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# edmdata

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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](https://CRAN.R-project.org/package=edmdata) and
[GitHub](https://github.com/tmsalab/edmdata). 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:

``` r
install.packages("edmdata")
```

For the development version, you can install the `edmdata` package from
GitHub with:

``` r
# 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](https://cran.r-project.org/doc/manuals/r-release/R-ints.html#Lazy-loading)
of the [R
Internals](https://cran.r-project.org/doc/manuals/r-release/R-ints.html)
manual.

``` r
# 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.

``` r
# 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 = 29 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`](https://github.com/tmsalab/edmdata/tree/master/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*:

``` r
citation("edmdata")
```

## License

MIT

## References

<div id="refs" class="references csl-bib-body hanging-indent"
entry-spacing="0">

<div id="ref-Chen:2018:EDINA" class="csl-entry">

Chen, Yinghan, Culpepper, S. A., Chen, Y., & Douglas, J. (2018).
Bayesian estimation of the DINA q matrix. *Psychometrika*, *83*(1),
89–108. <https://doi.org/10.1007/s11336-017-9579-4>

</div>

<div id="ref-Chen:2020:SLCMDC" class="csl-entry">

Chen, Yinyin, Culpepper, S. A., & Liang, F. (2020). A sparse latent
class model for cognitive diagnosis. *Psychometrika*, 1–33.
<https://doi.org/10.1007/s11336-019-09693-2>

</div>

<div id="ref-Chen:2021:InferK" class="csl-entry">

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.
<https://doi.org/10.1007/s11336-021-09750-9>

</div>

<div id="ref-Culpepper:2014:SequentialIRT" class="csl-entry">

Culpepper, S. A. (2014). If at first you don’t succeed, try, try again:
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<https://doi.org/10.1177/0146621614536464>

</div>

<div id="ref-Culpepper:2015:BayesianDINA" class="csl-entry">

Culpepper, S. A. (2015). Bayesian estimation of the DINA model with
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</div>

<div id="ref-Culpepper:2019:EODM" class="csl-entry">

Culpepper, S. A. (2019a). An exploratory diagnostic model for ordinal
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<https://doi.org/10.1007/s11336-019-09683-4>

</div>

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</div>

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<https://doi.org/10.1007/s11336-015-9484-7>

</div>

<div id="ref-Culpepper:2021:OHOEGDM" class="csl-entry">

Culpepper, S. A., & Balamuta, J. J. (2021). Inferring latent structure
in polytomous data with a higher-order diagnostic model. *Multivariate
Behavioral Research*, 1–19.
<https://doi.org/10.1080/00273171.2021.1985949>

</div>

<div id="ref-Culpepper:2019:ErRUM" class="csl-entry">

Culpepper, S. A., & Chen, Y. (2019). Development and application of an
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Educational and Behavioral Statistics*, *44*(1), 3–24.
<https://doi.org/10.3102/1076998618791306>

</div>

<div id="ref-Heller:2013:ProbabilityKS" class="csl-entry">

Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in
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</div>

<div id="ref-Jimenez:2023:OPGEDM" class="csl-entry">

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/10.1111/bmsp.12307>

</div>

<div id="ref-TIMSS:2015:Background" class="csl-entry">

Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds. ). (2016).
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and science*. Retrieved from Boston College, TIMSS & PIRLS International
Study Center website:
http://timssandpirls.bc.edu/timss2015/encyclopedia/.

</div>

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Myszkowski, N., & Storme, M. (2018). A snapshot of g? Binary and
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<https://doi.org/10.1016/j.intell.2018.03.010>

</div>

<div id="ref-ECLSK:2010:ATLData" class="csl-entry">

NCES. (2010). *Early childhood longitudinal study, kindergarten class of
1998-99 (ECLS-k) kindergarten through fifth grade approaches to learning
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</div>

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<https://openpsychometrics.org/_rawdata/IQ1.zip>

</div>

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</div>

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<https://openpsychometrics.org/_rawdata/NPI.zip>

</div>

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<https://doi.org/10.1037/0022-3514.54.5.890>

</div>

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</div>

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</div>

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</div>

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Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic
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<https://doi.org/10.1007/s11336-013-9362-0>

</div>

<div id="ref-Templin:2013:DCMECPE" class="csl-entry">

Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification
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</div>

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Yoon, S. Y. (2011). *Psychometric properties of the revised purdue
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</div>

</div>
