Factores que determinaron el inicio (edad) del trabajo sexual

The Secondary Aim of this research had two main goals. One goal was to develop a latent class IRT model, similar to those used for the analyses in the Primary Aims, for scales comprising mixed item types – specifically, a scale with six Likert-type items and a single count item that exhibits inflation and heaping. The other goal was to evaluate the contribution of the single count item to the scale’s precision of measurement: Does including the count item reduce the posterior standard deviations associated with scale scores?

The first goal was met with moderate success: It is possible to estimate a latent class IRT model using a scale that comprises mixed item types. Even though the scale used includes just one count item, the proportions of individuals belonging to the exact count and round- ing/selected response classes were estimated successfully. Unlike the results from the analysis of the scale comprising only count items, which suggest that the majority of respondents treat count items as open-ended scales and utilize the full range of possible responses, analysis of the scale with mixed item types suggests that a large majority of respondents – 75% – treat the count item as a multiple choice question (or round their answers to the nearest multiple of five). The reversal of latent class estimates may be due to differences in the structure and item order- ing of the two scales. The scale used to address the Primary Aims comprises only count items; however, the scale used to address the Secondary Aim comprises six Likert-type items and one count item, in which respondents answer the six Likert-type questions before responding to the count item. It is likely that the response options of the Likert-type items, which are categorized into five increasing units of time along a continuum, primed respondents to categorize the open- ended count item into similar categories, explaining the very low proportion of people estimated to belong to the exact count class. In completing the questionnaire composed of only count items, it is less likely that respondents were conditioned to categorize their responses, possibly explaining the much larger proportion of people who used the full range of the count scale.

While priming with Likert-type items may explain the division of latent class proportions, it should be emphasized that with only one count item included on the scale, the latent class estimates are likely unstable. Due to an apparently multimodal and ridged log likelihood surface,

there were several stationary points that thenlmoptimizer treated as maxima. Multiple sets of

and depending on the starting values used, the division of proportions between the exact count and rounding/selected response classes varied widely. Regardless of whether the parameter estimates presented here are the correct solution, the latent classes are probably not well-defined. Additionally, the zero and maximum latent classes carry less meaning on scales of mixed item types. While it is still possible to define the zero and maximum classes as comprising the most extreme responses, endorsing a Likert response of “None of the time” is qualitatively different from choosing a count response of 0 days. Likewise, selecting a Likert response of “All of time” does not necessarily suggest the same symptom severity as reporting a count response of 30 days. For all of these reasons, researchers should proceed more cautiously in interpreting latent classes on scales with heterogeneous response formats, especially in the presence of only a single count item.

An additional goal of this research was to evaluate the inclusion of count items on scales with mixed item types. The results suggest that including a count item can substantially improve the precision of measurement, with some posterior standard deviations dropping by as much as 50%; however, this statement requires some important qualification. First, measurement precision is only substantially improved for people belonging to the exact count class; this class comprises a mere 7% of the population. For the remaining 75% of the scoring sample – those who either round their answers or treat the item as multiple choice – the contribution of the count item is trivial, and it may not be worth the additional effort to include the count item on the scale. However, if the researcher believes that members of the exact count class report counts according to a strict count process, such as that dictated by the Poisson IRT model, the count item can be very informative. Second, as mentioned in the earlier discussion of the limitations of using scale scores from latent class IRT models, it is impossible to determine which people with a multiple-of-five count response belong to the exact count class; thus, the large reduction in posterior standard deviations cannot be of practical benefit in assigning scores to individuals in the sample. A clinician may decide to use the exact count scoring method only for the 546 individuals who must belong to the exact count class because their count responses are non-multiples-of-five; however, this is not entirely accurate, as it excludes 1.5% of the population with multiple-of-five responses who also belong to the exact count class, and it creates an asymmetrical scoring approach that may make it difficult to compare individuals in

the sample.