Path analysis and residual plotting as methods of environmental scanning in higher education: An...

Research in Higher Education, Vol. 31, No. 6, 1990

PATH ANALYSIS AND RESIDUAL PLOTTING AS METHODS OF ENVIRONMENTAL SCANNING IN HIGHER EDUCATION" An Illustration with Applications and Enrollments

Goktug Morcol and Gerald W. McLaughlin

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.

Strategic planning efforts in higher education institutions have intensified in the last two decades. The causes of this trend are attributed to the new economic and social environment in which these organizations are operating. There are differing views about the nature and causes of the new environment. It was hypothesized in the 1970s that the enrollments in higher education would suffer from the demographic changes and mainly the declines in the number of 18-year-olds in the population in the 1980s and 1990s (Hoffman, 1980; Barenbaum and Ricci, 1982; Breneman, 1983; Morrell, 1988). Whether the projected declines have materialized or will materialize is a subject of debate. While Chapman (1979); Krohn and Gruttadauria (1985); King, Kobayashi, and

Goktug Morcol and Gerald W. McLaughlin, Virginia Polytechnic Institute and State University. Address correspondence to: Dr. Gerald W. McLaughlin, Office of Institutional Research and Planning Analysis, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.

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0361-0365/90/1200-0555506.00/0 @ 1990 Human Sciences Press, inc.

556 MORCOL AND McLAUGHLIN

Bigler (1986); and Rhiel and Chaffin (1987) report that enrollments have declined, Marshall and Delman (1984) mention that the early projections made in the 1970s have not been evidenced yet. Wilson (1989) reports that enrollments have increased at most universities in 1989, contrary to the early projections. On the other hand, Morgan (1982), and Larson, Milton, and Schmidtlein (1988) point out that colleges and universities are having some difficulties in the new environment, not necessarily due only to the demographic changes but also to the unstable economic conditions and unsettling government practices.

While the debate continues, it has been well established in the literature that there is an increasing tendency among higher education institutions to devote considerable attention to strategic planning and marketing approaches in this new environment (Kotler and Murphy, 1981; Morrison and Cope, 1985). Consequently, it is reported that many institutions have improved their enrollment figures with these new approaches (Preble and Rau, 1986).

The effectiveness of strategic planning lies, at least partially, in the fact that it places its emphasis on the external environment of an organization, and assumes that organizations have open boundaries (Morrison and Cope, 1985). In such an "open system" approach "environmental scanning" or "environmental assessment" becomes a crucial activity in the early stages of the planning process (Heam and Heydinger, 1985). Environmental scanning (assessment) involves identifying and analyzing the economic, demographic, social, political, and technological trends surrounding the organization (Morrison, Renfro, and Boucher, 1983; McCune, 1986; and Clagett, 1988). The techniques employed aim at uncovering the patterns of relationships between social, economic, demographic, and technical variables.

The following illustrates a method that identifies the economic, demographic, and social variables impacting applications and enrollments at a higher education institution, and uncovers the pattems of relationships among these variables. The path models use three levels of variables (socioeconomic background, current economic background, and educational background) that are hypothesized to affect the applications and enrollments at a major research university, and the enrollments at the 4-year higher education institutions in the state. The latter model is developed to make comparisons with the university. The residuals from the direct effects in the path models are plotted on Virginia county maps to identify the geographic regions in which the applications and enrollments are higher and lower than expected on the basis of these models.

The results from these analyses can be used in the environmental scanning phase of strategic planning. The variables in our analyses are not ones that can be manipulated by the university. They do, however, indicate the external factors and trends to which the university will need to adjust itself. The maps indicate the geographic distributions of the applications and enrollments

ENVIRONMENTAL SCANNING 557

controlled by the variables in the models. The university may also choose to focus its future recruitment efforts on the regions in which the residuals are negative (i.e., performance is lower than expected).

METHOD

As Morrison, Renfro, and Boucher (1984, p. 44) mention, causal regression models are used to explain complex and dynamic social, economic, and demographic trends impacting university enrollments. In an earlier study, Hoenack (1968) tested the models of the enrollments at certain universities in California. Enrollments were regressed on independent variables such as academic ability of high school graduates, tuition rate, opportunity costs, unemployment rate, and population size, using high school districts as the unit of analysis. Corazzini, Dugan, and Grabowski (1972) developed a model of enrollments at the national level, and tested it using the states as their unit of analysis. Their independent variables were academic ability, sex, parental education, tuition rate, opportunity costs, unemployment rate, and population size. Strickland, Bonomo, McLaughlin, Montgomery, and Mahan (1984) tested a series of enrollment models for Virginia and certain higher education institutions in this state, using geographic municipality (i.e., county or city) as their unit of analysis. Their independent variables were eligible population (number of high school graduates), ability levels of high school graduates, proportion of adults completed one year or more of college education, median family income, cost of college attendance, average wage, unemployment rate, and municipality's characteristic as being urban or rural.

In this study, path analysis was used to identify the patterns of relationships between the applications or enrollments and the independent variables. Path analysis was preferred over simple multiple regression analysis, because it provides additional information about the indirect effects of independent variables on the dependent variable. This is better for environmental scanning, since the indirect effects of key variables may be masked in a simple regression analysis.

The unit of analysis was municipality (i.e., county or city), as in Strickland and her colleagues' study. The data on the 135 municipalities (counties and cities) of Virginia were used in the analyses. Clifton Forge was excluded from the analyses due to the lack of data on several variables. This unit was selected for the study, because the objective was to uncover the geographic distribution of applications and enrollments controlled by the independent variables. Using such a unit made it possible to plot the residuals for each county in the state, and thus, to find out the patterns of distributions of enrollments.

The variables were selected in accordance with this aggregate unit of

558 MORCOL AND McLAUGHLIN

analysis. They reflected the percentages, mean values, and totals for the units. The three levels of variables used in the path analyses are shown in Figure 1.

As Figure 1 indicates, the relationships between the variables were predicted in a block-recursive path model. Block-recursive models allow obtaining the estimates of the extent to which intervening variables account for relationships among prior and subsequent variables, that is, the indirect causal effects, in addition to assessing the direct net causal effects (Wolfle, 1980). As Wolfle points out, block-recursive models also allow the researcher to examine the residuals of blocked variables for covariation, and assess the extent to which zero-order associations among particular blocked and subsequent variables are accounted for by associations among other blocked variables. However, in this study we limited our analyses with the direct and indirect effects on dependent variables, because our main objective was not to set up an explanatory model but to help strategic planning through predicting some trends.

The direct effects are the standardized regression coefficients computed in each analysis. The indirect effect of a particular variable on the dependent variable is the sum of the products of path coefficients connecting these two via intervening variables, according to the fundamental theorem of path analysis (Wolfle, 1980).

The main sources of variables were County and City Data Book, 1988 by the U.S. Department of Commerce, the report titled Covered Employment and Wages in Virginia by Virginia Employment Commission, Annual Report: Fiscal Year 1988 by Virginia Department of Taxation, Facing Up-22, 1986-87 School Year by Virginia Department of Education, and the reports by the Office of Institutional Research and Planning Analysis of Virginia Tech. More specific information about the variables can be obtained from the authors upon request.

The variables, population density, percent farm earnings, and percent employment in agriculture and mining, were used in the equations as the measures of each municipality's characteristic of being urban or rural. The logarithm of total population was preferred over direct number of total population for the analyses, because logarithm of a variable with large numbers yields a better estimate when the dependent variable and others are mostly ratios and percentages.

The path models were tested on six dependent variables: the enrollments at four-year colleges in Virginia and five variables for Virginia Tech covering the stages of the process from graduation from high school to enrollment at the university. For each model, a series of stepwise regression analyses (backward elimination) was utilized with the full set of variables. The variables in the prior blocks were regressed on each one of those in the subsequent blocks. The independent variables that did not contribute to the model at the .05 level of significance were dropped from the respective model. The second regression analyses for each dependent variable included only those variables contributing

ENVIRONMENTAL SCANNING 559

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significantly to the model. The results reported in this paper are those of the second analyses. The models tested and their R-square values are as follows.

1. Virginia Model: Enrollments at four-year colleges as a proportion of high school graduates (R-square = .65)

2. Virginia Tech Models: a. Applications as proportion of number of high school graduates (R-square

= .62) b. Applications as proportion of number of high school graduates attending

colleges (R-square = .20) c. Students accepted as proportion of applicants (R-square = .04) d. Enrollments as proportion of high school graduates (R-square -- .32) e. Enrollments as proportion of high school graduates attending four-year

colleges (R-square = . 10)

Three models are discussed below: applications at Virginia Tech (2-a) (VTAPP), enrollments at Virginia Tech (2-d) (VTENR), and enrollments at four-year higher education institutions in Virginia (1) (VAENR). Although the enrollments at Virginia Tech model has a relatively low R-square value, it provides an opportunity to make comparisons with the Virginia enrollments model.

The residuals from the direct effects in these three models were plotted on the county maps of Virginia. Cities that were originally in the analysis as independent units were merged with the counties in which they are located, to prevent visual confusions on the maps. Thus, the original 135 units were reduced to 100 for plotting. The relative performances of each county in enrollments at Virginia Tech and 4-year higher education institutions in Virginia were obtained through these plottings.

The residuals were computed as the actual values minus values expected for the municipalities. A positive residual indicates that the proportion from a municipality is larger than expected. A negative residual indicates the opposite, that is, the proportion from a municipality is lower than expected.

RESULTS AND IMPLICATIONS

In this section basic statistics are presented for the variables along with the results from the path analyses. Table 1 shows the means, standard deviations, and numbers of observation for each variable, and the intercorrelations between them. The reader should note that the correlations between the dependent variables are identical for all three models, since the variance and covariance are the functions of the 135 municipalities and all municipalities are represented in each model.

The regression equations for the three models are shown in Table 2. Figure 2 shows the path model of applications at Virginia Tech as proportion

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TABLE 2. Regression Equations

Applications at VA Tech as Proportions of High School Graduates

Dependent Variable = - .080 Intercept .003 Percent Coll. Grads in Population .00002 AGI Per Capita

- . 118 Local Ability to Pay for Education .001 EAS Score

Enrollments at VA Tech as Proportion of High School Graduates

Dependent Variable = - .007 Intercept - .0002 Perent Blacks in Population

.000003 AGI Per Capita - .029 Local Ability to Pay for Education

.0006 EAS Score

Enrollments at Four-Year Colleges as Proportion of High School Graduates

Dependent Variable = - 11.7 Intercept .002 Population Density • 172 Percent Blacks in Population .384 Percent Coll. Grads in Population

4.52 log Total Population - .25 # Teachers per Thousand Students

.54 EAS Score

of high school graduates in Virginia. The models of the enrollments at Virginia Tech and enrollments in Virginia are shown in Figure 3 and Figure 4 respectively. The residual plottings from the enrollment models of Virginia Tech and Virginia are shown in Figure 5 and Figure 6 respectively.

It can be seen in the path models that the EAS (ability) score is a strong variable that has positive direct influences on the applications and enrollments at Virginia Tech and enrollments in Virginia. The students from the municipalities with higher average EAS scores tend to apply and enroll at Virginia Tech and the four-year higher education institutions in Virginia more than other municipalities.

Another variable that has significant and positive effects on enrollments in Virginia and applications and enrollments at Virginia Tech is the percentage of college graduates in the population. The effects of this variable on the dependent variables are both direct and indirect in these three models. The indirect effects occur through the intervening variables, AGI per capita, percent unemployment, and local ability to pay for education. These results indicate the positive role of the education of parents and others in the larger social environment on the

ENVIRONMENTAL SCANNING 563

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FIG. 2. Model of enrollments at Virginia Tech.

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college-going behavior of high school graduates. It can also be pointed out that a high percentage of college graduates in the environment both encourages applications and enrollments at colleges directly, by setting examples and

564 MORCOL AND McLAUGHLIN

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parental motivation, and creates a favorable economic environment for going to college by increasing income and decreasing unemployment.

However, the natures and magnitudes of effects of percent college graduates in the population are not the same for Virginia Tech and the four-year colleges in Virginia in general. As Table 3 indicates, the indirect effect of this variable is larger than the direct effect on the applications at Virginia Tech, while it has no direct effect on the enrollments at this university. In the case of enrollments at colleges in Virginia, the direct effect of this variable is larger than the sum of its

indirect effects. A variable distinguishing the Virginia Tech models from the Virginia model

is the adjusted gross income per capita. This variable has the strongest direct effect and a positive direct influence on the applications at Virginia Tech (Fig. 3). On the other hand, the AGI per capita did not even emerge as a significant variable in the Virginia model (Fig. 4). It would seem that Virginia Tech is preferred by students from wealthier communities where this influence is not relevant for the enrollments at four-year colleges in Virginia.

The effects of the variable percentage of blacks in the population are mixed on the enrollments at four-year colleges in Virginia. This variable has a positive direct effect but a negative indirect effect in this model. The effect of this variable is indirect and negative in the applications at Virginia Tech model. In

ENVIRONMENTAL SCANNING

TABLE 3. Direct, Indirect, and Total Effects of Variables on Dependent Variables

565

Direct Indirect Total Total Variable Effect Effect Effect Association

Applications at Virginia Tech

Farm Earnings - .02 - .02 - , 19 Agri. Employment - . 0 1 - . 0 1 - . 1 5 College Grads. .27 .30 .57 .61 Percent Blacks - . 0 5 - . 0 5 - . 2 4 log Total Population .06 .06 .24 AGI Per Capita .63 .63 .74 Unemployment - .03 - ,03 - .36 Local Ability to Pay - . 2 4 - . 2 4 ,34 EAS Score .13 .13 .50

Enrollments at Virginia Tech

Farm Earnings - .05 - .05 - . 10 Agri. Employment - .02 - .02 - . 14 College Grads. .19 .19 .36 Percent Blacks - . 18 - . 11 - .29 - .35 log Total Population .06 .06 .20 AGI Per Capita .37 .37 .40 Unemployment - . 0 6 - . 0 6 - . 2 3 Local Ability to Pay - . 2 2 - . 2 2 .08 EAS Score .27 .27 .45

Enrollments at Four-Year Colleges in Virginia

Farm Earnings --.08 - . 0 8 - . 3 9 Agri. Employment - .04 - .04 - . 17 Population Density .28 - . 0 2 .26 .58 College Grads. .27 .15 .42 .75 Percent Blacks .24 - . 18 .06 - .06 log Total Population .16 .05 .21 .47 Unemployment - .09 - .09 - .46 Local Ability to Pay - . 0 3 - . 0 3 .46 # Teachers - . 13 - . 13 - . 0 t EAS Score .43 .43 .52

the enrol lments at Virg in ia Tech mode l it has both indirect and direct effects,

which are negat ive . These f indings indicate that the applicat ions and

enrol lments at Virg in ia Tech are less in the regions that have larger percentages

o f blacks. As the total effects in Table 3 indicate, the negat ivi ty o f this var iable

566 MORCOL AND McLAUGHLIN

sharply increases at the enrollment stage. It may be interpreted that black students, mostly, do not tend to go to Virginia Tech for higher education. However, the fact that the indirect effects of this variable are mediated by EAS score in both models indicates that academic performances of students and selection processes of Virginia Tech confound the effects of student preferences in applications and enrollments.

The variables intended to measure each municipality's place on the urban-rural continuum (percent farm earnings, employments in agriculture, and population density) generally indicate that the students from urban areas tend to go on to higher education more than students from rural areas, both in Virginia in general and at Virginia Tech. The effects of these variables, however, are indirect and weak in the case of Virginia Tech. The variable population density is not even significant in the Virginia Tech models, while it has a strong and direct effect in the Virginia model. It can be pointed out that the tendency of attracting more students from urban areas in not as significant for Virginia Tech as it is in general.

The negative direct effects of the variable local ability to pay for education on the applications and enrollments at Virginia Tech, and that of number of teachers per thousand students on the enrollments at four-year colleges in Virginia, may be surprising. As the total associations, which are zero-order correlations, in Table 3 indicate, the relationships between the variables local ability to pay for education and the application and enrollment variables are positive. However, due to the other components of these correlations, that is, multicollinearity, spurious effects, and noncausal joint associations, which were not analyzed in this study, the direct effects of this variable turn out to be negative. The total association between the number of teachers per thousand students and enrollments at four-year colleges in Virginia is negative, and very small. Due to the similar multicollinearity, spurious effects, and noncausal joint associations, the negativity increases in its direct effect on the dependent variable.

The enrollments map of residuals (Fig. 5) indicates that Virginia Tech's appeal among high school graduates is the highest in the regions surrounding the location of its campus, that is, Montgomery County. The residuals are positive in these areas, which indicates that the enrollments (also applications) are higher than expected after controlling for the variables in our models.

The maps also indicate that the university's appeal is lower than expected in northern Virginia and the industrial and urban crescent of eastern Virginia (including Richmond area). In Tidewater, the residuals for the applications are slightly positive, whereas those for enrollments are negative. It can also be seen on the map that Virginia Tech does not attract as many students as expected from the coal counties of southwestern Virginia.

These trends are not similar to those of the general enrollments in Virginia, as

ENVIRONMENTAL SCANNING 567

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FIG. 5. Map of residuals from the model of enrollments at four-year higher education institutions in Virginia.

indicated in Figure 6. The residuals from the Virginia model indicate that the enrollments in four-year higher educational institutions in this state are higher than expected in northern Virginia, the Richmond area, and the Tidewater area. These trends are just the opposites of the enrollments at Virginia Tech. It also should be noted that there are large urban communities in each of the three areas.

Despite the lack of effect of population density in the Virginia Tech models,

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568 MORCOL AND McLAUGHLIN

it can be suggested that the nature of the university's appeal is somewhat regional and local. The relative weakness in the industrialized and urban/suburban areas of the East and the North might be a source of concern for a major land-grant university such as Virginia Tech. It is known that these regions are the ones in which economy is expanding and the population is growing fast. A policy implication of these findings is that Virginia Tech might want to focus its attention and energy to these regions for recruitment.

The differences between the trends in the enrollments at four-year colleges in Virginia and those at Virginia Tech on three groups of variables, that is, urban-rural, percent of blacks, and AGI per capita, can also be evaluated in this context. The impacts of being from urban/suburban areas on the applications and enrollments at Virginia Tech are weaker and indirect as we mentioned above. The implications of this coincide with those of the geographic distribution of residuals: The university might want to intensify its recruitment efforts in the northern and eastern urban/suburban areas. The negative effects of the percentage of blacks in the population on the applications and enrollments at Virginia Tech have parallel implications. Blacks in Virginia reside mainly in the eastern and northern regions. An effort to attract more black students to the university might well be focused on the same geographic regions. The strong positive effects of the AGI per capita on the applications and enrollments at Virginia Tech seem to have similar implications. If the university wants to reverse this trend and attract more students from the communities with lower income groups, it might want to concentrate its attention and resources on the same urban areas.

DISCUSSION

The primary objective of this study was to provide assistance to those performing environmental scanning as part of a strategic enrollment management process. This assistance was accomplished by identifying the variables affecting applications and enrollments at a university, and plotting the residuals from the analyses on maps to determine geographic regions in which the university "performs" better or worse than expected. The study was aimed at generalizing a methodology, rather than at generalizing the significances of variables and the relationships between them to the general population of high school graduates. The results can be compared and discussed in relation to those of the earlier studies cited in the method section with the caution that different studies used different levels of aggregation.

A concern is that such a comparison and discussion of variables could create the problem of "ecological fallacy," that is, the fallacy of making inferences from correlations based on aggregate data (Robinson, 1950). This may be a

ENVIRONMENTAL SCANNING 569

valid concern for the analyses aimed at making generalizations at the level of the individual. However, as Pedhazur points out, the interest of the analyst is not always in individual-level variables and correlations. One may be interested in ecological correlations in their own sake (Pedhazur, 1982, p. 529). This study was the result of such an interest. The unit of analysis was municipality (county or city), because this made it possible to plot the residuals of each county on Virginia maps. In addition, the data on municipalities are available through numerous sources. For environmental scanning in strategic planning, the predictions made by variables based on geographic distributions are important. Of course, if one were interested in generalizing results to explain individuals' behaviors, then individuals should be the unit of analysis.

There are similarities in the results of our study and others in regard to the positive and significant effects of ability levels of high school graduates and educational attainment levels of parents or communities on enrollments in higher education institutions. Educational a~lity appeared to be an important variable in all three of our models as well as in those of Corazzini, Dugan, and Grabowski (1972) and Strickland et al. (1984). The average educational attainment levels of the municipalities were found to have positive impacts on enrollments in Virginia in both our study and in the Strickland study. Corazzini and his colleagues found, using state as their unit of analysis, that there were positive effects of the educational attainment level of parents on enrollments at the national level.

The results of this study have some differences from previous research, as well. Hoenack's (1968) analyses, using school district as the unit of analysis, indicated that opportunity costs and tuition level were the two major and negative contributors of enrollment in Califomia. His tests failed to identify unemployment rate as a significant variable. However, Strickland and her colleagues found this variable to be a significant and positive contributor to enrollment in Virginia. In our analyses, unemployment rate appeared to be a variable having indirect and weak negative effects on enrollments and applications.

Strickland and her colleagues tested the effects of family income on enrollments and found that income was not significant. On the other hand, the effects of adjusted gross income per capita were insignificant in our Virginia model, but significant and positive in the Virginia Tech models.

Strickland and her colleagues found that students from rural municipalities tend to enroll less. The set of variables used in our analyses confirmed this finding, but most effects were indirect for both Virginia Tech and Virginia.

The effects of race were not tested in the other studies mentioned. In our analyses, percentage of blacks in municipality appeared to have mixed effects on enrollments in Virginia, while those were indirect but negative for Virginia Tech.

570 MORCOL AND McLAUGHLIN

The advantages of using path analysis instead of simple multiple regression analysis can be seen in each of the three models discussed. In the applications at Virginia Tech model, the variables, percent farm earnings, percent employ- ments in agriculture and mining, percent blacks in population, and percent unemployment, have only indirect effects on the dependent variable. The variable, percent college graduates, has both direct and indirect effects in this model. As Table 3 indicates, its total indirect effects are larger than its direct effect. In the model of enrollments at Virginia Tech, five out of the nine variables have only indirect effects on the dependent variable: percent farm earnings, percent employments in agriculture and mining, percent college graduates in the population, log total population, and percent unemployment. The variables, percen t farm earnings, percent employments in agriculture and mining, percent unemployment, and local ability to pay for education, have only indirect effects in the model of enrollments at four-year colleges in Virginia. In this model, percent college graduates in the population has large indirect effects, beside its direct effect, as Table 3 indicates.

Simple multiple regression analyses would fail to identify the variables with only indirect effects on applications and enrollments at Virginia Tech. Such analyses would also downplay the effects of percent college graduates in the population by ignoring the relatively large indirect effects this variable has on both applications and enrollments. Without identifying the indirect effects of percent blacks in the population on enrollments at four-year colleges in Virginia, the positive direct effect of this variable could lead to misleading conclusions. Such failings could have important implications for strategic planning.

The residuals plotted on the maps were not derived from the full path models. In our analyses, they were rather obtained from the models of direct effects on enrollments. Therefore, they do not provide as strong a basis for anticipating mid- and long-term trends, because some of the variables in the first block, that is, socioeconomic structure, are not represented in them. However, they provide a good deal of near-term information, and they are excellent tools for suggesting discussions of what to do in the near future.

The method of using path analysis and residual plotting can also be applied in other areas of higher education research to assist strategic planning. Those applications would include the processes in which demographic characteristics are known for groups and are anticipated to change. One such use might be identifying the patterns of donations to a university where the location of the donor (e.g., municipality) is the unit of analysis. A similar application might be looking at the need for institution to offer remedial programs, where the institution is the unit of analysis and the demographic characteristics of the institution and its environment are known.

The success of this methodology suggests it might also have value in other

ENVIRONMENTAL SCANNING 571

fields where anticipation of customer and consumer behavior is both relevant and also influenced by group characteristics. The consumption of specific products is of course an extension, particularly when the products have a history of consumption in various demographic regions. Demands for municipal services is also another natural extension where the planner is faced with shifting age and socioeconomic forces. The users of this methodology must remember that it is designed to identify issues. It does not give affirmative solutions, nor does it explain the behavior of individuals based on their personal characteristics.

CONCLUSIONS

The use of path models and residual plotting in environmental scanning have been illustrated in the case of Virginia Tech. The residual plottings and path analyses indicated the regional character of the applications and enrollments at this institution, and the most important variables contributing to them. The relative weakness of the university in certain geographic regions that are mainly urban and higher in percentage of blacks and the poor identify potential problem areas that need to be addressed in a strategic planning for the university. The methods proposed here can be used in the environmental scanning efforts in other universities and colleges.

REFERENCES

Barenbaum, L., and Ricci, R. (1982). Forecasting enrollment: An extrapolative approach. College and University 57(2): 135-142.

Breneman, D. (1983). The coming enrollment crisis: Focusing on the figures. Change 15(2): 14-19.

Chapman, R. G. (1979). Pricing policy and the college choice process. Research in Higher Education 10(1): 37-56.

Clagett, C. A. (1988). A practical guide to environmental scanning: Approaches, sources and selected techniques. Planning for Higher Education 17(2): 19-28.

Corazzini, A., Dugan, D., and Grabowski, H. (1972). Determinants and distributional aspects of enrollment in U.S. higher education. Journal of Human Resources 7(1): 39-59.

Hearn, J. C., and Heydinger, R. B. (1985). Scanning the university's external environment. Journal of Higher Education 56(4): 419-445.

Hoenack, S. A. (1968). Private demand for higher education in California. Unpublished doctoral dissertation. University of California.

Hoffman, R. (1980). College enrollments: Strategic planning in the 1980s. Liberal Education 66(3): 346-356.

King, K. P., Kobayashi, N., and Bigler, L. G. (1986). Factors influencing students' perceptions of college recruitment activities. College and University 61:99-113.

Kotler, P., and Murphy, P. E. (1981). Strategic planning for higher education. Journal of Higher Education 52(5): 470-489.

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Received September 7, 1990