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satisfaction; and (d) sociocultural living demands and community integration in rural native
cultures.
Data Analysis
Descriptive analysis was used to describe archival data. The Pearson correlation
coefficient (r) was calculated using SPSS between retention and student achievement. Statistical
significance (p) is reported at the 95% confidence level. The audio-recorded data were
transcribed after the interviews were competed to identify factors related to teacher retention and
working in rural schools. The researchers read the transcripts and the field notes to identify
themes through inductive coding and sorting (Berg & Lune, 2004). Peer debriefing was used
during transcription and analysis to increase credibility of the study and ensure that analysis were
grounded in data (Kleinsasser, 2000).
Results and Discussion
Archival data presented in Table 1 indicates that average teacher retention rates of rural
districts (< 77%) are significantly lower than the average rate in the three urban districts (>
92%). In addition, the retention rates in rural districts varied significantly (see SD) by school
year. Calculating a Pearson correlation coefficient r shows a statistically significant correlation
between average teacher retention and average percent proficiency in reading over the same
four-year time frame, school years 2010-2013 for the 10 study districts: r = .623 (p < .054).
Similarly in math, r = .665 (p < .036) for average teacher retention and average percent
proficiency. The correlation coefficients were higher when including the three urban districts.
Correlation between average teacher retention and average percent proficiency in reading is r
= .826 (p < .001) and between average teacher retention and average percent proficiency in math
is r = .768 (p < .002). Overall, data indicates a significant difference in teacher retention rates
between rural and urban areas and a statistically significant correlation between teacher retention
rates and student achievement.