national gvt
#> 76 qa6b_10_1: trust EU
#> 77 qa6b_10_2: do not trust EU
#> 78 qa8_1_1: trust Euro parliament
#> 79 qa8_1_2: not trust Euro parliament
#> 80 qa8_2_1: trust Euro Commission
#> 81 qa8_2_2: not trust Euro Commission
#> 82 qa10_1_1: Very Satisfied with National covid response
#> 83 qa10_1_2: Fairly satisfied with National covid response
#> 84 qa10_1_3: Not very satisfied with National covid response
#> 85 qa10_1_4: Not at all satisfied with National covid response
#> 86 qa10_2_1: Very Satisfied with local covid response
#> 87 qa10_2_2: Fairly satisfied with local covid response
#> 88 qa10_2_3: Not very satisfied with local covid response
#> 89 qa10_2_4: Not at all satisfied with local covid response
#> 90 qa10_3_1: Very Satisfied with EU covid response
#> 91 qa10_3_2: Fairly satisfied with EU covid response
#> 92 qa10_3_3: Not very satisfied with EU covid response
#> 93 qa10_3_4: Not at all satisfied with EU covid response
#> 94 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 95 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 96 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 97 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 98 qa13_2_1: Totally agree covid had serious national economic consequences
#> 99 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 100 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 101 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 102 qa14_1: national economy already recovered from covid
#> 103 qa14_2: national economy will recover from covid in 2021
#> 104 qa14_3: national economy will recover from covid in 2022
#> 105 qa14_4: national economy will recover from covid in 2023+
#> 106 qa14_5: national economy will never recover from covid
#> 107 qa16_1: covid restrictions very justified
#> 108 qa16_2: covid restrictions fairly justified
#> 109 qa16_3: covid restrictions not very justified
#> 110 qa16_4: covid restrictions not at all justified
#> 111 qa16_4: covid restrictions endanger mental physical health
#> 112 qa18_1_1: totally agree: vaccines developed too fast
#> 113 qa18_1_2: tend to agree: vaccines developed too fast
#> 114 qa18_1_3: tend to disagree: vaccines developed too fast
#> 115 qa18_1_4: totally disagree: vaccines developed too fast
#> 116 qa18_2_1: totally agree: vaccines could have side effects
#> 117 qa18_2_2: tend to agree: vaccines could have side effects
#> 118 qa18_2_3: tend to disagree: vaccines could have side effects
#> 119 qa18_2_4: totally disagree: vaccines could have side effects
#> 120 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 121 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 122 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 123 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 124 qa19_1: Want vax asap
#> 125 qa19_2: want vvax in 2021
#> 126 qa19_4: want vax never
#> 127 sd18a_1: very satisfied with democracy in country
#> 128 sd18a_2: fairly satisifed with democracy in country
#> 129 sd18a_3: not very satisfied with democracy in country
#> 130 sd18a_4: not at all satisfied with democracy in country
#> 131 qc1a_3_1: Very attached: EU
#> 132 qc1a_3_2: Fairly attached: EU
#> 133 qc1a_3_3: Not very attached: EU
#> 134 qc1a_3_4: Not at all attached: EU
#> 135 qd8_1_1: Totally agree: often see fake news
#> 136 qd8_1_2: Tend to agree: often see fake news
#> 137 qd8_3_1: Totally agree: fake news a problem in country
#> 138 qd8_3_2: Tend to agree: fake news a problem in country
#> 139 qd8_3_3: Tend to disagree: fake news a problem in country
#> 140 qd8_3_4: Totally disagree: fake news a problem in country
#> 141 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 142 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 143 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 144 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 145 d60_1: difficulties to pay bills: most of time
#> 146 d60_2: difficulties to pay bills: time to time
#> 147 d60_3: difficulties to pay bills: Almost never
#> 148 d70_1: Very satisfied with life
#> 149 d78_1: EU association very positive
#> 150 d78_2: EU association fairly positive
#> 151 d78_4: EU association fairly negative
#> 152 d78_5: EU association very negative
#> 153 d73_2_1: EU going in right direction
#> Economic Threat
#> 1 d60_3: difficulties to pay bills: Almost never
#> 2 qa2a_5_1: National employment better in next 12 months
#> 3 qa2a_3_1: national economic situation better in next 12 months
#> 4 d60_1: difficulties to pay bills: most of time
#> 5 qa1a_2_4: Situation of national economy in general Very Bad
#> 6 qa14_5: national economy will never recover from covid
#> 7 qa1a_2_2: Situation of national economy in general rather good
#> 8 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 9 qa1a_5_4: Situation of household finances Very Bad
#> 10 qa1a_3_2: Situation of EU economy in general rather good
#> 11 qa1a_6_4: Situation of national employment Very Bad
#> 12 qa2a_7_1: EU economics better in next 12 months
#> 13 qa2a_5_2: National employment worse in next 12 months
#> 14 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 15 qa1a_4_1: Situation of personal job Very good
#> 16 qa1a_5_3: Situation of household finances rather bad
#> 17 qa1a_6_2: Situation of national employment rather good
#> 18 qa13_2_1: Totally agree covid had serious national economic consequences
#> 19 qa1a_3_4: Situation of EU economy in general Very Bad
#> 20 qa2a_6_2: personal job situation worse in next 12 months
#> 21 qa1a_4_3: Situation of personal job rather bad
#> 22 qa4a_5_1: most important personal issue: unemployment
#> 23 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 24 qa14_1: national economy already recovered from covid
#> 25 qa2a_4_2: household finances worse in next 12 months
#> 26 qa1a_4_4: Situation of personal job Very Bad
#> 27 qa2a_3_2: national economic situation worse in next 12 months
#> 28 d60_2: difficulties to pay bills: time to time
#> 29 qa1a_6_1: Situation of national employment Very good
#> 30 qa1a_5_1: Situation of household finances Very good
#> 31 qa1a_3_3: Situation of EU economy in general rather bad
#> 32 qa2a_4_1: household finances better in next 12 months
#> 33 qa1a_2_1: Situation of national economy in general Very good
#> 34 qa14_2: national economy will recover from covid in 2021
#> 35 qa2a_7_2: EU economics worse in next 12 months
#> 36 qa1a_4_2: Situation of personal job rather good
#> 37 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 38 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 39 qa3a_13_1: most important national issue: environment/climate
#> 40 qa4a_4_1: most important personal issue: taxation
#> 41 qa1a_5_2: Situation of household finances rather good
#> 42 qa2a_6_1: personal job situation better in next 12 months
#> 43 qa1a_3_1: Situation of EU economy in general Very good
#> 44 qa3a_5_1: most important national issue: unemployment
#> 45 qa4a_1_1: most important personal issue: crime
#> 46 qa4a_14_1: most important personal issue: working conditions
#> 47 qa4a_2_1: most important personal issue: national economy
#> 48 qa4a_3_1: most important personal issue: inflation
#> 49 qa4a_7_1: most important personal issue: housing
#> 50 qa3a_4_1: most important national issue: taxation
#> 51 qa3a_2_1: most important national issue: econ situation
#> 52 qa4a_12_1: most important personal issue: environment/climate
#> 53 qa3a_1_1: most important national issue: crime. Yes
#> 54 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 55 qa1a_6_3: Situation of national employment rather bad
#> 56 qa14_3: national economy will recover from covid in 2022
#> 57 qa1a_2_3: Situation of national economy in general rather bad
#> 58 qa4a_15_1: most important personal issue: living conditions
#> 59 qa3a_8_1: most important national issue: govt debt
#> 60 qa3a_12_1: most important national issue: pensions
#> 61 qa14_4: national economy will recover from covid in 2023+
#> 62 qa4a_13_1: most important personal issue: pensions
#> 63 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 64 qa1a_1_1: Situation of country in general Very good
#> 65 qa1a_1_2: Situation of country in general rather good
#> 66 qa1a_1_3: Situation of country in general rather bad
#> 67 qa1a_1_4: Situation of country in general Very Bad
#> 68 qa1a_7_1: Situation of public services Very good
#> 69 qa1a_7_2: Situation of public services rather good
#> 70 qa1a_7_3: Situation of public services rather bad
#> 71 qa1a_7_4: Situation of public services Very Bad
#> 72 qa2a_1_1: life in general better in next 12 months
#> 73 qa2a_1_2: life in general worse in next 12 months
#> 74 qa2a_2_1: national situation better in next 12 months
#> 75 qa2a_2_2: national situation worse in next 12 months
#> 76 qa3a_6_1: most important national issue: terrorism
#> 77 qa3a_9_1: most important national issue: immigration
#> 78 qa3a_10_1: most important national issue: health
#> 79 qa3a_11_1: most important national issue: education
#> 80 qa3a_15_1: most important national issue: cyprus
#> 81 qa4a_6_1: most important personal issue: terrorism
#> 82 qa4a_9_1: most important personal issue: immigration
#> 83 qa4a_10_1: most important personal issue: health
#> 84 qa4a_11_1: most important personal issue: education
#> 85 qa4a_16_1: most important personal issue: cyprus
#> 86 qa6b_1_1: trust parties
#> 87 qa6b_1_2: do not trust parties
#> 88 qa6b_3_1: trust police
#> 89 qa6b_3_2: do not trust police
#> 90 qa6b_8_1: trust national gvt
#> 91 qa6b_8_2: do not trust national gvt
#> 92 qa6b_10_1: trust EU
#> 93 qa6b_10_2: do not trust EU
#> 94 qa8_1_1: trust Euro parliament
#> 95 qa8_1_2: not trust Euro parliament
#> 96 qa8_2_1: trust Euro Commission
#> 97 qa8_2_2: not trust Euro Commission
#> 98 qa10_1_1: Very Satisfied with National covid response
#> 99 qa10_1_2: Fairly satisfied with National covid response
#> 100 qa10_1_3: Not very satisfied with National covid response
#> 101 qa10_1_4: Not at all satisfied with National covid response
#> 102 qa10_2_1: Very Satisfied with local covid response
#> 103 qa10_2_2: Fairly satisfied with local covid response
#> 104 qa10_2_3: Not very satisfied with local covid response
#> 105 qa10_2_4: Not at all satisfied with local covid response
#> 106 qa10_3_1: Very Satisfied with EU covid response
#> 107 qa10_3_2: Fairly satisfied with EU covid response
#> 108 qa10_3_3: Not very satisfied with EU covid response
#> 109 qa10_3_4: Not at all satisfied with EU covid response
#> 110 qa16_1: covid restrictions very justified
#> 111 qa16_2: covid restrictions fairly justified
#> 112 qa16_3: covid restrictions not very justified
#> 113 qa16_4: covid restrictions not at all justified
#> 114 qa16_4: covid restrictions endanger mental physical health
#> 115 qa18_1_1: totally agree: vaccines developed too fast
#> 116 qa18_1_2: tend to agree: vaccines developed too fast
#> 117 qa18_1_3: tend to disagree: vaccines developed too fast
#> 118 qa18_1_4: totally disagree: vaccines developed too fast
#> 119 qa18_2_1: totally agree: vaccines could have side effects
#> 120 qa18_2_2: tend to agree: vaccines could have side effects
#> 121 qa18_2_3: tend to disagree: vaccines could have side effects
#> 122 qa18_2_4: totally disagree: vaccines could have side effects
#> 123 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 124 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 125 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 126 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 127 qa19_1: Want vax asap
#> 128 qa19_2: want vvax in 2021
#> 129 qa19_4: want vax never
#> 130 sd18a_1: very satisfied with democracy in country
#> 131 sd18a_2: fairly satisifed with democracy in country
#> 132 sd18a_3: not very satisfied with democracy in country
#> 133 sd18a_4: not at all satisfied with democracy in country
#> 134 qc1a_3_1: Very attached: EU
#> 135 qc1a_3_2: Fairly attached: EU
#> 136 qc1a_3_3: Not very attached: EU
#> 137 qc1a_3_4: Not at all attached: EU
#> 138 qd8_1_1: Totally agree: often see fake news
#> 139 qd8_1_2: Tend to agree: often see fake news
#> 140 qd8_3_1: Totally agree: fake news a problem in country
#> 141 qd8_3_2: Tend to agree: fake news a problem in country
#> 142 qd8_3_3: Tend to disagree: fake news a problem in country
#> 143 qd8_3_4: Totally disagree: fake news a problem in country
#> 144 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 145 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 146 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 147 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 148 d70_1: Very satisfied with life
#> 149 d78_1: EU association very positive
#> 150 d78_2: EU association fairly positive
#> 151 d78_4: EU association fairly negative
#> 152 d78_5: EU association very negative
#> 153 d73_2_1: EU going in right direction
#> Health Threat
#> 1 qa18_2_1: totally agree: vaccines could have side effects
#> 2 qa10_1_4: Not at all satisfied with National covid response
#> 3 qa18_2_3: tend to disagree: vaccines could have side effects
#> 4 qa18_1_1: totally agree: vaccines developed too fast
#> 5 qa10_2_2: Fairly satisfied with local covid response
#> 6 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 7 qa10_1_2: Fairly satisfied with National covid response
#> 8 qa10_2_3: Not very satisfied with local covid response
#> 9 qa18_1_4: totally disagree: vaccines developed too fast
#> 10 qa10_3_4: Not at all satisfied with EU covid response
#> 11 qa16_4: covid restrictions endanger mental physical health
#> 12 qa10_2_4: Not at all satisfied with local covid response
#> 13 qa10_1_1: Very Satisfied with National covid response
#> 14 qa10_2_1: Very Satisfied with local covid response
#> 15 qa18_1_3: tend to disagree: vaccines developed too fast
#> 16 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 17 qa3a_10_1: most important national issue: health
#> 18 qa10_3_2: Fairly satisfied with EU covid response
#> 19 qa4a_10_1: most important personal issue: health
#> 20 qa18_2_4: totally disagree: vaccines could have side effects
#> 21 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 22 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 23 qa10_1_3: Not very satisfied with National covid response
#> 24 qa19_2: want vvax in 2021
#> 25 qa10_3_1: Very Satisfied with EU covid response
#> 26 qa10_3_3: Not very satisfied with EU covid response
#> 27 qa19_1: Want vax asap
#> 28 qa18_1_2: tend to agree: vaccines developed too fast
#> 29 qa18_2_2: tend to agree: vaccines could have side effects
#> 30 qa16_2: covid restrictions fairly justified
#> 31 qa19_4: want vax never
#> 32 qa16_3: covid restrictions not very justified
#> 33 qa16_4: covid restrictions not at all justified
#> 34 qa16_1: covid restrictions very justified
#> 35 qa1a_1_1: Situation of country in general Very good
#> 36 qa1a_1_2: Situation of country in general rather good
#> 37 qa1a_1_3: Situation of country in general rather bad
#> 38 qa1a_1_4: Situation of country in general Very Bad
#> 39 qa1a_2_1: Situation of national economy in general Very good
#> 40 qa1a_2_2: Situation of national economy in general rather good
#> 41 qa1a_2_3: Situation of national economy in general rather bad
#> 42 qa1a_2_4: Situation of national economy in general Very Bad
#> 43 qa1a_3_1: Situation of EU economy in general Very good
#> 44 qa1a_3_2: Situation of EU economy in general rather good
#> 45 qa1a_3_3: Situation of EU economy in general rather bad
#> 46 qa1a_3_4: Situation of EU economy in general Very Bad
#> 47 qa1a_4_1: Situation of personal job Very good
#> 48 qa1a_4_2: Situation of personal job rather good
#> 49 qa1a_4_3: Situation of personal job rather bad
#> 50 qa1a_4_4: Situation of personal job Very Bad
#> 51 qa1a_5_1: Situation of household finances Very good
#> 52 qa1a_5_2: Situation of household finances rather good
#> 53 qa1a_5_3: Situation of household finances rather bad
#> 54 qa1a_5_4: Situation of household finances Very Bad
#> 55 qa1a_6_1: Situation of national employment Very good
#> 56 qa1a_6_2: Situation of national employment rather good
#> 57 qa1a_6_3: Situation of national employment rather bad
#> 58 qa1a_6_4: Situation of national employment Very Bad
#> 59 qa1a_7_1: Situation of public services Very good
#> 60 qa1a_7_2: Situation of public services rather good
#> 61 qa1a_7_3: Situation of public services rather bad
#> 62 qa1a_7_4: Situation of public services Very Bad
#> 63 qa2a_1_1: life in general better in next 12 months
#> 64 qa2a_1_2: life in general worse in next 12 months
#> 65 qa2a_2_1: national situation better in next 12 months
#> 66 qa2a_2_2: national situation worse in next 12 months
#> 67 qa2a_3_1: national economic situation better in next 12 months
#> 68 qa2a_3_2: national economic situation worse in next 12 months
#> 69 qa2a_4_1: household finances better in next 12 months
#> 70 qa2a_4_2: household finances worse in next 12 months
#> 71 qa2a_5_1: National employment better in next 12 months
#> 72 qa2a_5_2: National employment worse in next 12 months
#> 73 qa2a_6_1: personal job situation better in next 12 months
#> 74 qa2a_6_2: personal job situation worse in next 12 months
#> 75 qa2a_7_1: EU economics better in next 12 months
#> 76 qa2a_7_2: EU economics worse in next 12 months
#> 77 qa3a_1_1: most important national issue: crime. Yes
#> 78 qa3a_2_1: most important national issue: econ situation
#> 79 qa3a_4_1: most important national issue: taxation
#> 80 qa3a_5_1: most important national issue: unemployment
#> 81 qa3a_6_1: most important national issue: terrorism
#> 82 qa3a_8_1: most important national issue: govt debt
#> 83 qa3a_9_1: most important national issue: immigration
#> 84 qa3a_11_1: most important national issue: education
#> 85 qa3a_12_1: most important national issue: pensions
#> 86 qa3a_13_1: most important national issue: environment/climate
#> 87 qa3a_15_1: most important national issue: cyprus
#> 88 qa4a_1_1: most important personal issue: crime
#> 89 qa4a_2_1: most important personal issue: national economy
#> 90 qa4a_3_1: most important personal issue: inflation
#> 91 qa4a_4_1: most important personal issue: taxation
#> 92 qa4a_5_1: most important personal issue: unemployment
#> 93 qa4a_6_1: most important personal issue: terrorism
#> 94 qa4a_7_1: most important personal issue: housing
#> 95 qa4a_9_1: most important personal issue: immigration
#> 96 qa4a_11_1: most important personal issue: education
#> 97 qa4a_12_1: most important personal issue: environment/climate
#> 98 qa4a_13_1: most important personal issue: pensions
#> 99 qa4a_14_1: most important personal issue: working conditions
#> 100 qa4a_15_1: most important personal issue: living conditions
#> 101 qa4a_16_1: most important personal issue: cyprus
#> 102 qa6b_1_1: trust parties
#> 103 qa6b_1_2: do not trust parties
#> 104 qa6b_3_1: trust police
#> 105 qa6b_3_2: do not trust police
#> 106 qa6b_8_1: trust national gvt
#> 107 qa6b_8_2: do not trust national gvt
#> 108 qa6b_10_1: trust EU
#> 109 qa6b_10_2: do not trust EU
#> 110 qa8_1_1: trust Euro parliament
#> 111 qa8_1_2: not trust Euro parliament
#> 112 qa8_2_1: trust Euro Commission
#> 113 qa8_2_2: not trust Euro Commission
#> 114 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 115 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 116 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 117 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 118 qa13_2_1: Totally agree covid had serious national economic consequences
#> 119 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 120 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 121 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 122 qa14_1: national economy already recovered from covid
#> 123 qa14_2: national economy will recover from covid in 2021
#> 124 qa14_3: national economy will recover from covid in 2022
#> 125 qa14_4: national economy will recover from covid in 2023+
#> 126 qa14_5: national economy will never recover from covid
#> 127 sd18a_1: very satisfied with democracy in country
#> 128 sd18a_2: fairly satisifed with democracy in country
#> 129 sd18a_3: not very satisfied with democracy in country
#> 130 sd18a_4: not at all satisfied with democracy in country
#> 131 qc1a_3_1: Very attached: EU
#> 132 qc1a_3_2: Fairly attached: EU
#> 133 qc1a_3_3: Not very attached: EU
#> 134 qc1a_3_4: Not at all attached: EU
#> 135 qd8_1_1: Totally agree: often see fake news
#> 136 qd8_1_2: Tend to agree: often see fake news
#> 137 qd8_3_1: Totally agree: fake news a problem in country
#> 138 qd8_3_2: Tend to agree: fake news a problem in country
#> 139 qd8_3_3: Tend to disagree: fake news a problem in country
#> 140 qd8_3_4: Totally disagree: fake news a problem in country
#> 141 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 142 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 143 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 144 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 145 d60_1: difficulties to pay bills: most of time
#> 146 d60_2: difficulties to pay bills: time to time
#> 147 d60_3: difficulties to pay bills: Almost never
#> 148 d70_1: Very satisfied with life
#> 149 d78_1: EU association very positive
#> 150 d78_2: EU association fairly positive
#> 151 d78_4: EU association fairly negative
#> 152 d78_5: EU association very negative
#> 153 d73_2_1: EU going in right direction
#> Support Immigration
#> 1 qa3a_9_1: most important national issue: immigration
#> 2 qa4a_9_1: most important personal issue: immigration
#> 3 qa1a_1_1: Situation of country in general Very good
#> 4 qa1a_1_2: Situation of country in general rather good
#> 5 qa1a_1_3: Situation of country in general rather bad
#> 6 qa1a_1_4: Situation of country in general Very Bad
#> 7 qa1a_2_1: Situation of national economy in general Very good
#> 8 qa1a_2_2: Situation of national economy in general rather good
#> 9 qa1a_2_3: Situation of national economy in general rather bad
#> 10 qa1a_2_4: Situation of national economy in general Very Bad
#> 11 qa1a_3_1: Situation of EU economy in general Very good
#> 12 qa1a_3_2: Situation of EU economy in general rather good
#> 13 qa1a_3_3: Situation of EU economy in general rather bad
#> 14 qa1a_3_4: Situation of EU economy in general Very Bad
#> 15 qa1a_4_1: Situation of personal job Very good
#> 16 qa1a_4_2: Situation of personal job rather good
#> 17 qa1a_4_3: Situation of personal job rather bad
#> 18 qa1a_4_4: Situation of personal job Very Bad
#> 19 qa1a_5_1: Situation of household finances Very good
#> 20 qa1a_5_2: Situation of household finances rather good
#> 21 qa1a_5_3: Situation of household finances rather bad
#> 22 qa1a_5_4: Situation of household finances Very Bad
#> 23 qa1a_6_1: Situation of national employment Very good
#> 24 qa1a_6_2: Situation of national employment rather good
#> 25 qa1a_6_3: Situation of national employment rather bad
#> 26 qa1a_6_4: Situation of national employment Very Bad
#> 27 qa1a_7_1: Situation of public services Very good
#> 28 qa1a_7_2: Situation of public services rather good
#> 29 qa1a_7_3: Situation of public services rather bad
#> 30 qa1a_7_4: Situation of public services Very Bad
#> 31 qa2a_1_1: life in general better in next 12 months
#> 32 qa2a_1_2: life in general worse in next 12 months
#> 33 qa2a_2_1: national situation better in next 12 months
#> 34 qa2a_2_2: national situation worse in next 12 months
#> 35 qa2a_3_1: national economic situation better in next 12 months
#> 36 qa2a_3_2: national economic situation worse in next 12 months
#> 37 qa2a_4_1: household finances better in next 12 months
#> 38 qa2a_4_2: household finances worse in next 12 months
#> 39 qa2a_5_1: National employment better in next 12 months
#> 40 qa2a_5_2: National employment worse in next 12 months
#> 41 qa2a_6_1: personal job situation better in next 12 months
#> 42 qa2a_6_2: personal job situation worse in next 12 months
#> 43 qa2a_7_1: EU economics better in next 12 months
#> 44 qa2a_7_2: EU economics worse in next 12 months
#> 45 qa3a_1_1: most important national issue: crime. Yes
#> 46 qa3a_2_1: most important national issue: econ situation
#> 47 qa3a_4_1: most important national issue: taxation
#> 48 qa3a_5_1: most important national issue: unemployment
#> 49 qa3a_6_1: most important national issue: terrorism
#> 50 qa3a_8_1: most important national issue: govt debt
#> 51 qa3a_10_1: most important national issue: health
#> 52 qa3a_11_1: most important national issue: education
#> 53 qa3a_12_1: most important national issue: pensions
#> 54 qa3a_13_1: most important national issue: environment/climate
#> 55 qa3a_15_1: most important national issue: cyprus
#> 56 qa4a_1_1: most important personal issue: crime
#> 57 qa4a_2_1: most important personal issue: national economy
#> 58 qa4a_3_1: most important personal issue: inflation
#> 59 qa4a_4_1: most important personal issue: taxation
#> 60 qa4a_5_1: most important personal issue: unemployment
#> 61 qa4a_6_1: most important personal issue: terrorism
#> 62 qa4a_7_1: most important personal issue: housing
#> 63 qa4a_10_1: most important personal issue: health
#> 64 qa4a_11_1: most important personal issue: education
#> 65 qa4a_12_1: most important personal issue: environment/climate
#> 66 qa4a_13_1: most important personal issue: pensions
#> 67 qa4a_14_1: most important personal issue: working conditions
#> 68 qa4a_15_1: most important personal issue: living conditions
#> 69 qa4a_16_1: most important personal issue: cyprus
#> 70 qa6b_1_1: trust parties
#> 71 qa6b_1_2: do not trust parties
#> 72 qa6b_3_1: trust police
#> 73 qa6b_3_2: do not trust police
#> 74 qa6b_8_1: trust national gvt
#> 75 qa6b_8_2: do not trust national gvt
#> 76 qa6b_10_1: trust EU
#> 77 qa6b_10_2: do not trust EU
#> 78 qa8_1_1: trust Euro parliament
#> 79 qa8_1_2: not trust Euro parliament
#> 80 qa8_2_1: trust Euro Commission
#> 81 qa8_2_2: not trust Euro Commission
#> 82 qa10_1_1: Very Satisfied with National covid response
#> 83 qa10_1_2: Fairly satisfied with National covid response
#> 84 qa10_1_3: Not very satisfied with National covid response
#> 85 qa10_1_4: Not at all satisfied with National covid response
#> 86 qa10_2_1: Very Satisfied with local covid response
#> 87 qa10_2_2: Fairly satisfied with local covid response
#> 88 qa10_2_3: Not very satisfied with local covid response
#> 89 qa10_2_4: Not at all satisfied with local covid response
#> 90 qa10_3_1: Very Satisfied with EU covid response
#> 91 qa10_3_2: Fairly satisfied with EU covid response
#> 92 qa10_3_3: Not very satisfied with EU covid response
#> 93 qa10_3_4: Not at all satisfied with EU covid response
#> 94 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 95 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 96 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 97 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 98 qa13_2_1: Totally agree covid had serious national economic consequences
#> 99 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 100 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 101 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 102 qa14_1: national economy already recovered from covid
#> 103 qa14_2: national economy will recover from covid in 2021
#> 104 qa14_3: national economy will recover from covid in 2022
#> 105 qa14_4: national economy will recover from covid in 2023+
#> 106 qa14_5: national economy will never recover from covid
#> 107 qa16_1: covid restrictions very justified
#> 108 qa16_2: covid restrictions fairly justified
#> 109 qa16_3: covid restrictions not very justified
#> 110 qa16_4: covid restrictions not at all justified
#> 111 qa16_4: covid restrictions endanger mental physical health
#> 112 qa18_1_1: totally agree: vaccines developed too fast
#> 113 qa18_1_2: tend to agree: vaccines developed too fast
#> 114 qa18_1_3: tend to disagree: vaccines developed too fast
#> 115 qa18_1_4: totally disagree: vaccines developed too fast
#> 116 qa18_2_1: totally agree: vaccines could have side effects
#> 117 qa18_2_2: tend to agree: vaccines could have side effects
#> 118 qa18_2_3: tend to disagree: vaccines could have side effects
#> 119 qa18_2_4: totally disagree: vaccines could have side effects
#> 120 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 121 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 122 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 123 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 124 qa19_1: Want vax asap
#> 125 qa19_2: want vvax in 2021
#> 126 qa19_4: want vax never
#> 127 sd18a_1: very satisfied with democracy in country
#> 128 sd18a_2: fairly satisifed with democracy in country
#> 129 sd18a_3: not very satisfied with democracy in country
#> 130 sd18a_4: not at all satisfied with democracy in country
#> 131 qc1a_3_1: Very attached: EU
#> 132 qc1a_3_2: Fairly attached: EU
#> 133 qc1a_3_3: Not very attached: EU
#> 134 qc1a_3_4: Not at all attached: EU
#> 135 qd8_1_1: Totally agree: often see fake news
#> 136 qd8_1_2: Tend to agree: often see fake news
#> 137 qd8_3_1: Totally agree: fake news a problem in country
#> 138 qd8_3_2: Tend to agree: fake news a problem in country
#> 139 qd8_3_3: Tend to disagree: fake news a problem in country
#> 140 qd8_3_4: Totally disagree: fake news a problem in country
#> 141 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 142 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 143 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 144 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 145 d60_1: difficulties to pay bills: most of time
#> 146 d60_2: difficulties to pay bills: time to time
#> 147 d60_3: difficulties to pay bills: Almost never
#> 148 d70_1: Very satisfied with life
#> 149 d78_1: EU association very positive
#> 150 d78_2: EU association fairly positive
#> 151 d78_4: EU association fairly negative
#> 152 d78_5: EU association very negative
#> 153 d73_2_1: EU going in right direction
#> Support EU
#> 1 qa6b_10_1: trust EU
#> 2 qa6b_10_2: do not trust EU
#> 3 qa8_2_1: trust Euro Commission
#> 4 qa8_1_1: trust Euro parliament
#> 5 qc1a_3_4: Not at all attached: EU
#> 6 qa8_2_2: not trust Euro Commission
#> 7 d78_2: EU association fairly positive
#> 8 qa8_1_2: not trust Euro parliament
#> 9 d78_5: EU association very negative
#> 10 qa10_3_4: Not at all satisfied with EU covid response
#> 11 qc1a_3_1: Very attached: EU
#> 12 qc1a_3_2: Fairly attached: EU
#> 13 qa10_3_2: Fairly satisfied with EU covid response
#> 14 d78_4: EU association fairly negative
#> 15 d78_1: EU association very positive
#> 16 d73_2_1: EU going in right direction
#> 17 qa10_3_3: Not very satisfied with EU covid response
#> 18 qa10_3_1: Very Satisfied with EU covid response
#> 19 qc1a_3_3: Not very attached: EU
#> 20 qa1a_1_1: Situation of country in general Very good
#> 21 qa1a_1_2: Situation of country in general rather good
#> 22 qa1a_1_3: Situation of country in general rather bad
#> 23 qa1a_1_4: Situation of country in general Very Bad
#> 24 qa1a_2_1: Situation of national economy in general Very good
#> 25 qa1a_2_2: Situation of national economy in general rather good
#> 26 qa1a_2_3: Situation of national economy in general rather bad
#> 27 qa1a_2_4: Situation of national economy in general Very Bad
#> 28 qa1a_3_1: Situation of EU economy in general Very good
#> 29 qa1a_3_2: Situation of EU economy in general rather good
#> 30 qa1a_3_3: Situation of EU economy in general rather bad
#> 31 qa1a_3_4: Situation of EU economy in general Very Bad
#> 32 qa1a_4_1: Situation of personal job Very good
#> 33 qa1a_4_2: Situation of personal job rather good
#> 34 qa1a_4_3: Situation of personal job rather bad
#> 35 qa1a_4_4: Situation of personal job Very Bad
#> 36 qa1a_5_1: Situation of household finances Very good
#> 37 qa1a_5_2: Situation of household finances rather good
#> 38 qa1a_5_3: Situation of household finances rather bad
#> 39 qa1a_5_4: Situation of household finances Very Bad
#> 40 qa1a_6_1: Situation of national employment Very good
#> 41 qa1a_6_2: Situation of national employment rather good
#> 42 qa1a_6_3: Situation of national employment rather bad
#> 43 qa1a_6_4: Situation of national employment Very Bad
#> 44 qa1a_7_1: Situation of public services Very good
#> 45 qa1a_7_2: Situation of public services rather good
#> 46 qa1a_7_3: Situation of public services rather bad
#> 47 qa1a_7_4: Situation of public services Very Bad
#> 48 qa2a_1_1: life in general better in next 12 months
#> 49 qa2a_1_2: life in general worse in next 12 months
#> 50 qa2a_2_1: national situation better in next 12 months
#> 51 qa2a_2_2: national situation worse in next 12 months
#> 52 qa2a_3_1: national economic situation better in next 12 months
#> 53 qa2a_3_2: national economic situation worse in next 12 months
#> 54 qa2a_4_1: household finances better in next 12 months
#> 55 qa2a_4_2: household finances worse in next 12 months
#> 56 qa2a_5_1: National employment better in next 12 months
#> 57 qa2a_5_2: National employment worse in next 12 months
#> 58 qa2a_6_1: personal job situation better in next 12 months
#> 59 qa2a_6_2: personal job situation worse in next 12 months
#> 60 qa2a_7_1: EU economics better in next 12 months
#> 61 qa2a_7_2: EU economics worse in next 12 months
#> 62 qa3a_1_1: most important national issue: crime. Yes
#> 63 qa3a_2_1: most important national issue: econ situation
#> 64 qa3a_4_1: most important national issue: taxation
#> 65 qa3a_5_1: most important national issue: unemployment
#> 66 qa3a_6_1: most important national issue: terrorism
#> 67 qa3a_8_1: most important national issue: govt debt
#> 68 qa3a_9_1: most important national issue: immigration
#> 69 qa3a_10_1: most important national issue: health
#> 70 qa3a_11_1: most important national issue: education
#> 71 qa3a_12_1: most important national issue: pensions
#> 72 qa3a_13_1: most important national issue: environment/climate
#> 73 qa3a_15_1: most important national issue: cyprus
#> 74 qa4a_1_1: most important personal issue: crime
#> 75 qa4a_2_1: most important personal issue: national economy
#> 76 qa4a_3_1: most important personal issue: inflation
#> 77 qa4a_4_1: most important personal issue: taxation
#> 78 qa4a_5_1: most important personal issue: unemployment
#> 79 qa4a_6_1: most important personal issue: terrorism
#> 80 qa4a_7_1: most important personal issue: housing
#> 81 qa4a_9_1: most important personal issue: immigration
#> 82 qa4a_10_1: most important personal issue: health
#> 83 qa4a_11_1: most important personal issue: education
#> 84 qa4a_12_1: most important personal issue: environment/climate
#> 85 qa4a_13_1: most important personal issue: pensions
#> 86 qa4a_14_1: most important personal issue: working conditions
#> 87 qa4a_15_1: most important personal issue: living conditions
#> 88 qa4a_16_1: most important personal issue: cyprus
#> 89 qa6b_1_1: trust parties
#> 90 qa6b_1_2: do not trust parties
#> 91 qa6b_3_1: trust police
#> 92 qa6b_3_2: do not trust police
#> 93 qa6b_8_1: trust national gvt
#> 94 qa6b_8_2: do not trust national gvt
#> 95 qa10_1_1: Very Satisfied with National covid response
#> 96 qa10_1_2: Fairly satisfied with National covid response
#> 97 qa10_1_3: Not very satisfied with National covid response
#> 98 qa10_1_4: Not at all satisfied with National covid response
#> 99 qa10_2_1: Very Satisfied with local covid response
#> 100 qa10_2_2: Fairly satisfied with local covid response
#> 101 qa10_2_3: Not very satisfied with local covid response
#> 102 qa10_2_4: Not at all satisfied with local covid response
#> 103 qa13_1_1: Totally agree covid had serious personal financial consequences
#> 104 qa13_1_2: Tend to agree covid had serious personal financial consequences
#> 105 qa13_1_3: Tend to disagree covid had serious personal financial consequences
#> 106 qa13_1_4: Totally disagree covid had serious personal financial consequences
#> 107 qa13_2_1: Totally agree covid had serious national economic consequences
#> 108 qa13_2_2: Tend to agree covid had serious national economic consequences
#> 109 qa13_2_3: Tend to disagree covid had serious national economic consequences
#> 110 qa13_2_4: Totally disagree covid had serious national economic consequences
#> 111 qa14_1: national economy already recovered from covid
#> 112 qa14_2: national economy will recover from covid in 2021
#> 113 qa14_3: national economy will recover from covid in 2022
#> 114 qa14_4: national economy will recover from covid in 2023+
#> 115 qa14_5: national economy will never recover from covid
#> 116 qa16_1: covid restrictions very justified
#> 117 qa16_2: covid restrictions fairly justified
#> 118 qa16_3: covid restrictions not very justified
#> 119 qa16_4: covid restrictions not at all justified
#> 120 qa16_4: covid restrictions endanger mental physical health
#> 121 qa18_1_1: totally agree: vaccines developed too fast
#> 122 qa18_1_2: tend to agree: vaccines developed too fast
#> 123 qa18_1_3: tend to disagree: vaccines developed too fast
#> 124 qa18_1_4: totally disagree: vaccines developed too fast
#> 125 qa18_2_1: totally agree: vaccines could have side effects
#> 126 qa18_2_2: tend to agree: vaccines could have side effects
#> 127 qa18_2_3: tend to disagree: vaccines could have side effects
#> 128 qa18_2_4: totally disagree: vaccines could have side effects
#> 129 qa18_5_1: totally agree: Don't understand vaccine reluctance
#> 130 qa18_5_2: tend to agree: Don't understand vaccine reluctance
#> 131 qa18_5_3: tend to disagree: Don't understand vaccine reluctance
#> 132 qa18_5_4: totally disagree: Don't understand vaccine reluctance
#> 133 qa19_1: Want vax asap
#> 134 qa19_2: want vvax in 2021
#> 135 qa19_4: want vax never
#> 136 sd18a_1: very satisfied with democracy in country
#> 137 sd18a_2: fairly satisifed with democracy in country
#> 138 sd18a_3: not very satisfied with democracy in country
#> 139 sd18a_4: not at all satisfied with democracy in country
#> 140 qd8_1_1: Totally agree: often see fake news
#> 141 qd8_1_2: Tend to agree: often see fake news
#> 142 qd8_3_1: Totally agree: fake news a problem in country
#> 143 qd8_3_2: Tend to agree: fake news a problem in country
#> 144 qd8_3_3: Tend to disagree: fake news a problem in country
#> 145 qd8_3_4: Totally disagree: fake news a problem in country
#> 146 qd8_4_1: Totally agree: fake news a problem for democracies in general
#> 147 qd8_4_2: Tend to agree: fake news a problem for democracies in general
#> 148 qd8_4_3: Tend to disagree: fake news a problem for democracies in general
#> 149 qd8_4_4: Totally disagree: fake news a problem for democracies in general
#> 150 d60_1: difficulties to pay bills: most of time
#> 151 d60_2: difficulties to pay bills: time to time
#> 152 d60_3: difficulties to pay bills: Almost never
#> 153 d70_1: Very satisfied with life