Study Results: Coordinator Demographics Part 1

This post is part of a series inviting discussion, comments and reflection on the results of my dissertation.

Remember as you review the results of my study that every variable was examined to see its relationship with how often the school was using curriculum videoconferencing and whether that variable could be used to predict the use of videoconferencing.

In this post, we’ll look at the relationship between the school’s use of curriculum videoconferencing and some coordinator demographic variables.

Gender

  • Schools with female coordinators use videoconferencing significantly more than the average of the other levels (r=.152, p=.012). When this variable was considered by holding all the other variables constant using multiple regression, this result became non-significant.

Coordinator’s Age

  • The coordinator’s age was negatively correlated with the schools’ use of videoconferencing (r=-.142, p=.023). The coordinator’s age dropped out of significance in the final prediction model.

Coordinator’s Level of Education

  • Schools with a coordinator with 2 years of college use videoconferencing significantly more than the average of the other levels of education (r=.223, p=.000).
  • Schools with a coordinator with a Master’s degree use videoconferencing significantly less than the average of the other levels of education (r=-.154, p=.011).
  • Schools with a coordinator with a Ph.D. use videoconferencing significantly less than the average of the other levels of education (r=-.126, p=.036).
  • When these variables were considered by holding the other variables constant using a multiple regression prediction model, the 2 year degree result became stronger (b=20.544, p=.002).

Coordinator’s Ethnicity

  • The ethnicity of the coordinator is not significantly related to the school’s use of videoconferencing.

Coordinator’s Job Title

  • The job titles of librarian, media aide, principal, district VC coordinator, and regional VC coordinator were not significantly related to the school’s use of videoconferencing.
  • Where the coordinator’s job title was paraprofessional, the schools use videoconferencing significantly more than the average of the other job titles (r=.220, p=.000).
  • Where the coordinator’s job title was teacher, the schools use videoconferencing significantly more than the average of the other job titles (r=.115, p=.010).
  • Where the coordinator’s job title was technology specialist, the schools use videoconferencing significantly less than the average of the other job titles (r=-.144, p=.016).
  • When these variables were considered by holding the other variables constant using a multiple regression prediction model, the paraprofessional (b=31.413, p=.013) and teacher (b=11.752, p=.021) job titles were significant contributors to the model.

Recommendations & Discussion

  • What recommendations would you draw from these results?
  • Why do you think schools with a tech specialist as VC coordinator might use videoconferencing less?
  • Why might a school with a teacher or paraprofessional as VC coordinator use videoconferencing more?
  • Note the small r’s in all of the results. Clearly these results are not true in every situation. What exceptions do you know of to these results?
  • My methodologist said that in any study, you’ll find a significant difference between the genders. Do you think there is a difference between a male VC coordinator and a female VC coordinator? How so?

Please comment!

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