guess adjust estimates of learning for guessing related bias.

Description

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

Author(s)

Maintainer: Gaurav Sood gsood07@gmail.com

Authors:

• Ken Cor mcor@ualberta.ca

See Also

Useful links:

• https://github.com/finite-sample/guess

• Report bugs at https://github.com/finite-sample/guess/issues

Calculate expected values for goodness of fit test

Description

Calculate expected values for goodness of fit test

Usage

calculate_expected_values(gamma_i, params, total_obs, model_type = "nodk")

Arguments

Value

vector of expected values

Count transitions between pre and post test responses

Description

Count transitions between pre and post test responses

Usage

count_transitions(pre_responses, pst_responses)

Arguments

Value

named vector of transition counts

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

eq1dk(x, g1 = NA, data)

Arguments

Constraints: Sum to 1

Description

Constraints that some params sum to 1. Used Internally. For data without DK. Functions for constraining lambdas to sum to 1 and to bound params between 0 and 1

Usage

eqn1(x, g1 = NA, data)

Arguments

Goodness of fit statistics for transition matrix data

Description

Chi-square goodness of fit between true and model based multivariate distribution. Handles both data with and without don't know responses automatically.

Usage

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

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

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

Arguments

Details

Unified Goodness of Fit Statistics

Value

matrix with two rows: top row carrying chi-square value, bottom row p-values

Examples

## Not run: 
# Fit model first
transmatrix <- multi_transmat(pre_test, pst_test)
res <- lca_cor(transmatrix)

# Calculate goodness of fit
fit_stats <- fit_model(pre_test, pst_test, res$param.lca[nrow(res$param.lca), ], 
                       res$param.lca[-nrow(res$param.lca), ])

## End(Not run)

Format transition matrix result with appropriate row and column names

Description

Format transition matrix result with appropriate row and column names

Usage

format_transition_matrix(transition_list, n_items, add_aggregate = FALSE)

Arguments

Value

formatted matrix

Group Level Adjustment That Accounts for Propensity to Guess

Description

Adjusts observed 1s based on propensity to guess (based on observed 0s) and item level \gamma. You can also put in your best estimate of hidden knowledge behind don't know responses.

Usage

group_adj(pre = NULL, pst = NULL, gamma = NULL, dk = 0.03)

Arguments

Value

nested list of pre and post adjusted responses, and adjusted learning estimates

Examples

pre_test_var <- data.frame(pre = c(1,0,0,1,"d","d",0,1,NA))
pst_test_var <- data.frame(pst = c(1,NA,1,"d",1,0,1,1,"d"))
gamma <- c(.25)
group_adj(pre_test_var, pst_test_var, gamma)

guess_lik

Description

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

Usage

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

Arguments

guessdk_lik

Description

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

Usage

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

Arguments

Interleave vectors

Description

Interleaves two vectors. Used internally.

Usage

interleave(a, b)

Arguments

Person Level Adjustment

Description

Adjusts observed 1s based on item level parameters of the LCA model. Currently only takes data with Don't Know. And treats don't know responses as true confessions on ignorance. If NAs are observed in the data, they are treating as acknowledgments of ignorance.

Usage

lca_adj(pre = NULL, pst = NULL)

Arguments

Value

list of pre and post adjusted responses

Examples

pre_test_var <- data.frame(pre = c(1, 0, 0, 1, "d", "d", 0, 1, NA))
pst_test_var <- data.frame(pst = c(1, NA, 1, "d", 1, 0, 1, 1, "d"))
lca_adj(pre_test_var, pst_test_var)

Calculate item level and aggregate learning

Description

guesstimate

Usage

lca_cor(
  transmatrix = NULL,
  nodk_priors = c(0.3, 0.1, 0.1, 0.25),
  dk_priors = c(0.3, 0.1, 0.2, 0.05, 0.1, 0.1, 0.05, 0.25)
)

Arguments

Value

list with two items: parameter estimates and estimates of learning

Examples

# Without DK
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))
transmatrix <- multi_transmat(pre_test, pst_test)
res <- lca_cor(transmatrix)

Bootstrapped standard errors of effect size estimates

Description

guess_stnderr

Usage

lca_se(
  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: lca_se(pre_test, pst_test, nsamps = 10, seed = 31415)

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,
  agg = 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)

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/(number 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

stnd_cor(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

# Without DK
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); stnd_cor(pre_test, pst_test, lucky)
# With DK
pre_test <- data.frame(item1 = c(1,0,0,1,0,'d',0), item2 = c(1,NA,0,1,0,'d','d')) 
pst_test <- data.frame(item1 = c(1,0,0,1,0,'d',1), item2 = c(1,NA,0,1,0,1,'d')) 
lucky <- rep(.25, 2); stnd_cor(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)

Validate that two data frames have compatible dimensions

Description

Validate that two data frames have compatible dimensions

Usage

validate_compatible_dataframes(pre_test, pst_test)

Arguments

Value

TRUE if valid, throws error otherwise

Validate that input is a data frame

Description

Validate that input is a data frame

Usage

validate_dataframe(x, arg_name)

Arguments

Value

TRUE if valid, throws error otherwise

Validate gamma parameter

Description

Validate gamma parameter

Usage

validate_gamma(gamma)

Arguments

Value

TRUE if valid, throws error otherwise

Validate lucky vector for standard correction

Description

Validate lucky vector for standard correction

Usage

validate_lucky_vector(lucky, n_items)

Arguments

Value

TRUE if valid, throws error otherwise

Validate prior parameters

Description

Validate prior parameters

Usage

validate_priors(priors, expected_length, param_name)

Arguments

Value

TRUE if valid, throws error otherwise

Validate transition matrix values

Description

Validate transition matrix values

Usage

validate_transition_values(pre_test_var, pst_test_var)

Arguments

Value

TRUE if valid, throws error otherwise