Interpretive Processes in Collaborative Research

Abstract This article presents data problems from a longitudinal study on teacher retention in Florida. While many researchers experience delays or problems during data collection, the experiences of this research team present serious implications for the validity of any study or state policy built on district-reported data. Cross-district and within-district factors are also discussed. Background Our research team embarked on a study to identify the variables that affect teacher retention during teacher shortages. The study focuses on a group of new teachers hired during the 2000-2001 school year. A longitudinal approach to track which of our teachers were retained in their school districts was undertaken. As a team, we identified and defined variables we wanted to track in order to develop a predictive model for districts to use in teacher recruitment. We planned to use archival data collected on these variables from our four partner school districts. All we needed to do, we thought, was to ask the districts for the information, input the data, and watch the results roll out of SPSS. That's where the plan went awry. Getting the data accurately proved to be difficult. Our data collection plan was similar to other models (Ross, 2000). We outlined a method to collect data, designed and assigned management of data collection, and created a timeline for completion. We had planned that the data collection portion of our study would take about a month. It became a year-long process during which we encountered problems that we had not anticipated--problems that could impact the validity of any policy or research based on self-reported data from school districts. Cross-district We identified several problems in data collection common among the districts with which we worked. One problem encountered was the inconsistency across districts in the definitions and categories used to enter teacher information into district databases. Each district had slightly different definitions of key terms such as "alternative certification" and "out-of-field certification". Two districts reported teachers with temporary state certificates had received those certificates through following a course-by-course option, (taking only courses required for certification) when, in fact, the teachers may have completed an approved teacher education program. Also, some districts coded teachers as teaching out-of-field when they were teaching LEP children without the necessary endorsement, while other districts did not. One district reported that their teachers went through an alternative certification program in a year when the district did not have an alternative certification program. Our experience confirms Doucette-Gates' (2000) findings regarding cross-agency data collection that "idiosyncratic vocabularies" posed problems. The number of paths and ways one might become certified in Florida causes other interesting problems as well. In Florida, teachers can become certified through completion of a college of education program, an alternative certification program, taking courses for certification, and/or taking a state test. In addition, a number of out-of-state certified teachers are added annually to Florida's teaching ranks, and the question of how these teachers were certified becomes even more difficult. We also discovered that some districts are able to issue their own emergency certification when an employee was not eligible for a state certificate by any other route. And, of course, the plethora of paths to certification caused a lack of consistency across districts in coding teacher preparation. Consistent with another study (Doucette-Gates, 2000), we found that there was no uniformity in the databases and software districts were using. School districts used different types of software to store data. Each software program analyzed data according to different categories and codes resulting in different types of reports. …