The College Choice Process: Some Empirical Results

Robert E. Spekman, University of Maryland
James W. Harvey, University of Maryland
Paul N. Bloom, University of Maryland
ABSTRACT - As consumer researchers expand the range of consumption decisions they study, subjects like the college choice process should receive a considerable amount of attention. This paper reports on a study which attempted to apply thinking from the consumer research literature to the problem of understanding the college choices of high school students. The results suggest that consumer research approaches can add much to what is known about this complex process.
[ to cite ]:
Robert E. Spekman, James W. Harvey, and Paul N. Bloom (1980) ,"The College Choice Process: Some Empirical Results", in NA - Advances in Consumer Research Volume 07, eds. Jerry C. Olson, Ann Abor, MI : Association for Consumer Research, Pages: 700-704.

Advances in Consumer Research Volume 7, 1980     Pages 700-704


Robert E. Spekman, University of Maryland

James W. Harvey, University of Maryland

Paul N. Bloom, University of Maryland


As consumer researchers expand the range of consumption decisions they study, subjects like the college choice process should receive a considerable amount of attention. This paper reports on a study which attempted to apply thinking from the consumer research literature to the problem of understanding the college choices of high school students. The results suggest that consumer research approaches can add much to what is known about this complex process.


In his recent Presidential Address, Kassarjian (1976) cautioned the ACR membership about their tendency to focus on insignificant consumption decisions. While there clearly has been much research attention paid to rather mundane consumer decisions, a growing number of consumer researchers have, in accordance with Kassarjian's remarks, "broadened" their horizons to encompass such areas as voting behavior, safety behavior, and family-planning behavior (Zaltman and Sternthal 1975). Moreover, the college choice decision has received some attention (Punj and Staelin 1978; Vaughn, Pitlik and Hansota 1976). It has been recognized that research in this area has the potential of adding new insights into how consumers search for and process information when making major purchases. It has also been recognized that research on college choice can provide guidance to colleges and universities in their efforts to deal with a diminishing pool of applicants (Educational Testing Service 1976).

The purpose of this paper is to show how some recent thinking in the consumer research literature can contribute to our understanding of the college choice process. The paper reports on an investigation of: 1) the salience of various college/university attributes on a prospective student's attitude toward attending a particular college or university; 2) the impact of significant others on the prospective student's decision making process; 3) the differences students exhibit in attribute salience when evaluating three different types of schools; 4) the predictive validity of various attitude models of college choice (for different target segments and different schools); and 5) the cognitive styles or decision rules utilized by various segments of prospective students.


A review of over 35 studies in the education literature (not all are cited here) reveals the most prevalent research design to be cross-sectional and correlational in nature. Questioning either recently enrolled or recently admitted students, these studies focus typically on one of the following aspects of the college choice process: 1) what demographic characteristics are associated with a student's college choice behavior (e.g., Stordahl 1970); 2) what attributes of a college/university are important to a prospective student (e.g., Sternberg and Davis 1978); 3) who are the other influences in the decision-making process (e.g., Kerr 1962); and 4) some combination of the above (e.g., Sullivan and Litten 1976; Tillery 1973).

While this body of research furthers our understanding of the various attributes, influences, and individual moderators affecting a student's evaluation of a college or university; these studies are not predictive and provide little insight into the dynamics of the college choice decision. More research is needed which focuses on how college choice can be explained as a process, and on how college choice can be predicted. Recently, Punj and Staelin (1978) reported results suggesting that it is possible to predict student choice behavior. Although Punj and Staelin's (1978, p. 589) stochastic utility model can be used to depict (i.e., forecast) the college choice process, they state explicitly that the model cannot represent "the actual cognitive processes by which prospective students select the school of their choice."

A conceptual and methodological guide to delving into the cognitive processes of college choice can be found in Fishbein's multi-attribute attitude model (Fishbein and Ajzen 1975). Fishbein's model hypothesizes that attitude toward an act (here, enrolling at a particular college) is a function of: a) the strength of beliefs about an act, and b) the evaluative aspects of these beliefs. Within the college choice context attitude toward an act is expressed algebraically as follows:



"act = attitude toward the act of enrolling at a particular college

Bi = likelihood an important concept (e.g., getting a quality education) is linked to the behavior

ei = evaluation (i.e., good-bad) of the concept linked to the behavior.

The study described below utilizes a Fishbein approach to explore the college choice process.


The Sample

This research was supported by a grant from the undergraduate admissions office of a large, State university. During the summer of 1978, questionnaires were mailed to 2,000 randomly selected high school students from the state in which the university is located. Using a commercially-prepared mailing list, the sampling frame consisted of 1,000 recent high school graduates(referred to as seniors) and 1,000 students just completing their junior year (referred to as juniors). Of the 583 usable, returned questionnaires (a response rate of 29%), 541 came from juniors and the remainder from the seniors. Student residence was well distributed across the state with clusters in the heavily populated areas. Also, the racial composition of the sample matched the percentage of minority citizens found in the population-at-large. Moreover, the distribution of self-reported Scholastic Aptitude Test (SAT) scores seemed to coincide with the test results made available from the Educational Testing Service. Although the rather low response rate raises the threat of a non-response bias in the sample, the results of a telephone survey of all non-respondents did not suggest such bias was present.

Dependent Measure

Attitude toward the act Aact.  Aact was measured along a seven point scale ranging from "very unfavorable" to "very favorable." Respondents were asked how favorable they felt toward their enrolling at X University. The three schools of interest were State University (the main campus in the State system), State College (a 4-year college in the State system), and Private University (a nationally recognized private institution located in the State).

Measures of Independent Variables

As mentioned previously, Fishbein's Acct model is based on the strength of beliefs (Bi) about an act and the evaluative aspects (ei) of these beliefs. Based on the results of several focus group interviews with current college students and high school students and on previous research (Sullivan and Litten 1976; Vaughn et al. 1976), seventeen beliefs emerged as potential determinants of college choice. These beliefs can be found in the list of statements in Table 1 (see below). It should he noted that there are essentially two categories of beliefs. One group, consisting of 13 items, refers explicitly to attributes associated with the college or university (e.g., quality academics, national reputation). The remaining four items reflect the notion of normative beliefs in that reference is made to a particular college receiving a positive recommendation from significant others (i.e., friends, teachers, counselors, and parents). The precise operationalization is described below.

Belief (Bi).  Each respondent was questioned regarding the likelihood of each of the three schools possessing each of 17 attributes. Each attribute was measured on a seven point scale ranging from "very unlikely to have" to "very likely to have."

Affect (ei).  Affect was measured on a seven point scale of desirability ranging from "very bad" to "very good." Measured independently of the other constructs, a question asked the respondents: "How good or bad would it be for you if you enrolled at a school that had ...?" This question was then followed by each of the 17 attributes. It should be noted that each of the independent variables as well as the dependent variables (Aact) conformed to the response categories used by Fishbein (Fishbein and Ajzen 1975).

Demographic Segmentation Variables

One of the key objectives of this paper is to determine any differences which may exist in the decision process across important types of prospective college applicants. Exploratory research and other college choice literature suggested the use of the following demographic breakdowns:

Both SAT score and Grade Point Average (GPA) were used independently as surrogates for the degree of academic promise in a prospective student.

Year in School (i.e., junior versus senior) served as a proxy for a prospective student's stage in the college choice process.


Involvement as a Segmentation Variable

The focus group interviews clearly revealed that some students appeared to take the college choice process more seriously than others. In fact, many of the overt differences between these students seemed to be captured by the concept of "involvement" (see DeBruicker and Robertson 1977). That is, the more "highly-involved" students revealed a more intensive and deliberate searching for and processing of a number of different attributes and characteristics regarding the various colleges and universities. In order to capture both the behavioral and cognitive components of involvement, the construct was operationalized as a composite variable consisting of measures of the following: 1) the number of schools applied to, 2) the number of schools visited, 3) how long the student had been considering colleges, 4) how carefully the student felt he/she had considered college alternatives, 5) how important the student felt the college choice decision to be, and 6) how certain the student was about the academic major he/she wanted in college. Each of the measures was given equal weight and was then summed. The total score was used to indicate a student's degree of involvement. Once again, the idea was to explore the possibility of differences between high and low involved decision making about colleges.




Five major steps comprised the analysis of the data. First, a factor analysis was performed of the Biei scores to define parsimonious and orthogonal determinants of college choice. Second, a disaggregated (i.e., n factor predictor variables) stepwise regression analysis of these factors on Aact was undertaken to infer the relative importance of these factors in predicting Aact. Any apparent differences in the importance of the factors across schools were noted. Third, a comparison of the predictive validity of the models developed in steps 2 and 3 with a simple sum of all Biei pairs was conducted to make inferences as to whether an equal weight (i.e., suggesting compensatory) or an unequal weight (i. e, suggesting noncompensatory) cognitive structure was present. Last, an analysis of the different groups of prospective students was performed to reveal any important differences in attitude structure.

Factor Analysis of the Biei Measures

A factor analysis, employing varimax rotation, was utilized to identify the several independent basic dimensions defining the college choice process (Fishbein and Ajzen, 1975 p. 294). The resulting array of composite factors was then used in the Fishbein model so as to minimize the effect of covariances among the items. Table 1 shows that six factors with eigenvalues greater than one were extracted from the survey. Although the table reports only the factor analysis for the State University data, these same six independent dimensions were found in separate analyses of the State College and Private School.

Factor I (College Environment) appears to describe the general ambiance of the university with quality academics, national reputation, and friendly people given greater importance than either opportunities for part-time employment, equal opportunities for minorities, or "athletic teams I could play for." Factor II (Authority Recommendations) identifies a normative component, which adds credence to Fishbein's extended model in which Behavioral Intentions is hypothesized to be a function of Aact and normative beliefs (Fishbein and Ajzen 1975). Both number of students and size of classes load quite heavily on Factor III and are clearly indicants of the University's size.

Factor IV (Cost) reflects those attributes which may lower the expense of attending a particular college or university. Having a school close to one's home can surely lower the cost, both monetary and otherwise, of enrolling. Factor V (Social Aspects) embodies those nonacademic qualities that can attract a student to a university. For some students, the proximity of a school to an urban center and/or the number of campus parties can be important attributes. Factor VI (Friends) describes those attributes dealing with the influence of peers (i.e., recommendations from friends and friends from high school) on the college choice process.

Disaggregated Biei Model

To determine the relative importance of factors in predicting attitude toward the act of enrolling in a given school, stepwise regression using the six derived factors as predictor variables was performed. While raw-data sums of the Biei components of each factor were used, rather than factor scores, the low zero-order correlations between the six factors (range = .023 - .384; mean = .162) indicate little anticipated multicollinearity. Thus, the resulting Beta weights can be interpreted as representing any differences in attribute importance in attitude prediction (Fishbein and Ajzen, 1975 p. 311).

Table 2 summarizes the results of the regression analysis. Interestingly, differences in the importance of college choice factors may vary across schools. While all six factors are statistically significant at least once in the three school models, the order of entry is quite different for each school. For example, Size for State University, Authority Recommendation for Private University, and Environment for State College were the first factors to enter the regressions. This suggests that while six major factors may mediate school choices, the importance of these attributes is likely to change, depending on which school is being evaluated. This finding suggests that applicants are "processing by brand" rather than by attributes (see Jacoby et al 1976).



Single-Factor Summative Model

Finally, all six college choice factors were summed to form one predictor variable. Based on all beliefs and evaluations, this model corresponds to Fishbein's linear, compensatory model. Table 2 contains the Betas, F-values, and adjusted R2 for all three models. The predictive validity of this single factor (i.e., summative) model was consistently either only equal to or lower than that found in the disaggregated model. This result is utilized in the following discussion to examine further an applicant's attitude formation processes.

Attitude Formation Models

A comparison of the disaggregated add the single factor, summative models can he utilized to represent two quite different models of attitude formation. Allowing each of the six independent variables in the disaggregate model to enter the regression in a stepwise fashion and then examining the resultant Beta coefficients offers insight into the relative importance of college choice factors. If the size of Betas vary considerably across different factors and if the overall predictive validity of the model is higher than the single factor, summative model, the conclusion can be made that respondents are demonstrating significant differences in factor weightings. That is, such strongly unequal Betas suggest that school attributes may have been processed in a non-compensatory fashion (Bettman et al. 1975).



The next issue concerned those differences in attribute salience which might exist by group type. Again, some noteworthy differences occurred. First, the Black student models consistently showed a primary importance on the Authority Recommendation factor across all schools. Interestingly, the same finding occurred for the High Involvement and the Juniors. Low Involvement applicants demonstrated consistently higher attribute salience for the Cost Factor, while little differences between grade-point and SAT score groups emerged. Finally, whites, seniors, high and low grade point, and SAT score groups models resulted in attribute salience structures nearly identical to the overall model.

In the State University and State College models, the results suggest that some type of non-compensatory formation is present (see Table 2). This conclusion is based on the fact that each model has one factor (Size and Environment) which has a Beta considerably greater than others and a predictive validity greater than the compensatory model (R2 = .243 versus R2 = .192 and R2 = .251 versus R2 = .236). Conversely, little difference in either Betas or predictive validities occurred in the Private University model. This suggests that the Private University is perceived to have a diversity of attributes, no one of which clearly dominates the choice model.

Differences by Groups

To explore any possible differences in attitude structure by group type (Race, Involvement, Grade point, SAT scores, Year in School), several aspects of the student sample were examined. First, the same kind of Beta and predictive validity analysis described above was performed for each segment to make inferences as to processing models. Second, differences in the relative importance of factors was explored.

Beta weight and R2 analysis offer insights into group differences (see Table 3). First, unlike their counterparts, Blacks and high involvement groups appear to reflect uniformly non-compensatory attitude formations across all three schools. Conversely, no other groups demonstrated a trend other than that revealed in the overall analysis--namely, non-compensatory for the State schools and compensatory for the private school. This finding suggests that Blacks and high involvement students place considerably higher importance on a few factors of choice. Presumably, the school which does not "rate" well on these few dimensions would likely not be considered further despite other positive strengths.


While previous research on college choice has yielded insights into the characteristics and attitudes of students recently enrolled at or admitted to particular colleges, few studies have sampled high school students to find out about their thinking while they were actually going through the college choice process. The study reported on in this paper suggests that significant insights can be obtained by adopting a commonly-used consumer research approach in studying actual college-education "shoppers" (i.e., high school students). The results suggest that college choice is a highly complex process which probably involves a "processing by brand" (i.e., school) approach and which tends to be done using a non-compensatory evaluation procedure. However, the results also suggest that the process may differ markedly across segments of students, with different decision rules and attribute saliences being used depending on a student's race, level of "involvement," grade point average, and year-in-school. If similar findings are found in future research, it would suggest important strategic implications for college recruiting. Hopefully, other consumer researchers will address their talents to providing further clarification of this interesting, but difficult-to-understand process.


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