public double getStdDev(){ return sd.getResult(); }
public double getStdDev() { return sd.getResult(); }
public double getStandardDeviation(){ return sd.getResult(); }
public double getStandardDeviation(){ return sd.getResult(); }
public double getStdDev(){ return sd.getResult(); }
public double standardDeviation(){ return sd.getResult(); }
public double standardDeviation(){ return sd.getResult(); }
/** * Correct pearson correlation for spuriousness due to including the studied * item score Y in the computation of X values. This method is used for the * polyserial correlation in an item analysis. * * @return correlation corrected for spuriousness */ public double spuriousCorrectedPearsonCorrelation(){ double testSd = sdX.getResult(); double itemSd = sdY.getResult(); double rOld = r.value(); double denom = Math.sqrt(itemSd*itemSd+testSd*testSd-2*rOld*itemSd*testSd); if(denom==0.0) return Double.NaN; return (rOld*testSd-itemSd)/denom; }
@Override protected double getInitialMaximum(final StandardDeviation stats, final double total) { return (total / stats.getN()) + stats.getResult(); } }
@Override protected double getInitialMaximum(final StandardDeviation stats, final double total) { return (total / stats.getN()) + stats.getResult(); } }
public double value(){ return 1.06*sd.getResult()*Math.pow(sd.getN(),-0.2)*adjustmentFactor; }
static private Double evaluate(Collection<?> values, boolean biasCorrected){ StandardDeviation statistic = new StandardDeviation(); statistic.setBiasCorrected(biasCorrected); for(Object value : values){ Number number = (Number)TypeUtil.parseOrCast(DataType.DOUBLE, value); statistic.increment(number.doubleValue()); } return statistic.getResult(); } }
static private Double evaluate(Collection<?> values, boolean biasCorrected){ StandardDeviation statistic = new StandardDeviation(); statistic.setBiasCorrected(biasCorrected); for(Object value : values){ Number number = (Number)TypeUtil.parseOrCast(DataType.DOUBLE, value); statistic.increment(number.doubleValue()); } return statistic.getResult(); } }
public double value(){ double[] thresholds = null; thresholds = getThresholds(); double thresholdProbSum = 0.0; for(int i=0;i<thresholds.length;i++){ thresholdProbSum+=norm.density(thresholds[i]); } if(thresholdProbSum==0.0) return Double.NaN; double n = (double)freqY.getSumFreq(); double psr = Math.sqrt((n-1.0)/n)*sdY.getResult()*r.value()/thresholdProbSum; return psr; }
static private Double evaluate(Collection<?> values, boolean biasCorrected){ StandardDeviation statistic = new StandardDeviation(); statistic.setBiasCorrected(biasCorrected); for(Object value : values){ Double doubleValue = (Double)TypeUtil.parseOrCast(DataType.DOUBLE, value); statistic.increment(doubleValue); } return statistic.getResult(); } }
public double kr21(){ kr21 = new KR21(this.numberOfItems(), stats.getMean(), stdDev.getResult(), unbiased); return kr21.value(); }
public double kr21(){ kr21 = new KR21(this.numberOfItems(), stats.getMean(), stdDev.getResult(), unbiased); return kr21.value(); }
private void evaluate(){ Mean mX = new Mean(); StandardDeviation sdX = new StandardDeviation(populationStdDev); Mean mY = new Mean(); StandardDeviation sdY = new StandardDeviation(populationStdDev); ItemResponseModel irmX; ItemResponseModel irmY; for(VariableName v : sY){ irmX = itemFormX.get(v); irmY = itemFormY.get(v); irmX.incrementMeanSigma(mX, sdX); irmY.incrementMeanSigma(mY, sdY); } if(checkRaschModel()){ slope = 1.0; }else{ slope = sdY.getResult() / sdX.getResult(); } intercept = mY.getResult()-slope*mX.getResult(); }
public ConditionalSEM computeCSEM(ScoreReliability reliability, boolean unbiased){ Integer[] scores = getAllScores(); kr21 = new KR21(this.numberOfItems(),stats.getMean(), stdDev.getResult(), unbiased); CSEM = new ConditionalSEM(scores, this.computeMaximumPossibleTestScore(), reliability, this.kr21, unbiased); return CSEM; }
public ConditionalSEM computeCSEM(ScoreReliability reliability, boolean unbiased){ Integer[] scores = getAllScores(); kr21 = new KR21(this.numberOfItems(),stats.getMean(), stdDev.getResult(), unbiased); CSEM = new ConditionalSEM(scores, this.computeMaximumPossibleTestScore(), reliability, this.kr21, unbiased); return CSEM; }