Title: | Statistics: Data Analysis and Modelling |
Version: | 0.2.0 |
Description: | Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) https://mspeekenbrink.github.io/sdam-book/ and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) https://mspeekenbrink.github.io/sdam-r-companion/. All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts. |
License: | GPL-3 |
Encoding: | UTF-8 |
Language: | en-GB |
LazyData: | true |
RoxygenNote: | 7.2.1 |
URL: | https://mspeekenbrink.github.io/sdam-r/ |
Depends: | R (≥ 3.5.0) |
Imports: | methods, stats, dplyr, ggplot2, grid, car |
NeedsCompilation: | no |
Packaged: | 2022-11-13 19:29:53 UTC; maarten |
Author: | Maarten Speekenbrink
|
Maintainer: | Maarten Speekenbrink <m.speekenbrink@ucl.ac.uk> |
Repository: | CRAN |
Date/Publication: | 2022-11-16 21:50:03 UTC |
Flat violin geometry
Description
Flat violin geometry
Anchoring
Description
Numerical judgments of the height of the Mount Everest after a low or high anchor. This dataset comes from the ManyLabs 1 study
Usage
anchoring
Format
A data frame with 4632 rows and 5 variables:
- session_id
Unique identifier for participants
- sex
Sex of participant (f = female, m = male)
- age
Age of participant in years
- citizenship
Country code of citizenship
- referrer
Location of data collection. Site abbreviations used here can be matched up to the full site name in the online supplement https://osf.io/wx7ck/
- us_or_international
Was the study conducted on a US sample or international sample?
- lab_or_online
Was the study conducted online or in-lab?
- anchor
anchor, whether high or low
- everest_feet
judged height of Mount Everest in feet. Converted from meters if given in meters.
- everest_meters
judged height of Mount Everest in meters. Only contains values when judgment was actually given in meters.
Source
https://osf.io/pqf9r/. See also Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Jr., Bahník, S., Bernstein, M. J., . . ., Nosek, B. A. (2014). Investigating variation in replicability: A "many labs" replication project. Social Psychology, 45(3), 142-152. doi:10.1027/1864-9335/a000178
Mean-centered values
Description
center
computes mean-centered values. It is a convenience wrapper to scale
, equal to scale(x, scale=FALSE)
Usage
center(x)
Arguments
x |
Numeric vector |
Value
A numeric vector with mean-centered values
Examples
data(anchoring)
center(anchoring$everest_feet)
Data from Experiment 1 of Carragher, D.J., Thomas, N.A., Gwinn, O.S. et al. (2019) Limited evidence of hierarchical encoding in the cheerleader effect. Scientific Reports, 9, 9329. https://doi.org/10.1038/s41598-019-45789-6
Description
\@format A data frame with 320 observations of 16 variables:
- Participant
(factor) Participant ID
- Age
(numeric) Participant age in years
- Sex
(factor) Participant sex (Male or Female)
- Task
(factor) Identical-Distractors, or Self-Distractors.
- LineClickAccuracy
Measure of average response deviation from the visual analogue scale; scores > +/- 2.00 constitute exclusion.
- Excluded
(numeric) Indicator whether participant was excluded from main analysis (0 = no, 1 = yes)
- WhyExcluded
(character) explanation for exclusion
- Item
(factor) Item description
- Response
Attractiveness rating for the target face on a visual analogue scale ranging from “Very Unattractive” (0) to “Very Attractive” (100)
Usage
cheerleader
Format
An object of class data.frame
with 192 rows and 9 columns.
Source
https://osf.io/je5u7/. Carragher, D.J., Thomas, N.A., Gwinn, O.S. et al. (2019) Limited evidence of hierarchical encoding in the cheerleader effect. Scientific Reports, 9, 9329 doi:10.1038/s41598-019-45789-6.
Data from Experiment 5 of Gilder, T. S. E., & Heerey, E. A. (2018). The Role of Experimenter Belief in Social Priming. Psychological Science, 29(3), 403–417.
Description
\@format A data frame with 400 observations of 16 variables:
- pid
Participant ID
- exptrNum
Experimenter Number
- age
Participant age in years
- gender
Participant self-reported gender
- yearInUni
Year in University
- ethnicity
Self-reported ethnicity
- englishFluency
Self-reported English fluency (1=beginner; 7=native language)
- experimenterBelief
Experimenter Belief (H: High or L: Low)
- primeCond
Actual Prime Condition (HPP: High-power prime or LPP: low-power prime)
- powerPRE
Self-reported power BEFORE the manipulation
- powerPOST
Self-reported power AFTER the manipulation
- ApproachAdvantage
Approach advantage (Avoid RT - Approach RT; see manuscript)
- attractive
Rating of experimenter ATTRACTIVENESS
- competent
Rating of experimenter COMPETENCE
- friendly
Rating of experimenter FRIENDLINESS
- trustworthy
Rating of experimenter TRUSTWORTHINESS
Usage
expBelief
Format
An object of class data.frame
with 400 rows and 16 columns.
Source
https://osf.io/un4h6/. See also Gilder, T. S. E., & Heerey, E. A. (2018). The Role of Experimenter Belief in Social Priming. Psychological Science, 29(3), 403–417. doi:10.1177/0956797617737128.
Expand all contrast terms in car::Anova
Description
expand_Anova
is an experimental function to add more detailed results to those returned by car::Anova
. In particular, expand_Anova
aims to provide test results for all individual contrasts assigned to the factors in a linear model, in addition to the omnibus tests returned by car::Anova
.
Usage
expand_Anova(mod, type = c("III", "II", 3, 2), ...)
Arguments
mod |
A model of class lm (see ?stats::lm) |
type |
SS Type (see ?car::Anova) |
... |
Further arguments passed to Anova |
Details
This is an experimental function
Value
Object of class anova returned by car::Anova
See Also
car::Anova()
for more information about the Anova tables, and stats::lm()
for information about how to specify the model
Examples
data("tetris2015")
mod <- lm(Days_One_to_Seven_Number_of_Intrusions ~ Condition, data=tetris2015)
car::Anova(mod,type=3) # default type III Anova table
expand_Anova(mod,type=3)
Predictions by Paul the Octopus in the 2010 FIFA World Cup.
Description
A dataset containing the predictions and outcomes of matches in the 2010 FIFA European Cup.
Usage
fifa2010
Format
A data frame with 8 rows and 4 variables:
- Match
countries playing
- Prediction
country predicted to win
- Result
score at the end of the match
- Outcome
whether Paul was correct or incorrect
Source
https://en.wikipedia.org/wiki/Paul_the_Octopus
FIFA 2010 team statistics
Description
Statistics for all teams playing in the 2010 FIFA world cup.
Usage
fifa2010teams
Format
A data frame with 11 variables and 32 rows
- nr
Unique numeric identifier for each team
- team
Name of the team (i.e. country)
- matches_played
Number of matches played
- goals_for
Total goals counted against their opponents
- goals_scored
Total goals scored against their opponents
- goals_against
Goals counted against the team
- penalty_goal
Number of penalty goals scored
- own_goals_for
Number of own goals
- yellow_cards
Number of yellow cards
- indirect_red_cards
Number of indirect red cards
- direct_red_cards
Number of direct red cards
Source
FIFA website. https://www.fifa.com/worldcup/archive/southafrica2010/statistics/teams/goal-scored and https://www.fifa.com/worldcup/archive/southafrica2010/statistics/teams/disciplinary
Half violin plot
Description
Half violin plot
Usage
geom_flat_violin(
mapping = NULL,
data = NULL,
stat = "ydensity",
position = "dodge",
trim = TRUE,
scale = "area",
show.legend = NA,
inherit.aes = TRUE,
...
)
Arguments
mapping |
The mapping |
data |
data.frame |
stat |
statistic (don't change) |
position |
position dodge |
trim |
Logical |
scale |
Scale (don't change) |
show.legend |
Logical |
inherit.aes |
Logical |
... |
other arguments |
Value
A layer for a ggplot2::ggplot
object, similar to e.g. ggplot2::geom_violin
.
Source
urlhttps://gist.github.com/dgrtwo/eb7750e74997891d7c20
See Also
ggplot2::geom_violin()
, which provided the basis of this function.
Examples
library(ggplot2)
data(diamonds)
ggplot(diamonds, aes(cut, carat)) + geom_flat_violin() + coord_flip()
Data from Winter, B., & Burkner, P. (2021) Poisson regression for linguists: A tutorial introduction to modelling count data with brms. Language and Linguistics Compass, 15, e12439 doi:10.1111/lnc3.12439
Description
\@format A data frame with 54 observations of 6 variables:
- ID
(factor) Participant ID
- context
(factor) Whether talking to a friend or professor
- duration
(numeric) Duration of the interaction
- language
(factor) Language spoken: Catalan or Korean
- gender
(factor) Participant gender (M = male, F = female)
- gestures
(numeric) number of gestures in the interaction.
Usage
gestures
Format
An object of class data.frame
with 54 rows and 6 columns.
Source
Legacy motives and pro-environmental behaviour
Description
Legacy motives and pro-environmental behaviour
Usage
legacy2015
Format
A data frame with 245 rows and 9 variables:
- id
(numeric) ID variable relating to the original dataset
- sex
(character) biological sex of participant (male or female)
- age
(numeric) age in years
- legacy
(numeric) Sverage of 8 items reflecting legacy motivation, on a scale from 1 (Not at all) to 6 (A great amount)
- belief
(numeric) average of 5 items reflecting belief in climate change, on a scale from 1 (Strongly Disagree) to 7 (Strongly Agree)
- intention
(numeric) average of 8 items reflecting intention to act in a pro-environmental way, on a scale from 1 (Never) to 6 (All the time)
- education
(numeric) Level of education, 1 = 8th grade or less, 2 = Some high school, 3 = Graduated high school, 4 = Some college or technical school, 5 = Graduated college or technical school, 6 = Post-graduate
- income
(numeric) Approximate household income, 1 = less than $20K, 2 = $20K-$35K, 3 = $35K-$50K 4 = $50K-$75K, 5 = $75K-100K, 6 = more than 100K
- donation
(numeric) Donation of possible bonus payment, between $0 and $10
Source
Harvard DataVerse https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/27740&version=1.0
Examples
## Not run:
# this dataset was processed from the raw data as follows:
tdat <- read.csv("legacy study - pilot_data.csv")
## End(Not run)
Data from Rausch, M. & Zehetleitner, M. (2016) Visibility is not equivalent to confidence in a low contrast orientation discrimination task. Frontiers in Psychology, 7, p. 591 doi:10.3389/fpsyg.2016.00591
Description
\@format A data frame with 7560 observations of 10 variables:
- id
(factor) Participant ID
- age
(numeric) Participant age in years
- sex
(factor) Participant sex (male or female)
- block
(numeric) number of the test block (from 1 to 9). Practice block is excluded.
- trial
(numeric) number of trial (between 1 and 42) within a block.
- tilt
(numeric) whether grating is horizontal (0) or vertical (90)
- contrast
(numeric) contrast of grating shown
- correct
(numeric) Whether identified title was correct (1) or not (0)
- visibility
(numeric) Rated visibility of the stimulus, on a scale between 0 () and 100 ()
- confidence
(numeric) Rated confidence in tilt identification, on a scale between 0 () and 100 ()
Usage
metacognition
Format
An object of class data.frame
with 7560 rows and 10 columns.
Source
https://osf.io/vk6fe/. Rausch, M. & Zehetleitner, M. (2016) Visibility is not equivalent to confidence in a low contrast orientation discrimination task. Frontiers in Psychology, 7, p. 591 doi:10.3389/fpsyg.2016.00591.
Data based on a post-election survey by YouGov (see https://yougov.co.uk/topics/politics/articles-reports/2017/06/13/how-britain-voted-2017-general-election). Note that the data was recreated by combining frequency and percentage results reported in https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/smo1w49ph1/InternalResults_170613_2017Election_Demographics_W.pdf. Due to rounding and other potential inconsistencies, this data set will likely differ from the actual results.
Description
\@format A data frame with 90 observations of 3 variables:
- newspaper
(factor) Reported newspaper read most often
- vote
(factor) Which party voted on (including "did not vote")
- n
(numeric) Number of people in the survey who responded with that combination of newspaper and vote
Usage
papervotes
Format
An object of class data.frame
with 90 rows and 3 columns.
Source
Q-Q plots with distributions in the margins
Description
plot_qq_marginals
creates an enhanced Q-Q plot with the observed and theoretical distributions shown in the margins of the plot.
Usage
plot_qq_marginals(
x,
breaks = "Sturges",
newpage = TRUE,
xlab = "Observed Quantiles",
ylab = "Theoretical quantiles",
xlim = grDevices::extendrange(c(min(x), max(x))),
ylim = NULL,
main = NULL,
sub = NULL,
axes = TRUE,
border = TRUE,
...
)
Arguments
x |
A numeric vector |
breaks |
How to compute breakpoints for the histogram. See ?hist |
newpage |
(logical) Should the plot be plotted on a new page? |
xlab |
Label for x-axis |
ylab |
Label for y-axis |
xlim |
Range of x values shown |
ylim |
Range of y values shown |
main |
Main title |
sub |
Subtitle |
axes |
(logical) Draw axes? |
border |
(logical) Draw a border? |
... |
Further arguments |
Value
No return value. The function adds a plot to the active graphics window.
Examples
data(anchoring)
plot_qq_marginals(anchoring$everest_feet)
Create a raincloud plot
Description
plot_raincloud
creates a raincloud plot to display the distribution of data by a combination of a a boxplot, a kernel density plot, and a scatterplot. The boxplot includes the median (displayed as a horizontal line) and the mean (displayed as a point). It does not indicate potential outliers, as these can be seen in the scatter plot. The kernel density plot provides a nonparametric estimate of the distribution. The scatterplot depicts all values in y
with random jittering on the x-axis. The data can be grouped by supplying a grouping factor in the groups
argument, in which case multiple raincloud plots are shown side by side. As plot_raincloud
provides a ggplot2::ggplot
object, it can be combined with further layers and functionality from the ggplot2
package.
Usage
plot_raincloud(data, y, horizontal = FALSE, groups, point_size = 0.5, ...)
Arguments
data |
Data.frame (or tibble) |
y |
The unquoted name of the variable in data for which to create the raincloud plot |
horizontal |
(logical) change the orientation of the plot |
groups |
An unquoted name of grouping variable in data (ideally a factor) |
point_size |
Size of the jittered points |
... |
Other arguments, passed to ggplot(aes(...)) |
Value
An object of class gg
, i.e. a ggplot
object from the ggplot2
package
Source
Allen M, Poggiali D, Whitaker K et al. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res 2019, 4:63 (doi:10.12688/wellcomeopenres.15191.1)
See Also
ggplot2::ggplot()
for information about ggplot objects, ggplot2::theme()
for information about changing various aspects of the plot, and ggplot2::facet_wrap()
and ggplot2::facet_grid()
for creating multiple raincloud plots for different levels of grouping factors beyond those specified in groups
.
Examples
data(anchoring)
plot_raincloud(anchoring,y=everest_feet)
plot_raincloud(anchoring,y=everest_feet,groups=anchor)
plot_raincloud(anchoring,y=everest_feet,groups=anchor) +
ggplot2::facet_wrap(~us_or_international) +
ggplot2::ylab("How high is Mount Everest (in feet)?")
Simultaneously nudge and jitter
Description
Simultaneously nudge and jitter
Usage
position_jitternudge(
jitter.width = NULL,
jitter.height = 0,
nudge.x = 0,
nudge.y = 0,
seed = NA
)
Arguments
jitter.width |
degree of jitter in x direction. Defaults to 40% of the resolution of the data. |
jitter.height |
degree of jitter in y direction. Defaults to 0. |
nudge.x |
the amount to nudge in the x direction. |
nudge.y |
the amount to nudge in the y direction. |
seed |
Optional seed for the random jitter |
Value
Positions for data in a ggplot2::ggplot
object, similar to e.g. ggplot2::position_jitter
See Also
ggplot2::position_jitter()
, which is the basis of this function.
Examples
library(ggplot2)
dsub <- diamonds[ sample(nrow(diamonds), 1000), ]
ggplot(dsub, aes(x = cut, y = carat, fill = clarity)) +
geom_boxplot(outlier.size = 0) +
geom_point(pch = 21, position = position_jitterdodge())
Redistribution of wealth
Description
It is generally found that wealthy people tend to be more opposed to policies to reduce wealth inequalities. This may be unsurprising from a classical economic standpoint, because the material burden of the redistribution of wealth will fall on wealthier people. Wealthier people are also more likely than poorer people to adopt political ideologies that oppose redistribution policies. Dawtry, Sutton, and Sibley (2015) investigated whether, in addition to such factors, “social-sampling processes” lead wealthier people to oppose redistribution policies. Social sampling is the idea that people (partly) base inferences on their social surroundings. Wealthier people tend to live in more affluent areas and move in wealthier social circles. This may bias their view of the world, where wealthier people estimate the general population to be wealthier (with less of a gap between the wealthy and the poor) than it really is.
Usage
redist2015
Format
A data frame with 305 rows and 12 variables:
- id
unique ID number for each participant
- gender
only "male" or "female" could be answered by the looks of it
- age
participant age in years
- income
yearly household income (in units of $1,000)
- pol_att
political leaning from 1="Extremely Liberal" to 9="Extremely Conservative"
- PD_mean
estimate average household income in the general US population
- PD_gini
GINI index computed for a subjective distribution of wealth in the general US population. The GINI index is a measure of wealth inequality; higher numbers mean more inequality
- PD_fair
Answer to the question "To what extent do you feel that household incomes are fairly–unfairly distributed across the US population?" on a scale from 1="Extremely Fair" to 9 = "Extremely Unfair".
- PD_sat
Answer to the question "How satisfied–dissatisfied are you with the way in which household incomes are distributed across the US population?" on a scale from 1="Extremely satisfied" to 9="Extremely dissatisfied".
- SC_mean
estimate average household income in the participant's social circles
- SC_gini
(subjective) inequality in the participant's social circles
- redist
support for wealth redistribution policies (average of four items, higher scores indicate stronger support).
Details
In Experiment 1a of Dawtry, Sutton, and Sibley (2015), they assessed income and opinions for n=305 online U.S. participants recruited via Amazon’s Mechanical Turk.
Source
https://osf.io/3mftr/. See also Dawtry, Rael J., Robbie M. Sutton, and Chris G. Sibley. 2015. “Why Wealthier People Think People Are Wealthier, and Why It Matters: From Social Sampling to Attitudes to Redistribution.” Psychological Science 26 (9): 1389–1400. doi:10.1177/0956797615586560.
Data from Experiment 1 in Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342 doi:10.1007/s42113-022-00133-6. Participants (n=52) each play 50 rounds of Rock-Paper-Scissors against an AI player who either adopts a "level-1" or "level-2" strategy. A level-1 strategy assumes the opponent will repeat their last action, and chooses the action that beats this. A level-2 strategy assumes the opponent adopts a level-1 strategy, and chooses the action that beats this. On 10% of rounds, the AI players pick a random action. On the remainder, they act according to their strategy.
Description
\@format A data frame with 2600 observations of 6 variables:
- id
(factor) Participant ID
- ai_strategy
(factor) Strategy adopted by AI player
- round
(numeric) Round number (between 1 and 50)
- human_action
(factor) Action taken by human (rock, paper, or scissors)
- ai_action
(factor) Action taken by AI (rock, paper, or scissors)
- score
(numeric) Outcome for human player, where 1 indicates a win, -1 a loss, and 0 a tie
Usage
rps
Format
An object of class data.frame
with 2600 rows and 6 columns.
Source
Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342. doi:10.1007/s42113-022-00133-6
Compute a sample mode
Description
sample_mode
computes the sample mode, i.e. the value in x
with the highest frequency of occurrence. If there are multiple modes, the mode that occurs first in x
is returned, with a warning that lists the other modes found.
Usage
sample_mode(x)
Arguments
x |
Numeric vector |
Value
A single numeric value equal to the sample mode
Examples
data(anchoring)
sample_mode(anchoring$everest_feet)
# Multiple modes give a warning:
sample_mode(c(3,3,3,1,1,1,2,2,2))
Compute the sample standard deviation
Description
sample_sd
computes the sample standard deviation, i.e. the square root of the sum of squared deviations of x
from the mean divided by the total number of observations. This is in contrast to sd
, which computes an unbiased estimate of the standard deviation (i.e. it divides the sum of squared deviations by n - 1).
Usage
sample_sd(x, na.rm = FALSE)
Arguments
x |
Numeric vector |
na.rm |
(logical) Should missing values be removed? |
Value
A single numeric value equal to the sample variance
Examples
data(anchoring)
sample_sd(anchoring$everest_feet)
Compute the sample variance
Description
sample_var
computes the sample variance, i.e. the sum of squared deviations of x
from the mean divided by the total number of observations. This is in contrast to var
, which computes an unbiased estimate of the variance (i.e. it divides the sum of squared deviations by n - 1).
Usage
sample_var(x, na.rm = FALSE)
Arguments
x |
Numeric vector |
na.rm |
(logical) Should missing values be removed? |
Value
A single numeric value equal to the sample variance
Examples
data(anchoring)
sample_var(anchoring$everest_feet)
Speed dating
Description
A subset of cases (wave 6-9) and variables (see below) from an experiment on speed dating. by Columbia Business School professors Ray Fisman and Sheena Iyengar for their paper Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.
Usage
speeddate
Format
A data frame with 1562 rows and 32 variables:
- iid
(numeric) unique ID variable of participant
- pid
(numeric) unique ID variable of date partner
- gender
(character) gender of participant (male or female)
- age
(numeric) age in years
- date_like
(numeric) how much they like their date partner in general (between 1 and 10)
- other_like
(numeric) how much their date partner likes them (between 1 and 10)
- date_want
do they want to go on another date with their date partner? (1 = yes, 0 = no)
- other_want
does their date partner want to go on another date with them? (1 = yes, 0 = no)
- match
do they both want to go on another date with each other? (1 = yes, 0 = no)
- self_attr
how attractive do they think they are? (between 1 and 10)
- self_sinc
how sincere do they think they are? (between 1 and 10)
- self_intel
how intelligent do they think they are? (between 1 and 10)
- self_fun
how much fun do they think they are? (between 1 and 10)
- self_amb
how ambitious do they think they are? (between 1 and 10)
- other_attr,other_sinc,other_intel,other_fun,other_amb
how attractive etc does their date partner think they are? (between 1 and 10)
- other_shar
how much does their date partner think they share hobbies and interests? (between 1 and 10)
- date_attr,date_sinc,date_intel,date_fun,date_amb,date_shar
how do they rate their date partner's attractiveness etc? (between 1 and 10)
- self_imp_attr,self_imp_sinc,self_imp_intel,self_imp_fun,self_imp_amb,self_imp_shar
how important do they find attractiveness etc in a partner? (between 1 and 10)
- other_imp_attr,other_imp_sinc,other_imp_intel,other_imp_fun,other_imp_amb,other_imp_shar
how important does their date partner find attractiveness etc? (between 1 and 10)
Source
Kaggle https://www.kaggle.com/annavictoria/speed-dating-experiment
Tetris and intrusive memories
Description
Tetris and intrusive memories
Usage
tetris2015
Format
A data frame with 72 rows and 28 variables:
- Condition
(factor) Condition: Control, Tetris_Reactivation, Tetris, or Reactivation
- Time_of_Day
Time of day participant commenced experiment, either "morning” or “afternoon”
- BDI_II
Beck Depression Inventory-II (BDI-II): Total score
- STAI_T
Spielberger State-Trait Anxiety Trait scale (STAI): Total score
- pre_film_VAS_Sad
Self-rated level of Sadness: Pre-film VAS mood. VAS = visual analogue scale. All VAS mood scales anchored from “not at all” to “extremely” in response to the question “Right at this very moment I am feeling”. Composite for pre-film mood calculated by summing the six pre-film VAS mood ratings
- pre_film_VAS_Hopeless
Self-rated level of Hopelessness: Pre-film VAS mood
- pre_film_VAS_Depressed
Self-rated level of Depressed: Pre-film VAS mood
- pre_film_VAS_Fear
Self-rated level of Fear: Pre-film VAS mood
- pre_film_VAS_Horror
Self-rated level of Horror: Pre-film VAS mood
- pre_film_VAS_Anxious
Self-rated level of Anxiousness: Pre-film VAS mood
- post_film_VAS_Sad
Self-rated level of Sadness: Post-film VAS mood. Composite for post-film mood calculated by summing the six post-film VAS mood ratings
- post_film_VAS_Hopeless
Self-rated level of Hopelessness: Post-film VAS mood
- post_film_VAS_Depressed
Self-rated level of Depressed: Post-film VAS mood
- post_film_VAS_Fear
Self-rated level of Fear: Post-film VAS mood
- post_film_VAS_Horror
Self-rated level of Horror: Post-film VAS mood
- post_film_VAS_Anxious
Self-rated level of Anxious: Post-film VAS mood
- Attention_Paid_to_Film
Attention paid to the film rating: How much attention did you pay to the film from 0-not at all to 10-extremely
- Post_film_Distress
Post film distress rating: How distressing did you find the film from 0-not at all to 10-extremely
- Day_Zero_Number_of_Intrusions
Day 0: Number of image-based intrusive memories in the Intrusion Diary (pre-intervention)
- Days_One_to_Seven_Number_of_Intrusions
Days 1-7: Number of image-based intrusive memories in the Intrusion Diary (post-intervention)
- Visual_Recognition_Memory_Test
Visual recognition memory test score: Number of correct responses (out of 22)
- Verbal_Recognition_Memory_Test
Verbal recognition memory test score: Number of correct responses (out of 32)
- Number_of_Provocation_Task_Intrusions
Intrusion Provocation Task (IPT): Number of image-based intrusive memories during 2min laboratory task on Day 7
- Diary_Compliance
Diary compliance rating - indicate how accurate you think your diary is from 1 - not at all accurate to 10 extremely
- IES_R_Intrusion_subscale
Impact of Event Scale-Revised (IES-R): Intrusion Subscale
- Tetris_Total_Score
Tetris game play computer score total - cumulative (sum of all games). Only participants who played Tetris have data relating to Tetris_Total_Score
- Self_Rated_Tetris_Performance
Self-rated Tetris performance: How difficult or easy did you find the game you just played. Only participants who played Tetris have data relating to Self_Rated_Tetris_Performance.
- Tetris_Demand_Rating
Demand rating: How much did you think Tetris after a distressing film would increase or decrease intrusive memories of the film: -10: extremely decrease, to +10: extremely increase
Source
https://osf.io/ideta/. See also James et al., 'Computer Game Play Reduces Intrusive Memories of Experimental Trauma via Reconsolidation-Update Mechanisms'.
Trump votes in 2016 for 50 US states and the District of Columbia
Description
Trump votes in 2016 for 50 US states and the District of Columbia
Usage
trump2016
Format
A data frame with 4632 rows and 5 variables:
- state
Name of the state
- hate_groups
Number of hate groups in the state in 2016 as reported by the Southern Poverty Law Center (https://www.splcenter.org/hate-map)
- population
Number of citizens in the state in 2016
- hate_groups_per_million
Number of hate groups per million citizens
- percent_bachelors_degree_or_higher
Percentage of citizens with a bachelor's degree of higher
- percent_in_poverty
Percentage of citizens below the poverty threshold
- percent_Trump_votes
Percentage of votes for Trump in the 2016 elections
Source
CSI Without Dead Bodies "Hate Groups and Trump's Vote%: Predictive effect present when education and poverty are considered" https://web.archive.org/web/20210414051437/https://www.csiwithoutdeadbodies.com/2017/02/hate-groups-and-trumps-vote-predictive.html
Predictions by Paul the Octopus in the 2008 UEFA Cup.
Description
A dataset containing the predictions and outcomes of matches in the 2008 UEFA European Cup.
Usage
uefa2008
Format
A data frame with 6 rows and 4 variables:
- Match
countries playing
- Prediction
country predicted to win
- Result
score at the end of the match
- Outcome
whether Paul was correct or incorrect