guess adjusts estimates of learning for guessing related bias.

Description

It implements the method discussed in http://gsood.com/research/papers/guess.pdf

Simulated Responses to Knowledge Questions Without Don't Know

Description

Simulate Pre- and Post-Process Responses to Knowledge Questions without Don't Know

Simulate Pre- and Post-Process Responses to Knowledge Questions with Don't Know

Usage

alldat

alldat_dk

Format

A data frame with 500 rows and 4000 variables:

Simulated Responses to Knowledge Questions With Don't Know

Description

Simulate Pre- and Post-Process Responses to Knowledge Questions with Don't Know

Usage

dk_sim

Format

A data frame with 500 rows and 4000 variables:

Simulated Responses to Knowledge Questions With Don't Know

Description

Simulate Pre- and Post-Process Responses to Knowledge Questions with Don't Know

Usage

dk_sim_params

Format

A data frame with 1000 rows and 5 variables:

Constraints: Sum to 1

Description

Constraints that some params sum to 1. Used Internally. For data with DK. Functions for constraining lambdas to sum to 1 and to bound params between 0 and 1

Usage

eqn1(x, g1 = NA, data)

Arguments

Constraints: Sum to 1

Description

Constraints that some params sum to 1. Used Internally. For data with DK. Functions for constraining lambdas to sum to 1 and to bound params between 0 and 1

Usage

eqn1dk(x, g1 = NA, data)

Arguments

Goodness of fit statistics for data with don't know

Description

For data with Don't Know, chi-square goodness of fit between true and model based multivariate distribution

Usage

fit_dk(pre_test, pst_test, g, est.param, force9 = FALSE)

Arguments

Details

fit_nodk

Value

matrix with two rows: top row carrying chi-square value, and bottom row probability of observing that value

Examples

## Not run: fit_dk(pre_test, pst_test, g, est.param)

Goodness of fit statistics for data without don't know

Description

For data without Don't Know, chi-square goodness of fit between true and model based multivariate distribution

Usage

fit_nodk(pre_test, pst_test, g, est.param)

Arguments

Details

fit_nodk

Value

matrix with two rows: top row carrying chi-square value, and bottom row probability of observing that value

Examples

## Not run: fit_nodk(pre_test, pst_test, g, est.param)

guess_lik

Description

Likelihood function for data without Don't Know. Used Internally.

Usage

guess_lik(x, g1 = x[4], data)

Arguments

Bootstrapped standard errors of effect size estimates

Description

guess_stnderr

Usage

guess_stnderr(pre_test = NULL, pst_test = NULL, nsamps = 100,
  seed = 31415, force9 = FALSE)

Arguments

Value

list with standard error of parameters, estimates of learning, standard error of learning by item

Examples

pre_test <- data.frame(pre_item1=c(1,0,0,1,0), pre_item2=c(1,NA,0,1,0)) 
pst_test <- data.frame(pst_item1=pre_test[,1] + c(0,1,1,0,0), 
					  pst_item2 = pre_test[,2] + c(0,1,0,0,1))
## Not run: guess_stnderr(pre_test, pst_test, nsamps=10, seed = 31415)

guessdk_lik

Description

Likelihood function for data with Don't Know. Used Internally.

Usage

guessdk_lik(x, g1 = x[8], data)

Arguments

Calculate item level and aggregate learning

Description

guesstimate

Usage

guesstimate(transmatrix = NULL)

Arguments

Value

list with two items: parameter estimates and estimates of learning

Interleave

Description

Used Internally Interleave two character vectors. The output is a vector. The first entry is from the first vector. Vectors can be of different lengths. If one is shorter than the other, entries of unmatched longer vector are left un-interleaved.

Usage

interleave(vec1, vec2)

Arguments

Value

interleaved vector # t1 <- paste0("t1", letters[1:5]); t2 <- paste0("t2", letters[1:5]); interleave(t1, t2)

Creates a transition matrix for each item.

Description

Needs an 'interleaved' dataframe (see interleave function). Pre-test item should be followed by corresponding post-item item etc. Don't knows must be coded as NA. Function handles items without don't know responses. The function is used internally. It calls transmat.

Usage

multi_transmat(pre_test = NULL, pst_test = NULL, subgroup = NULL,
  force9 = FALSE)

Arguments

Details

multi_transmat: transition matrix of all the items

Value

matrix with rows = total number of items + 1 (last row contains aggregate distribution across items) number of columns = 4 when no don't know, and 9 when there is a don't know option

Examples

pre_test <- data.frame(pre_item1=c(1,0,0,1,0), pre_item2=c(1,NA,0,1,0)) 
pst_test <- data.frame(pst_item1=pre_test[,1] + c(0,1,1,0,0), 
					  pst_item2 = pre_test[,2] + c(0,1,0,0,1))
multi_transmat(pre_test, pst_test)

No NAs

Description

Converts NAs to 0s

Usage

nona(vec = NULL)

Arguments

Value

Character vector.

Examples

x <- c(NA, 1, 0); nona(x)
x <- c(NA, "dk", 0); nona(x)

Parameters of Simulated Responses to Knowledge Questions Without Don't Know

Description

Paramaters of the simulated Pre- and Post-Process Responses to Knowledge Questions without Don't Know

Usage

params

Format

A data frame with 1000 rows and 4 variables:

Parameters of Simulated Responses to Knowledge Questions With Don't Know

Description

Paramaters of the simulated Pre- and Post-Process Responses to Knowledge Questions with Don't Know

Usage

params_dk

Format

A data frame with 1000 rows and 4 variables:

Standard Guessing Correction for Learning

Description

Estimate of learning adjusted with standard correction for guessing. Correction is based on number of options per question. The function takes separate pre-test and post-test dataframes. Why do we need dataframes? To accomodate multiple items. The items can carry NA (missing). Items must be in the same order in each dataframe. Assumes that respondents are posed same questions twice. The function also takes a lucky vector – the chance of getting a correct answer if guessing randomly. Each entry is 1/(no. of options). The function also optionally takes a vector carrying names of the items. By default, the vector carrying adjusted learning estimates takes same item names as the pre_test items. However you can assign a vector of names separately via item_names.

Usage

stndcor(pre_test = NULL, pst_test = NULL, lucky = NULL,
  item_names = NULL)

Arguments

Value

a list of three vectors, carrying pre-treatment corrected scores, post-treatment scores, and adjusted estimates of learning

Examples

pre_test <- data.frame(item1=c(1,0,0,1,0), item2=c(1,NA,0,1,0)); 
pst_test <- pre_test + cbind(c(0,1,1,0,0), c(0,1,0,0,1))
lucky <- rep(.25, 2); stndcor(pre_test, pst_test, lucky)

transmat: Cross-wave transition matrix

Description

Prints Cross-wave transition matrix and returns the vector behind the matrix. Missing values are treated as ignorance. Don't know responses need to be coded as 'd'.

Usage

transmat(pre_test_var, pst_test_var, subgroup = NULL, force9 = FALSE)

Arguments

Value

a numeric vector. Assume 1 denotes correct answer, 0 and NA incorrect, and d 'don't know.' When there is no don't know option and no missing, the entries are: x00, x10, x01, x11 When there is a don't know option, the entries of the vector are: x00, x10, xd0, x01, x11, xd1, xd0, x1d, xdd

Examples

pre_test_var <- c(1,0,0,1,0,1,0)
pst_test_var <- c(1,0,1,1,0,1,1)
transmat(pre_test_var, pst_test_var)

# With NAs
pre_test_var <- c(1,0,0,1,"d","d",0,1,NA)
pst_test_var <- c(1,NA,1,"d",1,0,1,1,"d") 
transmat(pre_test_var, pst_test_var)