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A MODEL PROGRESSION - Demographic... · PDF file A DEMOGRAPHIC MODEL PROGRESSION W. L. Li Ohio State University* During the past few years there has been considerable interest in

Aug 08, 2020





    W. L. Li Ohio State University*

    During the past few years there has been considerable interest in constructing mathemati- cal models for the study of the educational pro- cess. These models, according to their subjects, can be classified into three categories (See Wurtele, 1967). The first category may be called demographic models. They focus primarily on the educational system or some of its components, such as the flow of students, teacher - student ratio, etc. The second category are the econo- metric models which treat education as one of the several interrelated economic activities; educa- tional institutions are viewed as producers of outputs that are employed by the different sec- tors of society. The third category of models deals with the learning outcomes of individual students, or group of students. The socio- psychological aspects of educational process are strongly emphasized.

    This paper is concerned with the first cate- gory of educational models. It attempts to exam- ine student progression in an educational system from the demographic point of view. So, the sub- ject of educational process is treated in the aggregate, and the interdependencies of the edu- cational system with other sectors of society is analytically disregarded.

    A General Demographic Model

    The subject of educational process has long been of great interest to demographers. And the demographic analysis of educational process has been a great contribution to the educational planners, who must continually estimate the size of future student enrollments at different levels of the educational structure. Examples are found in the work of the Census Bureau, which in the past years has provided a continuous projection of school enrollments (Census Bureau, 1963; Siegel, 1967). A systematic exposition of educa- tional demography using the Census Bureau's sta- tistics is shown in the publication of Folger and Nam (1967).

    However, demographers are often blamed for their failure to make an accurate educational projection. It is sometimes complained that demographers have relied too much on the tech- niques of trend extrapolation. Besides, the reliability of school enrollment analysis seems to be dependent on the birth -death projection of the total population which itself may be inaccu- rate.

    Let us begin with an examination of the gen- eral demographic methodology of projecting school population. It can be best summarized in the following equation (Stone, 1966):

    * This research has been supported by the National Sciences Foundation under contract NSF- GS2630.


    (1) s n q

    where s is the total student enrollment; n is the population vector of each age group; q is, also a vector which gives the age -specific enrollment rates.

    One way to implement the population vector is to follow Leslie's matrix approach, as Stone suggests. The Census Bureau's projection tech- nique, though slightly different, nevertheless, is more or less based on trend extrapolation. Similarly, the enrollment rates are generally computed as a linear projection of the trend in observed fall enrollment rates in the past years.

    One of the main limitations of such model, as Correa points out (Correa, 1967:34), is that the projected educational enrollments closely reflect the differences in the population struc- ture and they are inappropriate to be used for temporal or spatial comparisons. There is anoth- er important limitation of such a model. It is that the model fails to give sufficient attention to the underlying dynamics of the educational process. This weakness is similar to the econo- metricians' construct of labor force function, which yields very limited knowledge of how the size of the labor'force is determined by popula- tion structure.

    Structure of Educational System

    In order to reach a more realistic projec- tion of student population, we propose to begin with an examination of the underlying mechanism of educational process. The major part of this paper attempts to assess the underlying dynamics of cohort student progression. Let us first con- sider the following Lexis diagram which repre- sentq the progression of a student cohort (Fig. 1).1/

    The example shows that the student cohort first enters the educational system in 1954. With the increment of years, its size changes from the first grade to the last grade. Symboli- cally the size of the cohort may be denoted as N(x,t), where x = 1, 2, . 12 and t = 1954,

    55, ... It is obvious that the size tends to decrease over time in a population which is closed against immigration. If we take the ini- tial size of the cohort as a basis, it can be shown (Fig. 2) that the rlecline of this cohort size is very much similar to a negative exponen- tial distribution. It can be generalized as hav- ing the form of N(x) = N(1) e-er, where k is a constant term. For different cohorts, there will be different constant terms. It is possible to test empirically the variation of the terms for

    1/ An extensive application of Lexis diagram to demographic analysis can be found in Pressat (1961).

  • Grade (x)









    Figure I

    Progression of 1954 -55 Student Cohort (Population in thousands)






















    1954 55 56 57 58 59 60 61 62 63 64 65 66 Year (t)

    Source: Simon and Grant (1966: Table 28)

    Rate I.





    Figure 2 Survival Rate N(x) /N(I) and Retention

    Rate N (x + 1)/N(x) of 1954- 55 Student Cohort

    N(x)/N(I) %

    r I I I I I I I I Ist 2nd 3rd 4th 5th 6th 7th 8th 9th 10th I1th 12th

    Grade (x)


  • cohorts at different points in time; so some interesting results may be shown.

    Nevertheless, examining only the behavior of the constant terms can be misleading. Obviously the curve is not a smooth one. A more realistic approach is to analyze a student, cohort's "reten- tion rate" (Duncan, 1965:129).) Symbolically it is N(x +l, t +l) /N(x,t). Fig. 2 indicates that the rate does not show any tendency of monotonous decline. On the contrary, it increases from the first grade to the sixth grade. Only after the eighth grade does the rate begin to decline monotonously. This observation leads to the con- clusion that the decline of a student cohort is not strictly comparable to the survival curve in the general human population.

    This suggests that a student population of a given grade is not only affected by those sepa- rating forces such as mortality and dropout. Many other factors can also contribute to deter- mine the cohort size. Probably the most impor- tant factor is the effect of failure (or repeat- ers) at each level of an educational system. The effect is analogous to that of a rolling snow - ball; the repeaters tend to increase the rate of retention as shown in Fig. 2. Some other less important factors can be immigration and the re- enrollment of dropouts.

    Normally these two forces work in the oppo- site direction: mortality and dropout on the one hand, failure and new entry on the other hand. If the separating force is predominant, the size of a student cohort tends to decrease, and, hence, the retention rate will be less than unity. However, the effects of failure and new entry can be so strong that the decreasing ten- dency is cancelled out, which is obviously the case in Fig. 2. The combined effect of mortality and dropout is called the "separation factor" (Stockwell and Nam, 1963).

    An Educational Model from the Markov Process

    Several simplifications must be made before we can construct a model which will take account of these underlying dynamics. We shall limit ourselves to the analysis of a closed population, an assumption which is not uncommon in demo- graphic analysis. Furthermore, following a model of the Norwegian educational system (Thonstad, 1967), we assume that dropout is a one -time pro- cess; even though some dropouts re- enroll in schools in the later years, there are reasons to believe that the number may be too small to affect the total size of a school population. In other words, it is assumed that dropout is an absorbing state. Once an individual has left the educational system, he will not return.

    Here the concept of "retention rate" is used following the examples of Duncan (1965) and Stockwell and Nam (1963). However, the concept is used differently in the Office of Education's publications.


    Based on these assumptions, it follows that the progression of a student cohort has four alternatives, namely, failure, progress, death and dropout. The flow distribution of the cohort is a vector, and so the total educational system can be contrived as an input -output matrix, shown in Table 1.j.! The figures in the matrix are purely illustrative. The first three rows repre- sent the educational system up to the third grade; their row sums show the number of students in a given grade; their elements give the flow distribution of those students in that grade. The last three rows represent graduation (g), mortality (m) and dropout (d), respectively. Their row sums are zero, as they are assumed to be absorbing states.

    TABLE 1


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