com.itemanalysis.psychometrics.reliability.ReliabilityInterval.java Source code

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Here is the source code for com.itemanalysis.psychometrics.reliability.ReliabilityInterval.java

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/*
 * Copyright 2012 J. Patrick Meyer
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package com.itemanalysis.psychometrics.reliability;

import org.apache.commons.math3.distribution.FDistribution;

import java.util.Formatter;

/**
 *
 * Confidence interval is based on the sampling distribution derived for
 * Cronbach's alpha. It may or may not be appropriate for other methods
 * of estimating reliability.
 *
 *
 * @author J. Patrick Meyer <meyerjp at itemanalysis.com>
 */
public class ReliabilityInterval {

    private double sampleSize = 0;
    private int precision = 4;
    private double numberOfVariables = 0;
    private ScoreReliability reliability = null;
    private boolean unbiased = false;

    public ReliabilityInterval(ScoreReliability reliability, double sampleSize, double numberOfVariables,
            boolean unbiased) {
        this.reliability = reliability;
        this.sampleSize = Double.valueOf(sampleSize).intValue();
        this.numberOfVariables = numberOfVariables;
        this.unbiased = unbiased;
    }

    public double[] confidenceInterval() {
        double N = sampleSize;
        double nI = numberOfVariables;
        double df1 = N - 1.0;
        double df2 = (N - 1.0) * (nI - 1.0);
        double[] ci = new double[2];
        FDistribution fDist = new FDistribution(df1, df2);
        try {
            ci[0] = 1.0 - ((1.0 - reliability.value()) * fDist.inverseCumulativeProbability(0.975));
            ci[1] = 1.0 - ((1.0 - reliability.value()) * fDist.inverseCumulativeProbability(0.025));
        } catch (Exception ex) {
            ci[0] = Double.NaN;
            ci[1] = Double.NaN;
        }

        return ci;
    }

    public String print() {
        StringBuilder builder = new StringBuilder();
        Formatter f = new Formatter(builder);
        String f2 = "";
        if (precision == 2) {
            f2 = "%.2f";
        } else if (precision == 4) {
            f2 = "%.4f";
        }

        f.format("%18s", "95% Confidence Interval: (");
        f.format(f2, this.confidenceInterval()[0]);
        f.format("%2s", ", ");
        f.format(f2, this.confidenceInterval()[1]);
        f.format("%1s", ")");

        return f.toString();
    }

}