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Descriptive Statistics. Select one or more variables. Click the Run button. Differential Item Functioning. Create a matching variable — If a matching variable does not exist in your data you can create one by computing a sum score.

See the instructions for Test Scaling for directions on how to create a sum score. If a matching variable exists, then use it in the next step.

Choose thick or thin matching — Thin matching involves all levels of a sum score. To use thin matching, select a sum score variable as your matching variable in a DIF analysis. This method provides the best control over the measured trait, but it may result in sparse tables and omitted responses. Think matching preserves more of the data but gives you less control over the measured trait. For think matching group examinees into ordered groups such as deciles.

Use the Deciles option of the Ranking procedure to rank examinees into ten groups. Use the new decile variable as your matching variable in the DIF analysis. Select the items you would like to study and move them to the top right list by clicking the first select button. Select the matching variable and move it to the Matching Variable field by clicking second select button.

Select the DIF grouping variable and move it to the Group By field by clicking the last select button. An example grouping variable is gender. Identify the Focal and reference group codes that are in your grouping variable. For example, the code F might indicate females in your DIF group variable and the code M might represent males.

The case of the focal and reference group codes must match the case of the values listed for the DIF group variable. IF you use the wrong case, the program will not recognize the values in the group variable. You can run the analysis at this point, but you may want to change some of the default options. Binary item effect size — The default value is Common odds ratio option. This statistic ranges from 0 to positive infinity and has an expected value of unity.

To use a more symmetric effect size, choose the ETS Delta option. Show frequency tables — Select this option to display frequency tables for all levels of the matching variable. Choosing this option will greatly increase the output. Score as zero — Select this option to score missing item responses as zero points. If not selected examinees missing an item response for an item will be omitted from the analysis of that item. If you would like to save item statistics in a new database table, click the Save button and type a name for the new table.

Frequency Tables. Item Analysis and Reliability Estimation. Choose the items you would like included in the analysis. Note: If no variables appear in the dialog, you have not provided item scoring. See Item Scoring in this guide for more information. You can run the analysis with the default options, or change them to suit your needs. The available options are: Compute item statistics — Select this option to include item statistics.

If not selected, only reliability estimates will be computed. Item deleted reliability — Choose this option to see a table with item deleted reliability estimates. The table will show reliability estimates when each item has been omitted from the analysis. This helps you choose which item to remove to improve reliability. All response options — If selected, the output will show a complete distractor analysis. If not selected, only item difficulty and discrimination for the correct answer binary item or overall item score polytomous item will be computed.

Windows Zip File. This file is a zip file that can be extracted to a location on your computer. It is a good option if your IT manager does not allow you to use executable files. Mac OSX Installer. It is for recent versions of Mac OSX. This file file that can be extracted to a location on your computer. This file does not include the JRE. Linux Installer. Linux sh File. User Manual Applied Measurement with jMetrik is the official user guide.

The current release of jMetrik version 3. Traditional psychometric methods in jMetrik include classical item analysis, reliability estimation and differential item functioning, while modern measurement methods include nonparametric item response theory and the Rasch family of item response models.

Estimated parameters for these item response models are comparable or even identical with a very small converge criterion to those produced by Winsteps Linacre, IRT scale linking is possible using fixed common item calibration, concurrent calibration, or separate calibration with the Stocking-Lord or Haebara method.

Finally, the current version also includes maximum likelihood, maximum a posteriori and expected a posteriori methods for estimating person scores. The upcoming version 4 of jMetrik provides new features for item response theory and factor analysis. Newly implemented marginal maximum likelihood estimation makes available a wide array of item response models such as the 4PL, 3PL, 2PL, and Rasch models for binary items and the generalized partial credit and partial credit models for polytomous items.

These new features are also tightly integrated with the existing scale-linking and person-scoring procedures currently found in the software. Exploratory factor analysis - using minres, maximum likelihood and principal components - expands the measurement capabilities of jMetrik into multiple dimensions. These methods are accompanied by a variety of rotation methods and the ability to compute polychoric and polyserial correlations.

An early release of version 4 is available for download , but it will be in development for another month or two before its official release. A key benefit of jMetrik is that it uses a single framework to combine psychometric methods that have traditionally required multiple programs. This feature allows a researcher to quickly transition from one method of analysis to another e. This tight integration contrasts with other software. For instance, a researcher not using jMetrik might need up to three programs to estimate item parameters and conduct scale linking.

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