TY - JOUR
T1 - Path analysis and residual plotting as methods of environmental scanning in higher education
T2 - An illustration with applications and enrollments
AU - Morcol, Goktug
AU - McLaughlin, Gerald W.
PY - 1990/12
Y1 - 1990/12
N2 - In this study we propose using path analysis and residual plotting as methods supporting environmental scanning in strategic planning for higher education institutions. As an illustration, path models of three levels of independent variables, that is, socioeconomic background, current economic variables, and educational variables, are developed. The dependent variables measuring applications and enrollments at a research university, Virginia Tech, and enrollments at four-year institutions in Virginia are regressed on the independent variables. The residuals from the multiple regression models are plotted on the county maps of Virginia to identify the geographic regions in which the applications and enrollments at Virginia Tech and the enrollments in colleges and universities of Virginia are higher or lower than expected according to the models. The implications of the variables in the models and the geographic distributions of residuals for strategic planning decisions are discussed.
AB - In this study we propose using path analysis and residual plotting as methods supporting environmental scanning in strategic planning for higher education institutions. As an illustration, path models of three levels of independent variables, that is, socioeconomic background, current economic variables, and educational variables, are developed. The dependent variables measuring applications and enrollments at a research university, Virginia Tech, and enrollments at four-year institutions in Virginia are regressed on the independent variables. The residuals from the multiple regression models are plotted on the county maps of Virginia to identify the geographic regions in which the applications and enrollments at Virginia Tech and the enrollments in colleges and universities of Virginia are higher or lower than expected according to the models. The implications of the variables in the models and the geographic distributions of residuals for strategic planning decisions are discussed.
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U2 - 10.1007/BF00992621
DO - 10.1007/BF00992621
M3 - Article
AN - SCOPUS:34249953569
SN - 0361-0365
VL - 31
SP - 555
EP - 572
JO - Research in Higher Education
JF - Research in Higher Education
IS - 6
ER -