taylorSwift: Swiftly compute Taylor regression models for distribution...

In cNORM: Continuous Norming

Swiftly compute Taylor regression models for distribution free continuous norming

Description

Conducts distribution free continuous norming and aims to find a fitting model. Raw data are modelled as a Taylor polynomial of powers of age and location and their interactions. In addition to the raw scores, either provide a numeric vector for the grouping information (group) for the ranking of the raw scores. You can adjust the grade of smoothing of the regression model by setting the k, t and terms parameter. In general, increasing k and t leads to a higher fit, while lower values lead to more smoothing. If both parameters are missing, taylorSwift uses k = 5 and t = 3 by default.

Usage

taylorSwift(
  raw = NULL,
  group = NULL,
  age = NULL,
  width = NA,
  weights = NULL,
  scale = "T",
  method = 4,
  descend = FALSE,
  k = NULL,
  t = NULL,
  terms = 0,
  R2 = NULL,
  plot = TRUE,
  extensive = TRUE,
  subsampling = TRUE
)

Arguments

raw	Numeric vector of raw scores
group	Numeric vector of grouping variable, e. g. grade. If no group or age variable is provided, conventional norming is applied
age	Numeric vector with chronological age, please additionally specify width of window
width	Size of the sliding window in case an age vector is used
weights	Vector or variable name in the dataset with weights for each individual case. It can be used to compensate for moderate imbalances due to insufficient norm data stratification. Weights should be numerical and positive.
scale	type of norm scale, either T (default), IQ, z or percentile (= no transformation); a double vector with the mean and standard deviation can as well, be provided f. e. c(10, 3) for Wechsler scale index points
method	Ranking method in case of bindings, please provide an index, choosing from the following methods: 1 = Blom (1958), 2 = Tukey (1949), 3 = Van der Warden (1952), 4 = Rankit (default), 5 = Levenbach (1953), 6 = Filliben (1975), 7 = Yu & Huang (2001)
descend	ranking order (default descent = FALSE): inverses the ranking order with higher raw scores getting lower norm scores; relevant for example when norming error scores, where lower scores mean higher performance
k	The power constant. Higher values result in more detailed approximations but have the danger of over-fit (max = 6). If not set, it uses t and if both parameters are NULL, k is set to 5.
t	The age power parameter (max = 6). If not set, it uses k and if both parameters are NULL, k is set to 3, since age trajectories are most often well captured by cubic polynomials.
terms	Selection criterion for model building. The best fitting model with this number of terms is used
R2	Adjusted R square as a stopping criterion for the model building (default R2 = 0.99)
plot	Default TRUE; plots the regression model and prints report
extensive	If TRUE, screen models for consistency and - if possible, exclude inconsistent ones
subsampling	If TRUE (default), model coefficients are calculated using 10-folds and averaged across the folds. This produces more robust estimates with a slight increase in bias.

Value

cnorm object including the ranked raw data and the regression model

References

1. Gary, S. & Lenhard, W. (2021). In norming we trust. Diagnostica.

2. Gary, S., Lenhard, W. & Lenhard, A. (2021). Modelling Norm Scores with the cNORM Package in R. Psych, 3(3), 501-521. https://doi.org/10.3390/psych3030033

3. Lenhard, A., Lenhard, W., Suggate, S. & Segerer, R. (2016). A continuous solution to the norming problem. Assessment, Online first, 1-14. doi:10.1177/1073191116656437

4. Lenhard, A., Lenhard, W., Gary, S. (2018). Continuous Norming (cNORM). The Comprehensive R Network, Package cNORM, available: https://CRAN.R-project.org/package=cNORM

5. Lenhard, A., Lenhard, W., Gary, S. (2019). Continuous norming of psychometric tests: A simulation study of parametric and semi-parametric approaches. PLoS ONE, 14(9), e0222279. doi:10.1371/journal.pone.0222279

6. Lenhard, W., & Lenhard, A. (2020). Improvement of Norm Score Quality via Regression-Based Continuous Norming. Educational and Psychological Measurement(Online First), 1-33. https://doi.org/10.1177/0013164420928457

See Also

rankByGroup, rankBySlidingWindow, computePowers, bestModel

Examples

## Not run: 
# Using this function with the example dataset 'ppvt'
# You can use the 'getGroups()' function to set up grouping variable in case,
# you have a continuous age variable.
model <- taylorSwift(raw = ppvt$raw, group = ppvt$group)

# return norm tables including 90% confidence intervals for a
# test with a reliability of r = .85; table are set to mean of quartal
# in grade 3 (children completed 2 years of schooling)
normTable(c(5, 15), model, CI = .90, reliability = .95)

# ... or instead of raw scores for norm scores, the other way round
rawTable(c(8, 12), model, CI = .90, reliability = .95)

## End(Not run)

cNORM documentation built on Nov. 4, 2024, 5:07 p.m.