R/tools_documentation.R

In rcalinjageman/esci: Estimation Statistics with Confidence Intervals

#' jesci_document_object <- function(estimate) {
#'   tdoc <- NULL
#'   for (x in 1:length(estimate)) {
#'     if (is(estimate[[x]], "data.frame")) {
#'       tdoc <- paste(
#'         tdoc,
#'         "#' - **",
#'         names(estimate)[[x]],
#'         "**\n",
#'         sep = ""
#'       )
#'       for (mycol in colnames(estimate[[x]])) {
#'         tdoc <- paste(
#'           tdoc,
#'           "#'     - *",
#'           mycol,
#'           "* - \n",
#'           sep = ""
#'         )
#'       }
#'     }
#'   }
#'   cat(tdoc)
#' }
#'
#'
#'
#' jesci_document_data <- function(save_files = FALSE) {
#'
#' descriptions <- list()
#' sources <- list()
#'
#' descriptions["data_altruism_happiness"] <-
#' "#' Happiness may not be important just for the person feeling it; happiness may
#' #' also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)
#' #' examined this idea by collecting data on U.S. states. A Gallup poll in 2010
#' #' was used to measure each state's well-being index, a measure of mean
#' #' happiness for the state's residents on a scale from 0 to 100. Next, a kidney
#' #' donation database for 1999-2010 was used to figure out each state's rate
#' #' (number of donations per 1 million people) of non-directed kidney
#' #' donations-that's giving one kidney to a stranger, an extremely generous and
#' #' altruistic thing to do!
#' "
#'
#' sources$data_altruism_happiness <-
#'   "https://journals.sagepub.com/doi/full/10.1177/0956797613516148"
#'
#' descriptions["data_anchor_estimate_ma"] <-
#' "#' To what extent does the wording of a question influence one's judgment? In
#' #' their classic study, Jacowitz and Kahneman (1995) asked participants to
#' #' estimate how many babies are born each day in the United States. Participants
#' #' were given either a low anchor (more than 100 babies/day) or a high anchor
#' #' (less than 50,000 babies/day). Those who saw the low anchor estimated many
#' #' fewer births/day than those who saw the high anchor, which suggests that the
#' #' wording can have a profound influence. The correct answer, as it happens, is
#' #' ~11,000 births/day in 2014. To investigate the extent that these results are
#' #' replicable, the Many Labs project repeated this classic study at many
#' #' different labs around the world. You can find the summary data for 30 of
#' #' these labs in the Anchor Estimate ma data file
#' "
#'
#' sources$data_anchor_estimate_ma <-
#'   "https://econtent.hogrefe.com/doi/10.1027/1864-9335/a000178"
#'
#'
#' descriptions["data_basol_badnews"] <-
#' "#' Climate change? Vaccines? Fake news and conspiracy theories on these and
#' #' numerous other issues can be highly damaging, but are thriving in this social
#' #' media age. Trying to debunk a conspiracy theory by presenting facts and
#' #' evidence often doesn't work, alas. Psychological inoculation, also similar to
#' #' prebunking, presents a mild form of misinformation, preferably with
#' #' explanation, in the hope of building resistance to real-life fake news-a sort
#' #' of vaccine for fake news. The Bad News game is a spin-off from research on
#' #' psychological inoculation. Basol et al. (2020) assessed the possible
#' #' effectiveness of this game as a fake news vaccine. At getbadnews.com you can
#' #' click 'About' for information, or just start playing the game- it's easy and
#' #' maybe even fun. You encounter mock Twitter (now X) fake news messages that
#' #' illustrate common strategies for making fake news memorable or believable.
#' #' You make choices between messages and decide which ones to 'forward' as you
#' #' try to spread fake news while building your credibility score and number of
#' #' 'followers'-rather like real life for a conspiracy theorist wanting to spread
#' #' the word. Compete with your friends for credibility and number of followers.
#' #' Basol's online participants first saw 18 fictitious fake news tweets and
#' #' rated each for reliability (accuracy, believability), and also rated their
#' #' confidence in that reliability rating. Both ratings were on a 1 to 7 scale.
#' #' Those in the BadNews group then played the game for 15 minutes, whereas those
#' #' in the Control group played Tetris. Then all once again gave reliability and
#' #' confidence ratings for the 18 tweets.
#' "
#'
#' sources$data_basol_badnews <-
#'   "https://journalofcognition.org/articles/10.5334/joc.91"
#'
#' descriptions$data_bem_psychic <-
#' "#' Daryl Bem was an experienced mentalist and research psychologist, who, a
#' #' decade earlier, had been one of several outside experts invited to scrutinize
#' #' the laboratory and experimental procedures of parapsychology researcher
#' #' Charles Honorton. Bem not only judged them adequate, but joined the research
#' #' effort and became a coauthor with Honorton. Bem and Honorton (1994) first
#' #' reviewed early ganzfeld studies and described how the experimental procedure
#' #' had been improved to reduce the chance that results could be influenced by
#' #' various possible biases, or leakages of information from sender to receiver.
#' #' For example, the randomization procedure was carried out automatically by
#' #' computer, and all stimuli were presented under computer control. Bem and
#' #' Honorton then presented data from studies conducted with the improved
#' #' procedure. Table 13.1 presents basic data from 10 studies reported by Bem and
#' #' Honorton (1994). Participants each made a single judgment, so in Pilot 1, for
#' #' example, 22 participants responded, with 8 of them giving a correct response.
#' #' Three pilot studies helped refine the procedures, then four studies used
#' #' novice receivers. Study 5 used 20 students of music, drama, or dance as
#' #' receivers, in response to suggestions that creative people might be more
#' #' likely to show telepathy. Studies 6 and 7 used receivers who had participated
#' #' in an earlier study. The proportion of hits expected by chance is .25, and
#' #' Table 13.1 shows that all but Study 1 found proportions higher than .25.
#' "
#'
#' sources$data_bem_psychic <-
#'   "https://psycnet.apa.org/record/1994-20286-001"
#'
#'
#' descriptions$data_bodywellf <-
#' "#' A subset of data_bodywell_fm, reports only for those participants
#' #' who identified as female.  Data is Subjective Wellbeing and Body Satisfaction.
#' "
#'
#' descriptions$data_bodywellm <-
#' "#' A subset of data_bodywell_fm, reports only for those participants
#' #' who identified as male.  Data is Subjective Wellbeing abd Body Satisfaction.
#' "
#'
#' descriptions$data_bodywellfm <-
#' "#' Survey data from a convenience sample Dominican University students.
#' #' Reported are measures of Subjective Wellbeing abd Body Satisfaction.
#' "
#'
#' descriptions$data_campus_involvement <-
#' "
#' #' Clinton conducted a survey of college students to determine the extent to
#' #' which subjective well-being is related to campus involvement (Campus
#' #' Involvement data set on the book website). Participants completed a measure
#' #' of subjective well-being (scale from 1 to 5) and a measure of campus
#' #' involvement (scale from 1 to 5). Participants also reported gender (male or
#' #' female) and commuter status (resident or commuter).  Synthetic data simulated
#' #' to mimic survey data from a class project.
#' "
#'
#' descriptions$data_chap_8_paired_ex_8.18 <- "
#' #' *Fictitious* data from an unrealistically small HEAT
#' #' study comparing scores for a single group of students before and after a
#' #' workshop on climate change.
#' "
#'
#' descriptions$data_clean_moral <- "
#' #' Some researchers claim that moral judgments are based not only on rational
#' #' considerations but also on one's current emotional state. To what extent can
#' #' recent emotional experiences influence moral judgments? Schnall et al. (2008)
#' #' examined this question by manipulating feelings of cleanliness and purity and
#' #' then observing the extent that this changes how harshly participants judge
#' #' the morality of others. Inscho Study 1, Schnall et al. asked participants to
#' #' complete a word scramble task with either neutral words (neutral prime) or
#' #' words related to cleanliness (cleanliness prime). All students then completed
#' #' a set of moral judgments about controversial scenarios: Moral judgment is the
#' #' average of six items, each rated on a scale from 0 to 9, with high meaning
#' #' harsh. The data from this study are in the Clean moral file, which also
#' #' contains data from a replication by Johnson et al. (2014)
#' #'
#' "
#'
#' sources$data_clean_moral <-
#' "https://econtent.hogrefe.com/doi/full/10.1027/1864-9335/a000186"
#'
#' descriptions$data_college_survey_1 <-
#' "#' Data from a survey of Dominican University students; reports various
#' #' psychological and behavioral measures.
#' "
#'
#' descriptions$data_college_survey_1 <-
#' "#' Data from an additional survey of Dominican University students; reports
#' #' various psychological and behavioral measures.
#' "
#'
#' descriptions$damischrcj <-
#' "#' Damisch et al. (2010) investigated the possible effect of superstition on
#' #' performance. Their first study (Damisch 1) used a golf putting task. Students
#' #' in the experimental group were told they were using a lucky ball (the Lucky
#' #' group).  Calin-Jageman and Caldwell (2014) reported two studies designed to
#' #' replicate Damisch 1. The first (RCJ 1) followed Damisch 1 as closely as
#' #' practical, although the participants were American rather than German college
#' #' students. The second (RCJ 2) made several changes designed to increase any
#' #' effect of superstition on putting performance.
#' "
#'
#' sources$damischrcj <-
#'   "https://econtent.hogrefe.com/doi/full/10.1027/1864-9335/a000190"
#'
#'
#' descriptions$data_effronraj_fakenews <-
#' "#' *Synthetic data* meant to represent Experiment 1 of Effron & Raj, 2020.
#' #' 138 U.S. adults, recruited in August 2018 on Prolific Academic, worked
#' #' online. First, they saw six fake headlines four times, each time being asked
#' #' to rate how interesting/engaging/ funny/well-written the headline was. This
#' #' rating task simply ensured that the participants paid some attention to each
#' #' headline. The stimuli were 12 actual fake-news headlines about American
#' #' politics, with accompanying photographs. Half appealed to Republicans and
#' #' half to Democrats. Later, 12 fake headlines were presented one at a time, a
#' #' random mix of the six Old headlines-those seen before-and six New headlines
#' #' not seen previously. It was stated very clearly that independent,
#' #' non-partisan fact-checking had established that all the headlines were not
#' #' true. Participants first rated, on a 0 (not at all) to 100 (extremely) scale,
#' #' the degree to which to which they judged it unethical to publish that
#' #' headline. That's the Unethicality DV. They also rated how likely they would
#' #' be to share the headline if they saw it posted by an acquaintance on social
#' #' media; there were three further similar ratings. Finally, they rated how
#' #' accurate they believed the headline to be.
#' "
#'
#' sources$data_effronraj_fakenews <-
#'   "https://journals.sagepub.com/doi/full/10.1177/0956797619887896"
#'
#'
#'
#' descriptions$data_emotion_heartrate <-
#'   "#' Anger is a powerful emotion. To what extent can feeling angry actually change
#' #' your heart rate? To investigate, Lakens (2013) asked students to record their
#' #' heart rate (in beats per minute) at rest before (baseline) and then while
#' #' recalling a time of intense anger. This is a conceptual replication of a
#' #' classic study by Ekman et al. (1983). Load the Emotion heartrate data set
#' #' from the book website.
#' "
#'
#' sources$data_emotion_heartrate <-
#'   "https://ieeexplore.ieee.org/abstract/document/6464255"
#'
#'
#' descriptions$data_exam_scores <-
#'   "
#' #' To what extent does initial performance in a class relate to performance on a
#' #' final exam? First exam and final exam scores for nine students enrolled in an
#' #' introductory psychology course. Exam scores are percentages, where 0 = no
#' #' answers correct and 100 = all answers correct.
#' #' Data is synthetic to represent patterns found in a previous psych
#' #' stats course.
#' "
#'
#' descriptions$data_flag_priming_ma <-
#'   "
#' #' To what extent does being exposed to the American flag influence political
#' #' attitudes? One seminal study (Carter et al., 2011) explored this issue by
#' #' subtly exposing participants either to images of the American flag or to
#' #' control images. Next, participants were asked about their political
#' #' attitudes, using a 1-7 rating scale where high scores indicate conservative
#' #' attitudes. Participants exposed to the flag were found to express
#' #' substantially more conservative attitudes. The Many Labs project replicated
#' #' this finding at 25 different locations in the United States.
#' "
#'
#' sources$data_flag_priming_ma <-
#'   "https://openpsychologydata.metajnl.com/articles/10.5334/jopd.ad"
#'
#'
#' descriptions$data_gender_math_iat <-
#' "#' To what extent do men and women differ in their attitudes towards
#' #' mathematics? To investigate, Nosek et al. (2002) asked male and female
#' #' students to complete an Implicit Association Test (IAT)-this is a task
#' #' designed to measure a participant's implicit (non-conscious) feelings towards
#' #' a topic. (If you've never heard of the IAT, try it out here:
#' #' tiny.cc/harvardiat) On this IAT, students were tested for negative feelings
#' #' towards mathematics and art. Scores reflect the degree to which a student had
#' #' more negative implicit attitudes about mathematics than art (positive score:
#' #' more negative feelings about mathematics; 0: same level of negativity to
#' #' both; negative score: more negative feelings about art). data_gender_math_iat
#' #' has data from two labs that participated in a large-scale replication of the
#' #' original study (Klein et al., 2014a, 2014b)
#' "
#' sources$data_gender_math_iat <-
#'   "https://psycnet.apa.org/fulltext/2014-20922-002.html"
#'
#' descriptions$data_gender_math_iat_ma <-
#' "#' In EOC Exercise 4 in Chapter 7 we encountered the classic study of Nosek et
#' #' al. (2002), in which male and female participants completed an Implicit
#' #' Association Test (IAT) that measured the extent of negative attitudes towards
#' #' mathematics, compared with art. The study found that women, compared with
#' #' men, tended to have more negative implicit attitudes towards mathematics. The
#' #' Many Labs project repeated this study at locations around the world (Klein et
#' #' al., 2014a, 2014b). Summary data for 30 of these labs are available in Gender
#' #' math IAT ma. Higher scores indicate more implicit bias against mathematics.
#' #' See also data_gender_math_iat for raw data from two specific sites from this
#' #' replication effort.
#' "
#'
#' sources$data_gender_math_iat_ma <-
#'   "https://psycnet.apa.org/fulltext/2014-20922-002.html"
#'
#'
#' descriptions$data_halagappa <-
#' "
#' #' Could eating much less delay Alzheimer's? If so, that would be great news.
#' #' Halagappa et al. (2007) investigated the possibility by using a mouse model,
#' #' meaning they used Alzheimer-prone mice, which were genetically predisposed to
#' #' develop neural degeneration typical of Alzheimer's. The researchers used six
#' #' independent groups of mice, three tested in mouse middle age when 10 months
#' #' old, and three in mouse old age when 17 months. At each age there was a
#' #' control group of normal mice that ate freely (the NFree10 and NFree17
#' #' groups), a group of Alzheimer-prone mice that also ate freely (the AFree10
#' #' and AFree17 groups), and another Alzheimer-prone group restricted to 40% less
#' #' food than normal (the ADiet10 and ADiet17 groups). Table 14.2 lists the
#' #' factors that define the groups, and group labels. I'll discuss one measure of
#' #' mouse cognition: the percent time spent near the target of a water maze, with
#' #' higher values indicating better learning and memory. Table 14.2 reports the
#' #' means and standard deviations for this measure, and group sizes.
#' "
#'
#' sources$data_halagappa <-
#'   "https://www.sciencedirect.com/science/article/abs/pii/S0969996106003251"
#'
#'
#' descriptions$data_home_prices <-
#' "
#' #' Maybe you're thinking about buying a house after college? Regression can help
#' #' you hunt for a bargain. Download the Home Prices data set. This file contains
#' #' real estate listings from 1997 to 2003 in a city in California. Let's explore
#' #' the extent to which the size of the home (in square meters) predicts the sale
#' #' price.
#' "
#'
#' descriptions$data_kardas_expt_3 <-
#' "
#' #' Suppose you want to change the battery in your phone, cook the perfect
#' #' souffle, or perform a three-ball juggle. Just as numerous people do every
#' #' day, you might search online to find a video that shows what to do. Suppose
#' #' you watch such a video just once. First question: How well would you then
#' #' predict you could perform the task? Second question: How well would you
#' #' actually perform the task, the first time you tried? Now suppose you watch
#' #' the video many times: Again consider the two questions. These questions were
#' #' investigated in a series of studies by Kardas and O'Brien (2018). Let's first
#' #' do some quick analyses of Kardas Experiments 3 and 4-let's call them Expt 3
#' #' and Expt 4-focusing on the effect of watching a video many times rather than
#' #' once. In Expt 3, participants first watched a brief video of a person
#' #' performing the moonwalk. The Low Exposure group watched the video once, the
#' #' High Exposure group 20 times. Then participants predicted, on a 1 to 10
#' #' scale, how well they felt they would be able to perform the moonwalk
#' #' themselves. Finally, they attempted a single performance of the moonwalk,
#' #' which was videoed. These videos were rated, on the same 1 to 10 scale, by
#' #' independent raters.
#' "
#'
#' sources$data_kardas_expt_3 <-
#'   "https://journals.sagepub.com/doi/abs/10.1177/0956797617740646"
#'
#'
#' descriptions$data_kardas_expt_4 <-
#' "#' Suppose you want to change the battery in your phone, cook the perfect
#' #' souffle, or perform a three-ball juggle. Just as numerous people do every
#' #' day, you might search online to find a video that shows what to do. Suppose
#' #' you watch such a video just once. First question: How well would you then
#' #' predict you could perform the task? Second question: How well would you
#' #' actually perform the task, the first time you tried? Now suppose you watch
#' #' the video many times: Again consider the two questions. These questions were
#' #' investigated in a series of studies by Kardas and O'Brien (2018). Let's first
#' #' do some quick analyses of Kardas Experiments 3 and 4-let's call them Expt 3
#' #' and Expt 4-focusing on the effect of watching a video many times rather than
#' #' once. Expt 4 was conducted online with participants recruited from Amazon's
#' #' Mechanical Turk, who are typically more diverse than students. The online
#' #' task was based on a mirror-drawing game developed by Bob and students (Cusack
#' #' et al., 2015, tiny.cc/bobmirrortrace). Participants first read a description
#' #' of the game and the scoring procedure. To play, you use your computer
#' #' trackpad to trace a target line, as accurately and quickly as you can. The
#' #' task is tricky because you can see only a mirror image of the path you are
#' #' tracing with a finger on the trackpad. A running score is displayed. The
#' #' final score is the percentage match between the target line and the path you
#' #' traced, so scores can range from 0 to 100
#' "
#'
#' sources$data_kardas_expt_4 <-
#'   "https://journals.sagepub.com/doi/abs/10.1177/0956797617740646"
#'
#'
#' descriptions$data_labels_flavor <-
#' "#' To what extent do brand labels influence perceptions of a product? To
#' #' investigate, participants were asked to participate in a taste test. All
#' #' participants were actually given the same grape juice, but one glass was
#' #' poured from a bottle labeled 'Organic' and the other glass from a bottle
#' #' labeled 'Generic'. After each tasting (in counterbalanced order),
#' #' participants were asked to rate how much they enjoyed the juice on a scale
#' #' from 1 (not at all) to 10 (very much). Participants were also asked to say
#' #' how much they'd be willing to pay for a large container of that juice on a
#' #' scale from $1 to $10. Load the Labels flavor data set from the book website.
#' #' These data were collected as part of a class project by Floretta-Schiller et
#' #' al. (2015), whose work was inspired by a very clever study looking at the
#' #' effects of fast-food wrappers on children's enjoyment of food (Robinson et
#' #' al., 2007).
#' "
#'
#'
#' descriptions$data_latimier_3groups <-
#' "
#' #' The researchers were interested in how different study approaches might
#' #' impact learning. Working in France, they created three independent groups,
#' #' each comprising 95 adults. Participants worked online through seven learning
#' #' modules about DNA. The Reread group worked through a module, then worked
#' #' through it a second time before going on to the next module. The Quiz group
#' #' worked through a module, then had to complete a quiz before going on to the
#' #' next module. The Prequiz group had to work through the quiz before seeing the
#' #' presentation of a module, then went on to the quiz and presentation of the
#' #' next module. Participants received feedback and a brief explanation after
#' #' answering each question in a quiz, and could take as long as they wished to
#' #' work through each module and quiz. Seven days later, participants completed a
#' #' final test.
#' #' data_latimier_3groups is the full data set.
#' #' To facilitate different student exercises, there are also separate data
#' #' entities for each group (data_latimier_prequiz, data_latimier_reread, etc.),
#' #' and for every *pair* of groups (data_latimier_quiz_prequiz, etc.).
#' "
#'
#' sources$data_latimier_quiz <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#' descriptions$data_latimier_prequiz <- "
#' #' Just the Prequiz group from Latimier et al., 2019
#' #' See full details in data_latimier_3_groups
#' "
#'
#' sources$data_latimier_prequiz <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#' descriptions$data_latimier_quiz_prequiz <- "
#' #' Just the Quiz (RQ) an Prequiz (QR) groups from Latimier et al., 2019
#' #' See full details in data_latimier_3_groups
#' "
#'
#' sources$data_latimier_quiz_prequiz  <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#'
#' descriptions$data_latimier_quiz <- "
#' #' Just the Quiz group from Latimier et al., 2019
#' #' See full details in data_latimier_3_groups
#' "
#'
#' sources$data_latimier_quiz <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#'
#' descriptions$data_latimier_reread_prequiz <- "
#' #' Just the Reread (RR) an Prequiz (QR) groups from Latimier et al., 2019
#' #' See full details in data_latimier_3_groups
#' "
#'
#' sources$data_latimier_reread_prequiz <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#'
#' descriptions$data_latimier_reread_quiz <- "
#' #' Just the Reread Quiz groups from Latimier et al., 2019
#' #' See full details in data_latimier_3_groups
#' "
#'
#' sources$data_latimier_reread_quiz <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#'
#' descriptions$data_latimier_reread <- "
#' #' Just the Reread group from Latimier et al., 2019
#' #' See full details in data_latimier_3_groups
#' "
#'
#' sources$data_latimier_reread <-
#'   "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF"
#'
#'
#' descriptions$data_macnamara_r_ma <-
#' "#' Is genius born or made? Could any of us be Michael Jordan or Mozart if we
#' #' worked sufficiently hard to develop the requisite skills? Meta-analysis of
#' #' correlations can help answer such questions. The issue here is the extent
#' #' that practice and effort may be sufficient for achieving the highest levels
#' #' of expertise. Ericsson et al. (1993) argued that years of effort is what
#' #' matters most: 'Many characteristics once believed to reflect innate talent
#' #' are actually the result of intense practice extended for a minimum of 10
#' #' years' (p. 363). This view was enormously popularized by Malcolm Gladwell
#' #' (2008), who argued in his book Outliers that 10,000 hours of focused practice
#' #' is the key to achieving expertise. However, this view is now being
#' #' challenged, with one important contribution being a large meta-analysis of
#' #' correlations between amount of intense practice and level of achievement:
#' #' Macnamara et al. (2014) combined 157 correlations reported in a wide range of
#' #' fields, from sports to music and education, and found correlation of only r =
#' #' .35 (.30, .39). Table 11.1 shows the 16 main correlations for music, from
#' #' Macnamara et al. (2014).
#' "
#'
#' sources$data_macnamara_r_ma <-
#'   "https://journals.sagepub.com/doi/full/10.1177/0956797614535810"
#'
#'
#' descriptions$data_mccabemichael_brain <-
#' "#' You've probably seen cross sections of the brain with
#' #' brightly colored areas indicating which brain regions are most active during
#' #' a particular type of cognition or emotion. Search online for fMRI (functional
#' #' magnetic resonance imaging) brain scans to see such pictures and learn how
#' #' they are made. They can be fascinating-are we at last able to see how
#' #' thinking works? In 2008, McCabe and Castel published studies that
#' #' investigated how adding a brain picture might alter judgments of the
#' #' credibility of a scientific article. For one group of participants, an
#' #' article was accompanied by a brain image that was irrelevant to the article.
#' #' For a second, independent group, there was no image. Participants read the
#' #' article, then gave a rating of the statement 'The scientific reasoning in the
#' #' article made sense'. The response options were 1 (strongly disagree), 2
#' #' (disagree), 3 (agree), and 4 (strongly agree). The researchers reported that
#' #' mean ratings were higher with a brain picture than without, but that the
#' #' difference was small. It seemed that an irrelevant brain picture may have
#' #' some, but only a small influence. The authors drew appropriately cautious
#' #' conclusions, but the result quickly attracted attention and there were many
#' #' media reports that greatly overstated it. At least according to the popular
#' #' media, it seemed that adding a brain picture made any story convincing.
#' #' Search on 'McCabe seeing is believing', or similar, to find media reports and
#' #' blog posts. Some warned readers to watch out for brain pictures, which, they
#' #' said, can trick you into believing things that aren't true. The result
#' #' intrigued some New Zealander colleagues of mine who discovered that, despite
#' #' its wide recognition, the finding hadn't been replicated. They ran
#' #' replication studies using the materials used by the original researchers, and
#' #' found generally small ESs. I joined the team at the data analysis stage and
#' #' the research was published (Michael et al., 2013). I'll discuss here a
#' #' meta-analysis of two of the original studies and eight replications by our
#' #' team. The studies were sufficiently similar for meta-analysis, especially
#' #' considering that all the Michael studies were designed to have many features
#' #' that matched the original studies.  This data set does *not* include
#' #' two additional critique studies run by the Michael team.  See also
#' #' data_mccabemichael_brain2
#' "
#'
#' sources$data_mccabemichael_brain <-
#'   "https://link.springer.com/article/10.3758/s13423-013-0391-6"
#'
#'
#' descriptions$data_mccabemichael_brain2 <-
#' "#' Same as data_mccabemichael_brain but includes two additional critique studies
#' #' run by the Michael team.
#' "
#'
#' sources$data_mccabemichael_brain2 <-
#'   "https://link.springer.com/article/10.3758/s13423-013-0391-6"
#'
#'
#'
#' descriptions$data_meditationbrain <-
#' "#' My example is a well-known study of mindfulness meditation by Holzel et al.
#' #' (2011). People who wanted to reduce stress, and were not experienced
#' #' meditators, were assigned to a Meditation (n = 16) or a Control (n = 17)
#' #' group. The Meditation group participated in 8 weeks of intensive training and
#' #' practice of mindfulness meditation. The researchers used a questionnaire to
#' #' assess a range of emotional and cognitive variables both before (Pretest) and
#' #' after (Posttest) the 8-week period. All assessment was conducted while the
#' #' participants were not meditating. The study is notable for including brain
#' #' imaging to assess possible changes in participants' brains from Pretest to
#' #' Posttest. The researchers measured gray matter concentration, which increases
#' #' in brain regions that experience higher and more frequent activation. The
#' #' researchers expected that the hippocampus may be especially responsive to
#' #' meditation because it has been implicated in the regulation of emotion,
#' #' arousal, and general responsiveness. They therefore included in their planned
#' #' analysis the assessment of any changes to gray matter concentration in the
#' #' hippocampus.
#' "
#'
#' sources$data_meditationbrain <-
#'   "https://link.springer.com/chapter/10.1007/978-94-007-2079-4_9"
#'
#'
#' descriptions$data_organicmoral <-
#' "
#' #' To what extent might choosing organic foods make us morally smug? To
#' #' investigate, Eskine (2013) asked participants to rate images of organic food,
#' #' neutral (control) food, or comfort food. Next, under the guise of a different
#' #' study, all participants completed a moral judgment scale in which they read
#' #' different controversial scenarios and rated how morally wrong they judged
#' #' them to be (scale of 1-7, high judgments mean more wrong). Table 14.7 shows
#' #' summary data, which are also available in the first four variables in the
#' #' OrganicMoral file. In that file you can see two further variables, which
#' #' report full data-we'll come to these shortly. Here we use the summary data.
#' #' After the results of Eskine (2013) were published, Moery and Calin-Jageman
#' #' (2016) conducted a series of close replications. We obtained original
#' #' materials from Eskine, piloted the procedure, and preregistered our sampling
#' #' and analysis plan. The OSF page, osf.io/atkn7, has all the details. The data
#' #' from one of these close replications are in the last two variables of the
#' #' OrganicMoral file. For this replication study, group names are in the
#' #' variable ReplicationGroup and moral judgments in MoralJudgment. (You may need
#' #' to scroll right to see these variables.)
#' "
#'
#' sources$data_organicmoral <-
#'   "https://journals.sagepub.com/doi/full/10.1177/1948550616639649"
#'
#'
#'
#' descriptions$data_penlaptop1 <-
#' "#' % transcription scores from pen and laptop group of Meuller et al., 2014
#' "
#'
#' sources$data_penlaptop1 <-
#'   "https://journals.sagepub.com/doi/full/10.1177/0956797614524581"
#'
#'
#' descriptions$data_powerperformance_ma <-
#' "#' To what extent could feeling powerful affect your performance at motor
#' #' skills? To investigate, Burgmer and Englich (2012) assigned German
#' #' participants to either power or control conditions and then asked them to
#' #' play golf (Experiment 1) or darts (Experiment 2). They found that
#' #' participants manipulated to feel powerful performed substantially better than
#' #' those in the control condition. To study this finding further, Cusack et al.
#' #' (2015) conducted five replications in the United States. Across these
#' #' replications they tried different ways of manipulating power, different types
#' #' of tasks (golf, mirror tracing, and a cognitive task), different levels of
#' #' difficulty, and different types of participant pools (undergraduates and
#' #' online). Summary data from all seven studies are available in
#' #' PowerPerformance ma.
#' "
#'
#' sources$data_powerperformance_ma <-
#'   "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140806"
#'
#'
#' descriptions$data_rattanmotivation <-
#' "#' How do you think you would react to feedback that gave encouragement and
#' #' reassurance, or, instead, encouragement and challenge? Carol Dweck and her
#' #' colleagues have investigated many such questions about how people respond to
#' #' different types of feedback. My next example comes from Dweck's research
#' #' group and illustrates data analysis that starts with the full data, rather
#' #' than only summary statistics. Rattan et al. (2012) asked their college
#' #' student participants to imagine they were undertaking a mathematics course
#' #' and had just received a low score (65%) on the first test of the year.
#' #' Participants were assigned randomly into three groups, which received
#' #' different feedback along with the low score. The Comfort group received
#' #' positive encouragement and also reassurance, the Challenge group received
#' #' positive encouragement and also challenge, and the Control group received
#' #' just the positive encouragement. Participants then responded to a range of
#' #' questions about how they felt about the course and their professor. I'll
#' #' discuss data for their ratings of their own motivation toward mathematics,
#' #' made after they had received the feedback.
#' "
#'
#' sources$data_rattanmotivation <-
#'   "https://www.sciencedirect.com/science/article/pii/S0022103111003027"
#'
#'
#' descriptions$data_religionsharing <-
#' "#' To what extent is a religious upbringing related to prosocial behavior in
#' #' childhood? To investigate, a large international sample of children was asked
#' #' to play a game in which they were given 10 stickers but then asked if they
#' #' would give some of these stickers away to another child who had not been able
#' #' to be tested that day. The number of stickers donated was considered a
#' #' measure of altruistic sharing. In addition, the parents of each child
#' #' reported the family's religion. Summary data provided.
#' "
#'
#' descriptions$data_religious_belief <-
#' "#' Let's look at some data about religious beliefs. The Religious belief file
#' #' has data from a large online survey in which participants were asked to
#' #' report, on a scale from 0 to 100, their belief in the existence of God.  Age
#' #' was also reported.
#' "
#'
#' descriptions$data_selfexplain <-
#' "#' Self-explaining is a learning strategy where students write or say their own
#' #' explanations of the material they are studying. Self-explaining has generally
#' #' been found to be more effective than standard studying, but it may also take
#' #' more time. This raises the question of whether it's the study strategy or the
#' #' extra time that benefits learning. To explore this issue, grade school
#' #' children took a pretest of mathematics conceptual knowledge, studied
#' #' mathematics problems, and then took a similar posttest (McEldoon et al.,
#' #' 2013). Participants were randomly assigned to one of two study conditions:
#' #' normal study + more practice (More Practice group), or self-explaining
#' #' (Self-Explain group). The first condition was intended to make time spent
#' #' learning to be similar for the two groups. You can find part of the data from
#' #' this study in SelfExplain, the scores being percent correct.
#' "
#'
#' sources$data_selfexplain <-
#'   "https://bpspsychub.onlinelibrary.wiley.com/doi/abs/10.1111/j.2044-8279.2012.02083.x"
#'
#'
#' descriptions$data_simmonscredibility <-
#' "#' You're excited! Your company has developed a wonderful new weight-loss
#' #' program, and now it's your job to develop the ad campaign. Should you choose
#' #' a BeforeAfter pair of pictures, as in Figure 14.1, top panel? Or might a
#' #' Progressive sequence of pictures of the same person, as in the bottom panel,
#' #' be more effective? Pause, think, and discuss. Which would you choose, and
#' #' why? You might think that BeforeAfter is simpler and more dramatic. On the
#' #' other hand, Progressive highlights the steady improvement that you claim the
#' #' program will deliver. You're probably not surprised to learn that BeforeAfter
#' #' is used often and has long been a favorite of the advertising industry,
#' #' whereas Progressive is used only rarely. Luca Cian and colleagues (Cian et
#' #' al., 2020) were curious to know the extent to which BeforeAfter is actually
#' #' more effective, appealing, and credible than Progressive, or, indeed, whether
#' #' Progressive might score more highly. They reported seven studies of various
#' #' aspects of that question. I'll focus on their Study 2, in which they used
#' #' three independent groups to compare all three conditions illustrated in
#' #' Figure 14.1. The BeforeAfterInfo condition, in the middle panel, comprises
#' #' three BeforeAfter pairs, thus providing extra information about the before
#' #' and after endpoints. The researchers included this condition in case any
#' #' advantage of Progressive might stem simply from having more images, rather
#' #' than because it illustrates a clear progressive sequence. They randomly
#' #' assigned 213 participants from MTurk to one of the three groups. Participants
#' #' were asked to 'imagine that you have decided to lose some weight', then saw
#' #' one of the three ads for a weight loss program called MRMDiets. They then
#' #' answered the question 'How would you evaluate MRMDiets?' by choosing a 1-7
#' #' response on several scales, including Unlikeable-Likable,
#' #' Ineffective-Effective, and Not credible-Credible. The researchers averaged
#' #' six such scores to give an overall Credibility score, on the 1-7 scale, with
#' #' 7 being the most credible. Simmons and Nelson (2020) were sufficiently
#' #' intrigued to carry out two substantial very close replications. With the
#' #' cooperation of the original researchers, they used the same materials and
#' #' procedure. They used much larger groups and preregistered their research
#' #' plan, including data analysis plan. I'll focus on their first replication, in
#' #' which 761 participants from MTurk were randomized to the three groups.
#' "
#'
#' sources$data_simmonscredibility <-
#'   "http://datacolada.org/94"
#'
#'
#' descriptions$data_sleep_beauty <-
#' "#' Is there really such a thing as beauty sleep? To investigate, researchers
#' #' decided to examine the extent to which sleep relates to attractiveness. Each
#' #' of 70 college students self-reported the amount of sleep they had the night
#' #' before. In addition, a photograph was taken of each participant and rated for
#' #' attractiveness on a scale from 1 to 10 by two judges of the opposite gender.
#' #' The average rating score was used. You can download this data set (Sleep
#' #' Beauty) from the book website.
#' "
#'
#'
#' descriptions$data_stickgold <-
#'   "
#' #' Stickgold et al. (2000) found that, remarkably, performance on a visual
#' #' discrimination task actually improved over the 48-96 hours after initial
#' #' training, even without practice during that time. However, what if
#' #' participants were sleep deprived during that period? They trained 11
#' #' participants in that new skill, then all were sleep deprived. The data were
#' #' (-14.7, -10.7, -10.7, 2.2, 2.4, 4.5, 7.2, 9.6, 10, 21.3, 21.8)-or download
#' #' the Stickgold data set from the book website. The data are the changes in
#' #' performance scores from immediately after training to after the night without
#' #' sleep: 0 represents no change, positive scores represent improvement, and
#' #' negative scores represent decline. Data set courtesy of
#' #' DataCrunch (tiny.cc/Stickgold)
#' "
#'
#' sources$data_stickgold <- "https://www.statcrunch.com/app/index.html?dataid=1053539"
#'
#'
#' descriptions$data_studystrategies <-
#' "
#' #' To what extent does study strategy influence learning? To investigate,
#' #' psychology students were randomly assigned to three groups and asked to learn
#' #' biology facts using one of three different strategies: a) Self-Explain
#' #' (explaining for each fact what new knowledge is gained and how it relates to
#' #' what is already known), b) Elab Interrogation (elaborative interrogation:
#' #' stating for each fact why it makes sense), or c) Repetition Control (stating
#' #' each fact over and over). After studying, students took a 25-point
#' #' fill-the-blank test (O'Reilly et al., 1998)
#' "
#'
#' sources$data_studystrategies <-
#'   "https://www.sciencedirect.com/science/article/abs/pii/S0361476X97909772"
#'
#'
#' descriptions$data_thomason_1 <-
#' "#' Summary data from an unpublished study by Neil Thomason and colleagues, who
#' #' were interested in ways to enhance students' critical thinking. They were
#' #' investigating argument mapping, which is a promising way to use diagrams to
#' #' represent the structure of arguments. Students in their study completed an
#' #' established test of critical thinking (the Pretest), then a critical thinking
#' #' course based on argument mapping, then a second version of the test (the
#' #' Posttest).
#' "
#'
#' descriptions$data_videogameaggression <-
#' "#' Video games can be violent and they can also be challenging. To what extent
#' #' might these factors cause aggressive behavior? To explore, Hilgard (2015)
#' #' asked male participants to play one of four versions of a video game for 15
#' #' minutes. The game was customized so that it could vary in violence (shooting
#' #' zombies or helping aliens) and difficulty (targets controlled by tough AI or
#' #' dumb AI). After the game, players were provoked by being given an insulting
#' #' evaluation by a confederate. Participants then got to decide how long the
#' #' confederate should hold their hand in painfully cold ice water (between 0 and
#' #' 80 seconds), and this was taken as a measure of aggressive behavior. You can
#' #' find the materials and analysis plan for this study on the Open Science
#' #' Framework: osf. io/cwenz. This is a simplified version of the full data set.
#' "
#'
#' sources$data_videogameaggression <-
#'   "https://journals.sagepub.com/doi/10.1177/0956797619829688"
#'
#'
#' taglines <- data.frame(
#'   filenames = c(
#'     'Latimier Quiz.omv',
#'     'Latimier Reread.omv',
#'     'Latimier Prequiz.omv',
#'     'College survey 1.omv',
#'     'Religious belief.omv',
#'     'College survey 2.omv',
#'     'Stickgold.omv',
#'     'Latimier Reread Quiz.omv',
#'     'Latimier Quiz Prequiz.omv',
#'     'Latimier Reread Prequiz.omv',
#'     'Kardas Expt 3.omv',
#'     'Kardas Expt 4.omv',
#'     'Clean moral.omv',
#'     'Gender math IAT.omv',
#'     'Basol badnews.omv',
#'     'EffronRaj fakenews.omv',
#'     'Emotion heartrate.omv',
#'     'Labels flavor.omv',
#'     'McCabeMichael brain.omv',
#'     'McCabeMichael brain2.omv',
#'     'DamischRCJ.omv',
#'     'Anchor Estimate ma.omv',
#'     'Flag Priming ma.omv',
#'     'Gender math IAT ma.omv',
#'     'PowerPerformance ma.omv',
#'     'Thomason 1.omv',
#'     'Macnamara r ma.omv',
#'     'Exam Scores.omv',
#'     'Campus Involvement.omv',
#'     'Sleep Beauty.omv',
#'     'BodyWellFM.omv',
#'     'BodyWellM.omv',
#'     'BodyWellF.omv',
#'     'Home Prices.omv',
#'     'Altruism Happiness.omv',
#'     'Bem Psychic.omv',
#'     'SimmonsCredibility.omv',
#'     'RattanMotivation.omv',
#'     'Latimier 3Groups.omv',
#'     'Halagappa.omv',
#'     'StudyStrategies.omv',
#'     'ReligionSharing.omv',
#'     'OrganicMoral.omv',
#'     'SmithRecall.omv',
#'     'MeditationBrain.omv',
#'     'SelfExplain.omv',
#'     'VideogameAggression.omv'
#'   ),
#'   tags = c(
#'     'Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019)',
#'     'Latimier Reread - Ch03 - Reread group in Latimier et al. (2019)',
#'     'Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019)',
#'     'College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3',
#'     'Religious belief - Ch03 - for End-of-Chapter Exercise 3.5',
#'     'College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4',
#'     'Stickgold - Ch06 - from Stickgold et al. (2000)',
#'     'Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019)',
#'     'Latimier Quiz Prequiz - Ch07 - Quiz and Prequiz groups in Latimier et al. (2019)',
#'     'Latimier Reread Prequiz - Ch07 - Reread and Prequiz groups in Latimier et al. (2019)',
#'     "Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3",
#'     "Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4",
#'     'Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014)',
#'     'Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002)',
#'     'Basol badnews - Ch07 - from Basol et al. (2020)',
#'     'EffronRaj fakenews - Ch8 - from Effron and Raj (2020)',
#'     'Emotion heartrate - Ch8 - from Lakens (2013)',
#'     'Labels flavor - Ch8 - from Floretta-Schiller et al. (2015)',
#'     'McCabeMichael brain - Ch9 - from Michael et al. (2013)',
#'     'McCabeMichael brain2 - Ch9 - from Michael et al. (2013)',
#'     'DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014)',
#'     'Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995)',
#'     'Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011)',
#'     'Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002)',
#'     'PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015)',
#'     'Thomason 1 - Ch11 - from Thomason 1',
#'     'Macnamara r ma - Ch11 - from Macnamara et al. (2014)',
#'     'Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2',
#'     'Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7',
#'     'Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6',
#'     'BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1',
#'     'BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel',
#'     'BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel',
#'     'Home Prices - Ch12 - for End-of-Chapter Exercise 12.2',
#'     'Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014)',
#'     'Bem Psychic - Ch13 - from Bem and Honorton (1994)',
#'     'SimmonsCredibility - Ch14 - from Simmons and Nelson (2020)',
#'     'RattanMotivation - Ch14 - from Rattan et al. (2012)',
#'     'Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019)',
#'     'Halagappa - Ch14 - from Halagappa et al. (2007)',
#'     "StudyStrategies - Ch14 - from O'Reilly et al. (1998)",
#'     'ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3',
#'     'OrganicMoral - Ch14 - from Eskine (2013)',
#'     'SmithRecall - Ch15 - from Smith et al. (2016)',
#'     "MeditationBrain - Ch15 - from Holzel et al. (2011)",
#'     'SelfExplain - Ch15 - from McEldoon et al. (2013)',
#'     'VideogameAggression - Ch15 - from Hilgard (2015)'
#'   )
#' )
#'
#'
#'   #prep comments
#'
#'   tdoc <- NULL
#'
#'   badfiles <- c(
#'     NULL
#'   )
#'
#'   for (myfile in list.files(path = "./data", pattern="*.omv", full.names = TRUE)) {
#'
#'     if (! myfile %in% badfiles) {
#'
#'       # get the jamovi data
#'       f <- jmvReadWrite::read_omv(myfile)
#'       f_attribs <- f
#'       f[] <- lapply(f, c)
#'
#'       # make the name of the r data object
#'       thisfilename <- gsub("./data/", "", myfile)
#'
#'       dataname <- gsub("./data/", "", myfile)
#'       dataname <- gsub(".omv", "", dataname)
#'
#'       friendly_name <- dataname
#'       if (!is.null(taglines[taglines$filenames == thisfilename, ]$tags)) {
#'         friendly_name <- taglines[taglines$filenames == thisfilename, ]$tags
#'       }
#'
#'       dataname <- gsub(" ", "_", dataname)
#'       dataname <- tolower(dataname)
#'       dataname <- paste("data_", dataname, sep = "")
#'
#'       # Save rda
#'       if (save_files) {
#'         to_object <- paste(
#'           dataname, " <- f", sep = ""
#'         )
#'         save_rda <- paste(
#'           "usethis::use_data(", dataname, ", overwrite = TRUE)", sep = ""
#'         )
#'         eval(parse(text = to_object), envir = .GlobalEnv)
#'         eval(parse(text = save_rda), envir = .GlobalEnv)
#'
#'       }
#'
#'       # Build documents
#'       tdoc <- paste(
#'         tdoc,
#'         "#' ", friendly_name, "\n#' \n",
#'         sep = ""
#'       )
#'
#'       if (! is.null(descriptions[[dataname]])) {
#'         tdoc <- paste(
#'           tdoc,
#'           descriptions[[dataname]],
#'           "#'\n",
#'           sep = ""
#'         )
#'       }
#'
#'       tdoc <- paste(
#'         tdoc,
#'         "#' @format ## `", dataname, "`\n",
#'         "#' A data frame with ", nrow(f), " rows ",
#'         "and ", ncol(f), " columns:\n",
#'         sep = ""
#'       )
#'
#'
#'       tdoc <- paste(
#'         tdoc,
#'         "#' \\describe{\n",
#'         sep = ""
#'       )
#'
#'       for (mycol in colnames(f_attribs)) {
#'         if (is.null( attr(f_attribs$State, "jmv-desc") )) {
#'           coldesc <- class(f_attribs[[mycol]])
#'         } else {
#'           coldesc <- paste(
#'             class(f_attribs[[mycol]]),
#'             attr(f_attribs$State, "jmv-desc"),
#'             sep = " - "
#'           )
#'         }
#'
#'         tdoc <- paste(
#'           tdoc,
#'           "#'   \\item{", mycol, "}{", coldesc, "}\n",
#'           sep = ""
#'         )
#'       }
#'
#'       tdoc <- paste(
#'         tdoc,
#'         "#' }\n",
#'         sep = ""
#'       )
#'
#'       if (! is.null(sources[[dataname]])) {
#'         tdoc <- paste(
#'           tdoc,
#'           "#' @source <",
#'           sources[[dataname]],
#'           ">\n",
#'           sep = ""
#'         )
#'       }
#'
#'
#'       tdoc <- paste(
#'         tdoc,
#'         '"', dataname, '"\n\n',
#'         sep = ""
#'       )
#'
#'
#'     }
#'
#'
#'   }
#'
#'   write(tdoc, file = "./R/data.R")
#'
#' }