Publications by George
MB3 Robustness Analyses
# pulled from 06_main-analysis-mixed-model -- was there other preprocessing in 07_main-analysis_results we need?? merged_data <- read_csv("data/processed_data/03_merged_data_sans_exclusions.csv") ## Rows: 8043 Columns: 61 ## ── Column specification ────────────────────────────────�...
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Mars-IB overview
Here we analyze the by-item median correct RTs and accuracies provided from the original Chierchia et al. (2019) study of the MaRs-IB, in which 659 adults, adolescents, and children had 8 min to complete as many of the 80 MaRs-IB items as possible. Note that the items were presented in a fixed order, and many participants did not reach the end of ...
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Document
library(rugarch) Mut=read.csv("ACTUARIAL.CSV");Mut mydata=Mut$INFLATION;mydata M1=ugarchspec(variance.model = list(model="gjrGARCH",garchOrder=c(1,1)),mean.model = list(armaOrder=c(0,0),inlude.mean=F),distribution.model="ged");M1 M2=ugarchfit(mydata,spec=M1);M2 M3=ugarchforecast(M2);M3 plot(M3,which=4) R Markdown This is an R Markdown docum...
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FGarch model us
setwd("C:/Users/omond/OneDrive/Desktop/Nyabs") Nyabs=read.csv("ACTUARIAL.CSV");Nyabs ## TIME CPI PENSION Year Month INFLATION CLAIMS ## 1 2007-01 6.12 13.50 2018 November 5.58 4.59 ## 2 2007-02 6.33 13.59 2018 October 5.53 4.53 ## 3 2007-03 6.47 13.49 2018 September 5.70 4.53 ## 4 2007-04 ...
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CDI-CAT Japanese Production
Introduction Our goal is to develop and test via simulation a bank of CDI items and IRT parameters that can be used for a CDI-CAT in Japanese. Our approach is as follows: We first fit basic IRT models (1-parameter logistic (1PL; i.e. Rasch), 2PL, and 3PL) to CDI data and perform a model comparison. For the favored model, we then identify candidate...
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CDI-CAT Japanese Production
Introduction Our goal is to develop and test via simulation a bank of CDI items and IRT parameters that can be used for a CDI-CAT in Japanese`. Our approach is as follows: We first fit basic IRT models (1-parameter logistic (1PL; i.e. Rasch), 2PL, and 3PL) to CDI data and perform a model comparison. For the favored model, we then identify candidat...
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CDI-CAT Japanese Production Norms
Our goal is to produce estimated WG and WS raw scores for each ability level, so that we can produce norms tables for the CDI-CAT that are equivalent norms tables for the full WG and WS forms. These norms are based on all available Wordbank data, combined with data from Sho’s lab: 46 WG administrations and 509 WS administrations. (The same data w...
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Document
R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the...
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Norms tables for CDI-CAT-SP
Our goal is to produce estimated WG and WS raw scores for each ability level, so that we can produce norms tables for the CDI-CAT that are equivalent norms tables for the full WG and WS forms. These norms are based on all available Wordbank data: 407 WG administrations and 1092 WS administrations. (The same data we used to fit the IRT model.) For t...
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Building MH Swadesh lists
Just pick N items, either from the target language’s CDI:WS, or from English. load(here("data/apophenia.Rdata")) dat %>% ggplot(aes(x=N, y=mean_cor, color=language)) + facet_wrap(. ~ Random) + geom_line() + theme_bw() dat %>% group_by(N, Random) %>% summarise(mean_cor = mean(mean_cor)) %>% ggplot(aes(x=N, y=mean_cor, color=Random)) +...
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