Discussion question responses and analysis

¶ … Predictions

In the past few weeks of this course, you have worked on a Mini-Project in which you tested a data collection tool. Before you tested your data collection tool, you made some predictions about what themes and patterns you expected to emerge from the data. For this Discussion, you will analyze the accuracy or inaccuracy of your predictions.

To prepare for this Discussion:

Review the required reading for this week.

Review the predictions you made in the Week 6 Application about the themes and patterns you expected from the data generated by your data collection tool test. Did your predictions come true? To what extent? Where did the two differ? To what degree? What was your reaction to this discovery?

What does this result tell you about qualitative research, data collection, and data analysis? What conclusions can you draw?

With these thoughts in mind:

Post by Day 3 a 2- to 3-paragraph comparison of your predictions about themes and patterns in data to your actual results. Conclude your posting by describing what these results tell you about qualitative research, data collection, and data analysis.

My Response:

Revisiting predictions: Human trafficking

My initial prediction regarding the variables of human trafficking and gender was that there would be a much higher percentage of women vs. males trafficked. The precise gender proportions regarding trafficking have been difficult to determine, according to the existing literature. After all, the numbers of victims counted as being trafficked can only consist of persons who have been apprehended or rescued by law enforcement. Since women are more likely to be trafficked as part of the sex trade and this specific industry may be more apt to be investigated (versus the agricultural or garment industries employing undocumented coerced labor, or domestic employment using forced labor) this may cause a disproportionately large number of female victims to be accounted for in the results -- males are more likely to be trafficked into other fields as forced manual labor, versus the sex trade. These males go undetected, and are not available for study (Hepburn & Simon 2010).

In my own initial data search, my findings were consistent with other qualitative inquiries which found women to be disproportionately represented in all industries involving trafficking, but particularly the sex trade. However, I am mindful that selection bias may be at work, given that with any qualitative study on trafficking, there is a large underground economy which invariably escapes the detection of researchers. I can only present my findings as noteworthy of the fact that of the industries which law enforcement has been able to scrutinize and of the proportion of rescued workers, there is a greater percentage of females.