KnowledgeVoteDuty: Survey Respondents' Views of Voting as a Duty and Political Knowledge Questions

Description

KnowledgeVoteDuty is a modified set of responses to a small set of questions on the American National Election Study's 2024 Time Series Study. These data only include respondents who had non-missing values on all of the variables included, dropping respondents with one or more missing values.

Usage

data(KnowledgeVoteDuty)

Format

A data frame with 3,059 observations and 5 variables:

voteduty

Whether respondents feel that voting is a duty or a choice. Values range from 1 to 7, with 1 being "Very strongly a duty" and 7 being "Very strongly a choice," created based on variable V241218x.

SenateTerm

Dummy variable (0 or 1) for whether respondent correctly stated the length of a U.S. Senate term. Created based on variable V241612.

SpendLeast

Dummy variable (0 or 1) for whether respondent correctly identified "Foreign aid" from a list as the category the federal government spends the least on. Created based on variable V241613.

HouseParty

Dummy variable (0 or 1) for whether respondent correctly identified the political party that currently has the most members in the U.S. House of Representatives. Created based on variable V241614.

SenateParty

Dummy variable (0 or 1) for whether respondent correctly identified the political party that currently has the most members in the U.S. Senate. Created based on variable V241615.

References

American National Election Studies. 2024. ANES 2024 Time Series Study Full Release [dataset and documentation]. Available at electionstudies.org.

Examples

data(KnowledgeVoteDuty)
voteduty <- KnowledgeVoteDuty$voteduty
knowledge <- scale(rowMeans(KnowledgeVoteDuty[ , -1]))
summary(lm(voteduty ~ knowledge))

A function to build the environment for lpmec. Builds a conda environment in which 'JAX', 'numpyro', and 'numpy' are installed. Users can also create a conda environment where 'JAX' and 'numpy' are installed themselves.

Description

A function to build the environment for lpmec. Builds a conda environment in which 'JAX', 'numpyro', and 'numpy' are installed. Users can also create a conda environment where 'JAX' and 'numpy' are installed themselves.

Usage

build_backend(conda_env = "lpmec", conda = "auto")

Arguments

Value

Invisibly returns NULL; this function is used for its side effects of creating and configuring a conda environment for lpmec. This function requires an Internet connection. You can find out a list of conda Python paths via: Sys.which("python")

Examples

## Not run: 
# Create a conda environment named "lpmec"
# and install the required Python packages (jax, numpy, etc.)
build_backend(conda_env = "lpmec", conda = "auto")

# If you want to specify a particular conda path:
# build_backend(conda_env = "lpmec", conda = "/usr/local/bin/conda")

## End(Not run)

Infer orientation signs for each observable indicator

Description

This helper analyzes observable indicators and returns a numeric vector of 1 or -1 for use with the orientation_signs argument in lpmec. Each sign is chosen so that the correlation between the oriented indicator and either the outcome Y or the first principal component of the indicators is positive.

Usage

infer_orientation_signs(Y, observables, method = c("Y", "PC1"))

Arguments

Value

A numeric vector of length ncol(observables) containing 1 or -1.

Examples

set.seed(1)
Y <- rnorm(10)
obs <- data.frame(matrix(sample(c(0,1), 20, replace = TRUE), ncol = 2))
infer_orientation_signs(Y, obs)

lpmec

Description

Implements bootstrapped analysis for latent variable models with measurement error correction

Usage

lpmec(
  Y,
  observables,
  observables_groupings = colnames(observables),
  orientation_signs = NULL,
  make_observables_groupings = FALSE,
  n_boot = 32L,
  n_partition = 10L,
  boot_basis = 1:length(Y),
  return_intermediaries = TRUE,
  ordinal = FALSE,
  estimation_method = "em",
  latent_estimation_fn = NULL,
  mcmc_control = list(backend = "pscl", n_samples_warmup = 500L, n_samples_mcmc = 1000L,
    batch_size = 512L, chain_method = "parallel", subsample_method = "full",
    anchor_parameter_id = NULL, n_thin_by = 1L, n_chains = 2L),
  conda_env = "lpmec",
  conda_env_required = FALSE
)

Arguments

Details

This function implements a bootstrapped latent variable analysis with measurement error correction. It performs multiple bootstrap iterations, each with multiple partitions. For each partition, it calls the lpmec_onerun function to estimate latent variables and apply various correction methods. The results are then aggregated across partitions and bootstrap iterations to produce final estimates and bootstrap standard errors.

Value

A list containing various estimates and statistics (in snake_case):

• ols_coef: Coefficient from naive OLS regression.

• ols_se: Standard error of naive OLS coefficient.

• ols_tstat: T-statistic of naive OLS coefficient.

• iv_coef: Coefficient from instrumental variable (IV) regression.

• iv_se: Standard error of IV regression coefficient.

• iv_tstat: T-statistic of IV regression coefficient.

• corrected_iv_coef: IV regression coefficient corrected for measurement error.

• corrected_iv_se: Standard error of the corrected IV coefficient (currently NA).

• corrected_iv_tstat: T-statistic of the corrected IV coefficient.

• var_est: Estimated variance of the measurement error (split-half variance).

• corrected_ols_coef: OLS coefficient corrected for measurement error.

• corrected_ols_se: Standard error of the corrected OLS coefficient (currently NA).

• corrected_ols_tstat: T-statistic of the corrected OLS coefficient (currently NA).

• corrected_ols_coef_alt: Alternative corrected OLS coefficient (if applicable).

• corrected_ols_se_alt: Standard error for the alternative corrected OLS coefficient (if applicable).

• corrected_ols_tstat_alt: T-statistic for the alternative corrected OLS coefficient (if applicable).

• bayesian_ols_coef_outer_normed: Posterior mean of the OLS coefficient under MCMC, after normalizing by the overall sample standard deviation.

• bayesian_ols_se_outer_normed: Posterior standard error corresponding to bayesian_ols_coef_outer_normed.

• bayesian_ols_tstat_outer_normed: T-statistic for bayesian_ols_coef_outer_normed.

• bayesian_ols_coef_inner_normed: Posterior mean of the OLS coefficient under MCMC, after normalizing each posterior draw individually.

• bayesian_ols_se_inner_normed: Posterior standard error corresponding to bayesian_ols_coef_inner_normed.

• bayesian_ols_tstat_inner_normed: T-statistic for bayesian_ols_coef_inner_normed.

• m_stage_1_erv: Extreme robustness value (ERV) for the first-stage regression (x_est2 on x_est1), if computed.

• m_reduced_erv: ERV for the reduced model (Y on x_est1), if computed.

• x_est1: First set of latent variable estimates.

• x_est2: Second set of latent variable estimates.

References

Jerzak, C. T. and Jessee, S. A. (2025). Attenuation Bias with Latent Predictors. arXiv:2507.22218 [stat.AP]. https://arxiv.org/abs/2507.22218

Examples

# Generate some example data
set.seed(123)
Y <- rnorm(1000)
observables <- as.data.frame(matrix(sample(c(0,1), 1000*10, replace = TRUE), ncol = 10))

# Run the bootstrapped analysis
results <- lpmec(Y = Y,
                 observables = observables,
                 n_boot = 10,    # small values for illustration only
                 n_partition = 5 # small for size
                 )

# View the corrected IV coefficient and its standard error
print(results)

lpmec_onerun

Description

Implements analysis for latent variable models with measurement error correction

Usage

lpmec_onerun(
  Y,
  observables,
  observables_groupings = colnames(observables),
  make_observables_groupings = FALSE,
  estimation_method = "em",
  latent_estimation_fn = NULL,
  mcmc_control = list(backend = "pscl", n_samples_warmup = 500L, n_samples_mcmc = 1000L,
    batch_size = 512L, chain_method = "parallel", subsample_method = "full", n_thin_by =
    1L, n_chains = 2L),
  ordinal = FALSE,
  conda_env = "lpmec",
  conda_env_required = FALSE
)

Arguments

Details

This function implements a latent variable analysis with measurement error correction. It splits the observable indicators into two sets, estimates latent variables using each set, and then applies various correction methods including OLS correction and instrumental variable approaches.

Value

A list containing various estimates and statistics:

• ols_coef: Coefficient from naive OLS regression

• ols_se: Standard error of naive OLS coefficient

• ols_tstat: T-statistic of naive OLS coefficient

• iv_coef_a: IV coefficient using first split as instrument

• iv_coef_b: IV coefficient using second split as instrument

• iv_coef: Averaged IV coefficient from both splits

• iv_se: Standard error of IV regression coefficient

• iv_tstat: T-statistic of IV regression coefficient

• corrected_iv_coef_a: Corrected IV coefficient using first split as instrument

• corrected_iv_coef_b: Corrected IV coefficient using second split as instrument

• corrected_iv_coef: Averaged corrected IV coefficient from both splits

• corrected_iv_se: Standard error of corrected IV coefficient

• corrected_iv_tstat: T-statistic of corrected IV coefficient

• corrected_ols_coef_a: Corrected OLS coefficient using first split

• corrected_ols_coef_b: Corrected OLS coefficient using second split

• corrected_ols_coef: Averaged corrected OLS coefficient from both splits

• corrected_ols_se: Standard error of corrected OLS coefficient (currently NA)

• corrected_ols_tstat: T-statistic of corrected OLS coefficient (currently NA)

• corrected_ols_coef_alt: Alternative corrected OLS coefficient (currently NA)

• var_est_split: Estimated variance of the measurement error

• x_est1: First set of latent variable estimates

• x_est2: Second set of latent variable estimates

Standard Errors

The following standard errors and t-statistics are currently returned as NA because their analytical derivation is not yet implemented:

• corrected_ols_se: Standard error for the corrected OLS coefficient

• corrected_ols_tstat: T-statistic for the corrected OLS coefficient

• corrected_ols_coef_alt: Alternative corrected OLS coefficient

For inference on these quantities, use the bootstrap approach via lpmec, which provides valid confidence intervals and standard errors through resampling.

Examples

# Generate some example data
set.seed(123)
Y <- rnorm(1000)
observables <- as.data.frame(matrix(sample(c(0,1), 1000*10, replace = TRUE), ncol = 10))

# Run the analysis
results <- lpmec_onerun(Y = Y,
                        observables = observables)

# View the corrected estimates
print(results)

Plot method for lpmec objects

Description

Creates visualizations of LPMEC model results. Can plot either the latent variable estimates or the bootstrap distribution of coefficients.

Usage

## S3 method for class 'lpmec'
plot(x, type = "latent", ...)

Arguments

Value

No return value, called for side effects (creates a plot).

See Also

lpmec, summary.lpmec, print.lpmec

Plot method for lpmec_onerun objects

Description

Creates a scatter plot comparing the two split-half latent variable estimates.

Usage

## S3 method for class 'lpmec_onerun'
plot(x, ...)

Arguments

Value

No return value, called for side effects (creates a plot).

See Also

lpmec_onerun, summary.lpmec_onerun, print.lpmec_onerun

Print method for lpmec objects

Description

Prints a concise summary of bootstrapped LPMEC model results.

Usage

## S3 method for class 'lpmec'
print(x, ...)

Arguments

Value

The input object x, returned invisibly.

See Also

lpmec, summary.lpmec, plot.lpmec

Print method for lpmec_onerun objects

Description

Prints a concise summary of single-run LPMEC model results.

Usage

## S3 method for class 'lpmec_onerun'
print(x, ...)

Arguments

Value

The input object x, returned invisibly.

See Also

lpmec_onerun, summary.lpmec_onerun, plot.lpmec_onerun

Summary method for lpmec objects

Description

Provides a comprehensive summary of bootstrapped LPMEC model results including OLS, IV, corrected, and Bayesian coefficient estimates with confidence intervals.

Usage

## S3 method for class 'lpmec'
summary(object, ...)

Arguments

Value

A data frame containing coefficient estimates, standard errors, and confidence intervals, returned invisibly. The data frame has rows for OLS, IV, Corrected IV, Corrected OLS, and Bayesian OLS estimates.

See Also

lpmec, print.lpmec, plot.lpmec

Summary method for lpmec_onerun objects

Description

Provides a summary of single-run LPMEC model results including OLS, IV, and corrected coefficient estimates.

Usage

## S3 method for class 'lpmec_onerun'
summary(object, ...)

Arguments

Value

A data frame containing coefficient estimates and standard errors, returned invisibly. The data frame has rows for OLS, IV, Corrected IV, and Corrected OLS estimates.

See Also

lpmec_onerun, print.lpmec_onerun, plot.lpmec_onerun