Journal of Applied Measurement

GUIDELINES FOR MANUSCRIPTS

Reprinted from Smith, R.M., Linacre, J.M., and Smith, Jr., E.V. (2003).
Guidelines for Manuscripts. *Journal of Applied Measurement, 4*, 198-204.

Included in this editorial are guidelines
for manuscripts submitted to the ** Journal of Applied Measurement** that involve
applications of Rasch measurement. These guidelines may also be of use to those
attempting to publish Rasch measurement applications in other journals that may not
be familiar with these methods.

Following the guidelines, we provide a list of references that may assist individuals in gaining an overview of some of the material discussed in the guidelines. The guidelines and the list of references are by no means exhaustive. If you feel an important reference has been left out or have a recommendation for the guidelines, please e-mail us your suggestions (rsmith@jampress.org, mike@winsteps.com, or evsmith@uic.edu).

Finally, we consider this a work in progress and thank William Fisher and George Karabatsos for comments on an earlier version. We will attempt to incorporate ideas and references as we receive them. Please periodically visit the journal website at http://www.jampress.org for the most recent updates.

A. Describing the problem

1. Adequate references, at least reference to Georg Rasch (1960) when appropriate.

2. Adequate theory, at least exact algebraic representation of the Rasch model(s) used and citation for primary developer(s).

3. Adequate description of the measurement problem, including hypothesized definition of latent variable, identification of facets under investigation, description of rating scales or response formats.

4. Rationale for using Rasch measurement techniques. For example, this may include the preference for the unique properties that Rasch models embody, the goal of establishing generalized reference standard metrics, or empirical justification by performing, for example, a comparison of the generalizability of the estimated parameters obtained from competing models. Addressing the rationale for using Rasch measurement is particular important when reviewers are more familiar with the philosophy behind Item Response Theory or True Score Theory.

B. Describing the analysis

1. Name and citation or adequate description of software or estimation methodology employed.

2. Provide a rationale for the choice of fit statistics and the criteria employed to indicate adequate fit to the model requirements. This should include some acknowledgment of the Type I error rate that the critical values imply. Note. The mean square is not a symmetric statistic. A value of 0.7 is further from 1.0 than is 1.3. Using a 1.3/0.7 cutoff for mean squares uses a different Type I error rate for the upper and lower tail of the mean square distribution.

C. Reporting the analysis

1. Map of linear variable as defined by items.

2. Map of distribution of sample on linear variable.

3. Report on functioning of rating scale(s), and of any procedures taken to improve measurement (e.g., category collapsing).

Note: It is extremely difficult to make decisions about the use of response categories in the rating scale or partial credit model if there are less than 30 persons in the sample or 10 observations in each category. You might want to reserve that task until your samples are a little larger. If the sample person distribution is skewed you might actually need even larger sample sizes since one tail of the distribution will not be well populated. The same is true if the sample mean is offset from the mean of the item difficulties. This will result in there being few observations for the extreme categories for the items opposite the concentration of the persons.

4. Investigation of secondary dimensions in items, persons, etc. using, for example, fit statistics and other analysis of the residuals.

Note: All of the point-biserial correlations being greater that 0.30 in the rating scale and partial credit models does not lend a lot of support to the concept of unidimensionality. It is often the case that the median point-biserial in rating scale or partial credit data can be well above 0.70. A number of items in the 0.30 to 0.40 range in that situation would be a good sign of multidimensionality.

5. Investigation for local idiosyncrasies in items, persons, etc.

Note: Fit statistics for small sample sizes are very unstable. One or two unusual responses can produce a large fit statistic. Count up the number of item/person standardized residuals that are larger than 2.0. You might be surprised how few there are. Do you want to drop an item just because of a few unexpected responses?

6. Report Rasch separation and reliabilities, not KR-20 or Alpha.

Note: Reliability was originally conceptualized as the ratio of the true variance to the observed variance. Since there was no method in the true score model of estimating the SEM a variety of methods (e.g., KR-20, Alpha) were developed to estimate reliability without knowing the SEM. In the Rasch model it is possible to approach reliability the way it was originally intended rather than using a less than ideal solution.

7. Report on applicable validity issues

Note: This is of particular importance when attempting to convey the results of Rasch analysis to non-Rasch oriented readers. Attempts should be made to address the validity issues raised by Messick (1989, 1995), Cherryholmes (1988), and the Medical Outcomes Trust (1995). See Smith (2001) for one interpretation and Fisher (1994) for connecting qualitative mathematical criteria for meaningfulness with quantitative mathematical criteria.

8. Any special measurement concerns?

For example:
Missing data: *not administered* or what?
Folded data: how resolved?
Nested data: how accommodated?
Loosely connected facets: how were differences in local origins removed?
Measurement vs. description facets: how disentangled?

9. For tests of statistical significance, in addition to the test statistics, degrees of freedom, and p-values, we encourage authors to report and interpret effect sizes and/or confidence intervals.

D. Style and Terminology

1. Use *Score* for *Raw Score* and
*Measure* or *Calibration* for Rasch-constructed linear measures.

2. We do not encourage the use of
*Item Response Theory* as a term for Rasch measurement.

3. Rescale from logits to user-oriented scaling.

4. If appropriate, attempt to convey the results in graphical format.

5. Do not use inappropriate language when discussing reliability and validity (e.g., the test is reliable and valid). It is the measures that are reliable and the inferences made from the item and person measures and fit information that are valid for specific purposes.

6. When citing formulas or equations from other author's work, please use the notation of the original author. For example, when citing the formula for the partial credit model from Wright and Masters (1982), please use β for person measures, not θ. If you decide to change notation after that citation, please explain the reason for the change.

E. Common Oversights

1. Do not take the mean and standard
deviation of point biserial correlations. The statistics are more non-linear than the
raw scores. It is best to report the median and inter-quartile range or to use a
Fisher *z*-transformation before you calculate a mean.

2. When comparing the results of several calibrations of the same data, do not use the item and person reliability as criteria for improvement. These indices suffer from the same floor and ceiling effects as their true score counterparts and hence may not accurately reflect increases in reliability. If an increase in reliability is one of your criteria for improvement, use the item and person separation indices to compare the results of multiple calibrations as these indices do not suffer from the same deficiencies.

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