Generate Regression Norms for a Single Variable — cont_norms_single

This function generates regression norms and performs significance testing for an individual's score on a predictor variable. It calculates the regression equation, standard errors, and significance levels based on summary statistics from a control sample.

Usage

cont_norms_single_pred(
  ctrl.x.mean,
  ctrl.x.sd,
  ctrl.y.mean,
  ctrl.y.sd,
  r,
  n,
  x,
  y,
  conf.level = 0.05,
  direction = "lower",
  dp = 4,
  x.name = "X",
  y.name = "Y"
)

Arguments

ctrl.x.mean: Mean of the predictor variable in the control sample.
ctrl.x.sd: Standard deviation of the predictor variable in the control sample.
ctrl.y.mean: Mean of the criterion variable in the control sample.
ctrl.y.sd: Standard deviation of the criterion variable in the control sample.
r: Correlation coefficient between the predictor and criterion variables in the control sample.
n: Sample size of the control group.
x: Score of the individual on the predictor variable.
y: Obtained score of the individual on the criterion variable.
conf.level: Confidence level for the significance test. Default is 0.05.
direction: Direction for the significance test. Options are "lower" or "upper". Default is "lower".
dp: Number of decimal places.
x.name: Name for the predictor variable. Default is "X".
y.name: Name for the criterion variable. Default is "Y".

Value

A list containing calculated values and results.

Examples

cont_norms_single_pred(
  ctrl.x.mean = 63.8,
  ctrl.x.sd = 8.42,
  ctrl.y.mean = 41.3,
  ctrl.y.sd = 13.2,
  r = -0.58,
  n = 160,
  x = 26,
  y = 41,
  x.name = "Predictor",
  y.name = "Criterion"
)
#> Neuropsychological Regression Norms Single Case Analysis
#> 
#> Regression norms and significance testing for an individual case based on the control sample.
#> 
#> INPUTS:
#> 
#> Variable                 Mean      SD   Case's Score  n     r        t1 Score
#> ----------------------  -----  ------  -------------  ----  ------  ---------
#> Predictor (Predictor)    63.8    8.42             26  160   -0.58          26
#> Criterion (Criterion)    41.3   13.20             41                       41
#> 
#> OUTPUTS:
#> 
#> Outputs                                     Value                        
#> ------------------------------------------  -----------------------------
#> Regression equation (Predicted Y)           99.311 + -0.9093 * Predictor 
#> Standard error of estimate                  10.7869                      
#> Case's PREDICTED score                      75.6702                      
#> Discrepancy (Observed - Predicted)          -34.6702                     
#> Effect size (Z-OP)                          -3.2243                      
#> t value                                     -3.0196                      
#> One-tailed p-value                          0.0015                       
#> Two-tailed p-value                          0.0030                       
#> Estimated % with more extreme discrepancy   0.1476 %                     
#> 
#> Based on: Crawford, Garthwaite, & Porter (2010), Crawford & Garthwaite (2002), and Crawford & Howell (1998).