README.md
In tmsalab/ecdmdata: Data Sets for Psychometric Modeling
edmdata
The goal of edmdata R data package is to provide a set of assessment
data sets for psychometric modeling.
Installation
The edmdata package is available on both
CRAN and
GitHub. The CRAN version is
considered stable while the GitHub version is in a state of development
and may break.
You can install the stable version of the edmdata package with:
install.packages("edmdata")
For the development version, you can install the edmdata package from
GitHub with:
# install.packages("remotes")
remotes::install_github("tmsalab/edmdata")
Using data in the package
There are two ways to access the data contained within this package.
The first is to load the package itself and type the name of a data set.
This approach takes advantage of R’s lazy loading mechanism, which
avoids loading the data until it is used in R session. For details on
how lazy loading works, please see Section 1.17: Lazy
Loading
of the R
Internals
manual.
# Load the `edmdata` package
library("edmdata")
# See the first 10 observations of the `items_revised_psvtr` dataset
head(items_revised_psvtr)
# View the help documentation for `items_revised_psvtr`
?items_revised_psvtr
The second approach is to use the data() command to load data on the
fly without loading the package. After using data(), the data set will
be available to use under the given name.
# Loading `items_revised_psvtr` without a `library(edmdata)` call
data("items_revised_psvtr", package = "edmdata")
# See the first 10 observations of the `items_revised_psvtr` dataset
head(items_revised_psvtr)
# View the help documentation for `items_revised_psvtr`
?items_revised_psvtr
Data Sets Included
- Examination for the Certificate of Proficiency in English (ECPE)
(Templin & Bradshaw, 2014; Templin & Hoffman, 2013).
items_ecpe: N = 2922 subject responses to J = 28 items.qmatrix_ecpe: J = 28 items and K = 3 traits.- TMSA Papers: Culpepper & Chen (2019)
- Fraction Addition and Subtraction (C. Tatsuoka, 2002; K. K. Tatsuoka,
1984).
items_fractions: N = 536 subject responses to J = 20 items.qmatrix_fractions: J = 536 items and K = 20 traits.- TMSA Papers: Yinghan Chen et al. (2021), Yinyin Chen et al.
(2020), Culpepper (2019b), Culpepper & Chen (2019), Yinghan Chen et
al. (2018)
- Elementary Probability Theory (Heller & Wickelmaier, 2013).
items_probability_part_one_full: N = 504 subject responses to J =
12 items.items_probability_part_one_reduced: N = 431 subject responses to J
= 12 items.qmatrix_probability_part_one: J = 12 items and K = 4 traits.- TMSA Papers: Yinghan Chen et al. (2021)
- Revised PSVT:R (Culpepper & Balamuta, 2017; Yoon, 2011).
items_revised_psvtr: N = 516 subject responses to J = 30 items.- TMSA Papers: Culpepper & Balamuta (2017), Culpepper (2015)
- Subset of Early Childhood Longitudinal Study, Kindergarten Class of
1998-1999’s Approaches to Learning (NCES, 2010).
items_ordered_eclsk_atl: N = 13354 subject responses to J = 12
items.- TMSA Papers: Culpepper (2019a)
- Trends in International Mathematics and Science Study 2015 (TIMSS)
Grade 8 Student Background Survey Item Responses (Mullis et al.,
2016).
items_ordered_timss15_background: N = 9672 subject responses to J
= 16 items.- Calculus-based probability and statistics course homework problems
(Jimenez et al., 2023)
items_ordered_pswc_hw: N = 288 subject responses to J = 30 items.- Programme for International Student Assessment (PISA) 2012 U.S.
Student Questionnaire Problem-Solving Vignettes (Culpepper & Balamuta,
2021).
items_ordered_pisa12_us_vignette: N = 3075 subject responses to J
= 12 items.- Programme for International Student Assessment (PISA) 2012 U.S. Math
Assessment.
items_pisa12_us_math: N = 4978 subject responses to J = 76 items.- Last Series of the Standard Progressive Matrices (SPM-LS) (Myszkowski
& Storme, 2018; Raven, 1941; Robitzsch, 2020).
items_spm_ls: N = 499 subject responses to J = 12 items.- Human Connectome Project’s Penn Progressive Matrices Fluid
Intelligence Assessment
items_hcp_penn_matrix: N = 1201 subject responses to J = 24 items.items_hcp_penn_matrix_missing: N = 1201 subject responses with
missing data indicators to J = 24 items.- Experimental Matrix Reasoning Test (OpenPsychometrics, 2012a).
items_matrix_reasoning: N = 400 subject responses to J = 25 items.- TMSA Papers: Yinyin Chen et al. (2020)
- Taylor Manifest Anxiety Scale (OpenPsychometrics, 2012b; Taylor,
1953).
items_taylor_manifest_anxiety_scale: N = 4468 subject responses to
J = 50 items.- Narcissistic Personality Inventory (OpenPsychometrics, 2013; Raskin &
Terry, 1988).
items_narcissistic_personality_inventory: N = 11243 subject
responses to J = 40 items.- Pre-generated identified Q matrices.
qmatrix_oracle_k2_j12: 12 items and 2 traits.qmatrix_oracle_k3_j20: 20 items and 3 traits.qmatrix_oracle_k4_j20: 20 items and 4 traits.qmatrix_oracle_k5_j30: 30 items and 5 traits.- Pre-generated strategy sets.
strategy_oracle_k3_j20_s2: 20 items, 3 traits, and 2 strategies.strategy_oracle_k3_j30_s2: 30 items, 3 traits, and 2 strategies.strategy_oracle_k3_j40_s2: 40 items, 3 traits, and 2 strategies.strategy_oracle_k3_j50_s2: 50 items, 3 traits, and 2 strategies.strategy_oracle_k4_j20_s2: 20 items, 4 traits, and 2 strategies.strategy_oracle_k4_j30_s2: 30 items, 4 traits, and 2 strategies.strategy_oracle_k4_j40_s2: 40 items, 4 traits, and 2 strategies.strategy_oracle_k4_j50_s2: 50 items, 4 traits, and 2 strategies.
Build Scripts
Want to see how each data set was imported? Check out the
data-raw
folder!
Authors
James Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas
Citing the edmdata package
To ensure future development of the package, please cite edmdata
package if used during an analysis or simulation study. Citation
information for the package may be acquired by using in R:
citation("edmdata")
License
MIT
References
Chen, Yinghan, Culpepper, S. A., Chen, Y., & Douglas, J. (2018).
Bayesian estimation of the DINA q matrix. *Psychometrika*, *83*(1),
89–108.
Chen, Yinyin, Culpepper, S. A., & Liang, F. (2020). A sparse latent
class model for cognitive diagnosis. *Psychometrika*, 1–33.
Chen, Yinghan, Liu, Y., Culpepper, S. A., & Chen, Y. (2021). Inferring
the number of attributes for the exploratory DINA model.
*Psychometrika*, *86*(1), 30–64.
Culpepper, S. A. (2015). Bayesian estimation of the DINA model with
gibbs sampling. *Journal of Educational and Behavioral Statistics*,
*40*(5), 454–476.
Culpepper, S. A. (2019a). An exploratory diagnostic model for ordinal
responses with binary attributes: Identifiability and estimation.
*Psychometrika*, *84*(4), 921–940.
Culpepper, S. A. (2019b). Estimating the cognitive diagnosis $Q$ matrix
with expert knowledge: Application to the fraction-subtraction dataset.
*Psychometrika*, *84*(2), 333–357.
Culpepper, S. A., & Balamuta, J. J. (2017). A
Hierarchical Model for Accuracy and Choice on Standardized Tests.
*Psychometrika*, *82*(3), 820–845.
Culpepper, S. A., & Balamuta, J. J. (2021). Inferring latent structure
in polytomous data with a higher-order diagnostic model. *Multivariate
Behavioral Research*, 1–19.
Culpepper, S. A., & Chen, Y. (2019). Development and application of an
exploratory reduced reparameterized unified model. *Journal of
Educational and Behavioral Statistics*, *44*(1), 3–24.
Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in
probabilistic knowledge structures. *Electronic Notes in Discrete
Mathematics*, *42*, 49–56.
Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023). A sequential
exploratory diagnostic model using a pólya-gamma data augmentation
strategy. *British Journal of Mathematical and Statistical Psychology*,
*76*(3), 513–538. https://doi.org/
Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds. ). (2016).
*TIMSS 2015 encyclopedia: Education policy and curriculum in mathematics
and science*. Retrieved from Boston College, TIMSS & PIRLS International
Study Center website:
http://timssandpirls.bc.edu/timss2015/encyclopedia/.
Myszkowski, N., & Storme, M. (2018). A snapshot of g? Binary and
polytomous item-response theory investigations of the last series of the
standard progressive matrices (SPM-LS). *Intelligence*, *68*, 109–116.
NCES. (2010). *Early childhood longitudinal study, kindergarten class of
1998-99 (ECLS-k) kindergarten through fifth grade approaches to learning
and self-description questionnaire (SDQ) items and public-use data
files*.
OpenPsychometrics. (2012a). *Experimental matrix reasoning IQ test*.
OpenPsychometrics. (2012b). *Taylor manifest anxiety scale*.
OpenPsychometrics. (2013). *Narcissistic personality inventory*.
Raskin, R., & Terry, H. (1988). A principal-components analysis of the
narcissistic personality inventory and further evidence of its construct
validity. *Journal of Personality and Social Psychology*, *54*(5), 890.
Raven, J. C. (1941). Standardization of progressive matrices, 1938.
*British Journal of Medical Psychology*, *19*(1), 137–150.
Robitzsch, A. (2020). Regularized latent class analysis for polytomous
item responses: An application to SPM-LS data. *Preprint*.
Tatsuoka, C. (2002). Data analytic methods for latent partially ordered
classification models. *Journal of the Royal Statistical Society: Series
C (Applied Statistics)*, *51*(3), 337–350.
Tatsuoka, K. K. (1984). *Analysis of errors in fraction addition and
subtraction problems. Final report.*
Taylor, J. A. (1953). A personality scale of manifest anxiety. *The
Journal of Abnormal and Social Psychology*, *48*(2), 285.
Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic
classification models: A family of models for estimating and testing
attribute hierarchies. *Psychometrika*, *79*(2), 317–339.
Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification
model estimates using mplus. *Educational Measurement: Issues and
Practice*, *32*(2), 37–50.
Yoon, S. Y. (2011). *Psychometric properties of the revised purdue
spatial visualization tests: Visualization of rotations (the revised
PSVT: r)*. Purdue University.
tmsalab/ecdmdata documentation built on Aug. 24, 2024, 1:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
edmdata
The goal of edmdata R data package is to provide a set of assessment
data sets for psychometric modeling.
Installation
The edmdata package is available on both
CRAN and
GitHub. The CRAN version is
considered stable while the GitHub version is in a state of development
and may break.
You can install the stable version of the edmdata package with:
install.packages("edmdata")
For the development version, you can install the edmdata package from
GitHub with:
# install.packages("remotes")
remotes::install_github("tmsalab/edmdata")
Using data in the package
There are two ways to access the data contained within this package.
The first is to load the package itself and type the name of a data set. This approach takes advantage of R’s lazy loading mechanism, which avoids loading the data until it is used in R session. For details on how lazy loading works, please see Section 1.17: Lazy Loading of the R Internals manual.
# Load the `edmdata` package
library("edmdata")
# See the first 10 observations of the `items_revised_psvtr` dataset
head(items_revised_psvtr)
# View the help documentation for `items_revised_psvtr`
?items_revised_psvtr
The second approach is to use the data() command to load data on the
fly without loading the package. After using data(), the data set will
be available to use under the given name.
# Loading `items_revised_psvtr` without a `library(edmdata)` call
data("items_revised_psvtr", package = "edmdata")
# See the first 10 observations of the `items_revised_psvtr` dataset
head(items_revised_psvtr)
# View the help documentation for `items_revised_psvtr`
?items_revised_psvtr
Data Sets Included
- Examination for the Certificate of Proficiency in English (ECPE) (Templin & Bradshaw, 2014; Templin & Hoffman, 2013).
items_ecpe: N = 2922 subject responses to J = 28 items.qmatrix_ecpe: J = 28 items and K = 3 traits.- TMSA Papers: Culpepper & Chen (2019)
- Fraction Addition and Subtraction (C. Tatsuoka, 2002; K. K. Tatsuoka, 1984).
items_fractions: N = 536 subject responses to J = 20 items.qmatrix_fractions: J = 536 items and K = 20 traits.- TMSA Papers: Yinghan Chen et al. (2021), Yinyin Chen et al. (2020), Culpepper (2019b), Culpepper & Chen (2019), Yinghan Chen et al. (2018)
- Elementary Probability Theory (Heller & Wickelmaier, 2013).
items_probability_part_one_full: N = 504 subject responses to J = 12 items.items_probability_part_one_reduced: N = 431 subject responses to J = 12 items.qmatrix_probability_part_one: J = 12 items and K = 4 traits.- TMSA Papers: Yinghan Chen et al. (2021)
- Revised PSVT:R (Culpepper & Balamuta, 2017; Yoon, 2011).
items_revised_psvtr: N = 516 subject responses to J = 30 items.- TMSA Papers: Culpepper & Balamuta (2017), Culpepper (2015)
- Subset of Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999’s Approaches to Learning (NCES, 2010).
items_ordered_eclsk_atl: N = 13354 subject responses to J = 12 items.- TMSA Papers: Culpepper (2019a)
- Trends in International Mathematics and Science Study 2015 (TIMSS) Grade 8 Student Background Survey Item Responses (Mullis et al., 2016).
items_ordered_timss15_background: N = 9672 subject responses to J = 16 items.- Calculus-based probability and statistics course homework problems (Jimenez et al., 2023)
items_ordered_pswc_hw: N = 288 subject responses to J = 30 items.- Programme for International Student Assessment (PISA) 2012 U.S. Student Questionnaire Problem-Solving Vignettes (Culpepper & Balamuta, 2021).
items_ordered_pisa12_us_vignette: N = 3075 subject responses to J = 12 items.- Programme for International Student Assessment (PISA) 2012 U.S. Math Assessment.
items_pisa12_us_math: N = 4978 subject responses to J = 76 items.- Last Series of the Standard Progressive Matrices (SPM-LS) (Myszkowski & Storme, 2018; Raven, 1941; Robitzsch, 2020).
items_spm_ls: N = 499 subject responses to J = 12 items.- Human Connectome Project’s Penn Progressive Matrices Fluid Intelligence Assessment
items_hcp_penn_matrix: N = 1201 subject responses to J = 24 items.items_hcp_penn_matrix_missing: N = 1201 subject responses with missing data indicators to J = 24 items.- Experimental Matrix Reasoning Test (OpenPsychometrics, 2012a).
items_matrix_reasoning: N = 400 subject responses to J = 25 items.- TMSA Papers: Yinyin Chen et al. (2020)
- Taylor Manifest Anxiety Scale (OpenPsychometrics, 2012b; Taylor, 1953).
items_taylor_manifest_anxiety_scale: N = 4468 subject responses to J = 50 items.- Narcissistic Personality Inventory (OpenPsychometrics, 2013; Raskin & Terry, 1988).
items_narcissistic_personality_inventory: N = 11243 subject responses to J = 40 items.- Pre-generated identified Q matrices.
qmatrix_oracle_k2_j12: 12 items and 2 traits.qmatrix_oracle_k3_j20: 20 items and 3 traits.qmatrix_oracle_k4_j20: 20 items and 4 traits.qmatrix_oracle_k5_j30: 30 items and 5 traits.- Pre-generated strategy sets.
strategy_oracle_k3_j20_s2: 20 items, 3 traits, and 2 strategies.strategy_oracle_k3_j30_s2: 30 items, 3 traits, and 2 strategies.strategy_oracle_k3_j40_s2: 40 items, 3 traits, and 2 strategies.strategy_oracle_k3_j50_s2: 50 items, 3 traits, and 2 strategies.strategy_oracle_k4_j20_s2: 20 items, 4 traits, and 2 strategies.strategy_oracle_k4_j30_s2: 30 items, 4 traits, and 2 strategies.strategy_oracle_k4_j40_s2: 40 items, 4 traits, and 2 strategies.strategy_oracle_k4_j50_s2: 50 items, 4 traits, and 2 strategies.
Build Scripts
Want to see how each data set was imported? Check out the
data-raw
folder!
Authors
James Joseph Balamuta, Steven Andrew Culpepper, Jeffrey Douglas
Citing the edmdata package
To ensure future development of the package, please cite edmdata
package if used during an analysis or simulation study. Citation
information for the package may be acquired by using in R:
citation("edmdata")
License
MIT
References
Chen, Yinghan, Culpepper, S. A., Chen, Y., & Douglas, J. (2018).
Bayesian estimation of the DINA q matrix. *Psychometrika*, *83*(1),
89–108.
Chen, Yinyin, Culpepper, S. A., & Liang, F. (2020). A sparse latent
class model for cognitive diagnosis. *Psychometrika*, 1–33.
Chen, Yinghan, Liu, Y., Culpepper, S. A., & Chen, Y. (2021). Inferring
the number of attributes for the exploratory DINA model.
*Psychometrika*, *86*(1), 30–64.
Culpepper, S. A. (2015). Bayesian estimation of the DINA model with
gibbs sampling. *Journal of Educational and Behavioral Statistics*,
*40*(5), 454–476.
Culpepper, S. A. (2019a). An exploratory diagnostic model for ordinal
responses with binary attributes: Identifiability and estimation.
*Psychometrika*, *84*(4), 921–940.
Culpepper, S. A. (2019b). Estimating the cognitive diagnosis $Q$ matrix
with expert knowledge: Application to the fraction-subtraction dataset.
*Psychometrika*, *84*(2), 333–357.
Culpepper, S. A., & Balamuta, J. J. (2017). A
Hierarchical Model for Accuracy and Choice on Standardized Tests.
*Psychometrika*, *82*(3), 820–845.
Culpepper, S. A., & Balamuta, J. J. (2021). Inferring latent structure
in polytomous data with a higher-order diagnostic model. *Multivariate
Behavioral Research*, 1–19.
Culpepper, S. A., & Chen, Y. (2019). Development and application of an
exploratory reduced reparameterized unified model. *Journal of
Educational and Behavioral Statistics*, *44*(1), 3–24.
Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in
probabilistic knowledge structures. *Electronic Notes in Discrete
Mathematics*, *42*, 49–56.
Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023). A sequential
exploratory diagnostic model using a pólya-gamma data augmentation
strategy. *British Journal of Mathematical and Statistical Psychology*,
*76*(3), 513–538. https://doi.org/
Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds. ). (2016).
*TIMSS 2015 encyclopedia: Education policy and curriculum in mathematics
and science*. Retrieved from Boston College, TIMSS & PIRLS International
Study Center website:
http://timssandpirls.bc.edu/timss2015/encyclopedia/.
Myszkowski, N., & Storme, M. (2018). A snapshot of g? Binary and
polytomous item-response theory investigations of the last series of the
standard progressive matrices (SPM-LS). *Intelligence*, *68*, 109–116.
NCES. (2010). *Early childhood longitudinal study, kindergarten class of
1998-99 (ECLS-k) kindergarten through fifth grade approaches to learning
and self-description questionnaire (SDQ) items and public-use data
files*.
OpenPsychometrics. (2012a). *Experimental matrix reasoning IQ test*.
OpenPsychometrics. (2012b). *Taylor manifest anxiety scale*.
OpenPsychometrics. (2013). *Narcissistic personality inventory*.
Raskin, R., & Terry, H. (1988). A principal-components analysis of the
narcissistic personality inventory and further evidence of its construct
validity. *Journal of Personality and Social Psychology*, *54*(5), 890.
Raven, J. C. (1941). Standardization of progressive matrices, 1938.
*British Journal of Medical Psychology*, *19*(1), 137–150.
Robitzsch, A. (2020). Regularized latent class analysis for polytomous
item responses: An application to SPM-LS data. *Preprint*.
Tatsuoka, C. (2002). Data analytic methods for latent partially ordered
classification models. *Journal of the Royal Statistical Society: Series
C (Applied Statistics)*, *51*(3), 337–350.
Tatsuoka, K. K. (1984). *Analysis of errors in fraction addition and
subtraction problems. Final report.*
Taylor, J. A. (1953). A personality scale of manifest anxiety. *The
Journal of Abnormal and Social Psychology*, *48*(2), 285.
Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic
classification models: A family of models for estimating and testing
attribute hierarchies. *Psychometrika*, *79*(2), 317–339.
Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification
model estimates using mplus. *Educational Measurement: Issues and
Practice*, *32*(2), 37–50.
Yoon, S. Y. (2011). *Psychometric properties of the revised purdue
spatial visualization tests: Visualization of rotations (the revised
PSVT: r)*. Purdue University.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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