Predictors Of Success In Chemist Licensure Examination Chemistry Essay


A discriminant analysis was performed to identify variables that can successfully predict passing of Bachelor of Science in Chemistry graduates of Western Mindanao State University, Zamboanga City, Philippines in the Chemist Licensure Examination. Thirteen predictors were used which include final grades of students in three Inorganic Chemistry subjects, two Organic Chemistry subjects, one Biochemistry subject, four Analytical Chemistry subjects and two Physical Chemistry subjects, plus the College Entrance Test results of the students. The discriminant function revealed a significant association between groups (those who passed and those who failed) and ten of the predictors, accounting for 49.56 % of between group variability. Structure matrix revealed that six subjects were identified as good predictors: Chem 161, Chem 160, Chem 123, Chem 104, Chem 154, and Chem 101 (arranged in decreasing importance).


The Chemist Licensure Examination (CLE) in the Philippines is taken by qualified applicants who aspire to register as chemist. The Republic Act No. 754, also known as the Chemistry Law - An Act to Regulate the Practice of Chemistry in the Philippines, was enacted by Congress on June 18, 1952 which specifies among others, registration and conduct of the examination (PRC, 2009). The examination is being held in September at Manila and covers the following subjects: Inorganic Chemistry, Organic Chemistry, Analytical Chemistry and Physical Chemistry. The qualified applicant is specified to have finished a degree of Bachelor of Science (B.S.) in Chemistry or its equivalent degree.

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The Western Mindanao State University (WMSU) in Zamboanga City, Philippines, commenced offering the B.S. Chemistry course program at the College of Science and Mathematics (CSM) in 1976. For years, it has produced B.S. Chemistry graduates now in the field of education, industry, and research. Unfortunately, data on these registered and non-registered chemists who took their Chemistry undergraduate course from WMSU since then is not readily available.

The CLE results of WMSU for the last nine years (2000-2008) resulted to 40.82 % passing (CSM Annual Report, 2008). Although in 2008, WMSU obtained 50 % passing rate which is higher than the 2008 CLE national passing rate of 47 %. Still, there is a demand to maintain or improve these results.

Several measures have been undertaken by the Chemistry Department and CSM officials to address issues concerning the improvement of CLE passing rate. These include faculty development (i.e. faculty members are encouraged to pursue graduate studies and/or attend short-term courses); revision of the curriculum for the Bachelor of Science in Chemistry course program; improvement of laboratory rooms; and acquisition of much-needed laboratory equipment.

The above-mentioned measures could well address improvement of WMSU CLE passing rate. However, another additional tool that can be explored is application of discriminant analysis to predict success of chemistry students in passing the CLE. Based on existing record of students, a discriminant function can be generated to determine good predictors of success. These existing records could be grades of students in their chemistry or allied subjects, college entrance test (CET) results, and others.

Knowing the student's chance of success in CLE which may be based on certain subjects can be used as a benchmark by teachers in coordination with the school administrators to devise later on some interventions in class such as tutorial or use of other appropriate teaching strategies. It may also prompt students to exert more effort in understanding these specific subjects. Thus, it is worthwhile to determine good predictor variables for success in CLE & generate a discriminant function (equation).

Discriminant function analysis is a form of multivariate technique, capturing interactions of dependent variables and intercorrelations of independent and dependent variables (Buras, 1996). Discriminant analysis is used for a set of interval independent variables together with a set of dependent categorical variables.

Two types of discriminant function analysis exist: descriptive discriminant function analysis (DDA) and predictive discriminant function analysis (PDA). One can either use the two depending on the purpose of the researcher, although mixing of DDA and PDA is feasible. Generally, if one aims to predict or explain scores on the continuous variables using group membership, then DDA is applied. But if one aims to predict group membership using the scores on the continuous variables, then PDA is more appropriate. Stevens (1996) contrasts PDA from DDA in the following manner: "in the predictive discriminant analysis, the focus is on classifying subjects into one of several groups (or to predicate group membership), whereas in descriptive discriminant analysis, the focus is on revealing major differences among the groups".

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In this study, DDA was performed to identify variables (grades in Chemistry subjects) that could successfully predict passing of WMSU B.S. Chemistry students in CLE.



The sample included 37 (representing 44 % of the population) WMSU B.S. Chemistry graduates who took the CLE from 2003 to 2009. The dependent variable were the two (2) groups composed of 21 individuals who passed the CLE and 16 individuals who failed the CLE (only the first taken CLE result was considered in this study). The independent variables were the Chemistry subject grades in Inorganic Chemistry, Organic Chemistry, Biochemistry, and Physical Chemistry; and the CET percentile rank.

Data Collection

The data for this study were based from the student's academic records at the Chemistry Department and grading sheets of CSM-WMSU, from 1999 to 2009 academic sessions. The CET results were requested from the Testing and Evaluation Center (TEC)-WMSU.

Data Analysis

Preliminary statistical analysis was performed to determine significant differences between means of those who passed the CLE and those who failed for all 13 independent variables. After eliminating some variables, discriminant analysis was used to identify good predictors of success in CLE.


Discriminant analysis was used to determine good predictors of success of WMSU B.S. Chemistry graduates in CLE. A total of 13 independent predictor variables were initially used. These independent variables are the grades of students in their chemistry courses plus their CET result. Table 1 lists these courses.

Table 1. Predictor variables used


Chemistry Course/


Subject Area



Inorganic Chemistry


Chem 103


Chem 104


Chem 120

Organic Chemistry


Chem 121


Chem 123



Chem 151

Analytical Chemistry


Chem 152


Chem 153


Chem 154


Chem 160

Physical Chemistry


Chem 161



Preliminary analysis revealed that Chem 152, Chem 153, and CET were not able to significantly differentiate the two groups (those who passed the CLE and those who failed). Consequently, they were omitted in the succeeding analysis.

Table 2 shows the structure matrix results and the summary of the discriminant analysis performed to determine good predictors of success of chemistry graduates in CLE. The canonical discriminant function produced the following values: canonical correlation, Wilk's lambda, standardized function coefficients and classification results using cross-validation of the left one out method.

Table 2. Structure Matrix and Summary of Canonical Discriminant Function.

Structure Matrixa

Discriminant Function

Function 1

Passed the CLE

Standardized Coefficients

Chem 101

Chem 103

Chem 104

Chem 120

Chem 121

Chem 123

Chem 151

Chem 152

Chem 153

Chem 154

Chem 160

Chem 161



Box's M Sig.

Group Centroids (passed)


Correctly Classified Cases

Correct Predicted

Incorrect Predicted

Sample Size

Cross Validated

Correct Predicted

Incorrect Predicted

Canonical Correlation

Wilk's Lambda

Statistical Sig.
































18 (12)

4 (3)


62.2 %

14 (9)

7 (7)




a Pooled with-in groups correlations between discriminating variables and standardized canonical discriminant functions

Variables are absolute size of correlation with function

b Variable omitted from analysis

The structure matrix indicated the relative importance of the predictors. Results show that all analyzed variables were important although six subjects were identified as good predictors of success in CLE. The best predictor was Chem 161, followed by Chem 160, then Chem 123, Chem 104, Chem 154, and Chem 101.

Box's M indicated that the assumption of equality of covariance matrices was not violated. Moreover, results of the discriminant analysis revealed that the function generated was statistically significant (p<.05) having Wilk's lambda of .504. The canonical correlation between the discriminant function and passing the CLE was .704.

The standardized coefficients of the independent variables are shown at the third column of Table 2. The discriminant function (equation) therefore would give us this equation:

D = (Chem 101 x .756) + (Chem 103 x .464) + (Chem 104 x .120) + (Chem 120 x .036) + (Chem 121 x (-1.738)) + (Chem 123 x .787) + (Chem 151 x (-1.764)) + (Chem 154 x .974) + (Chem 160 x .127) + (Chem 161 x 3.298) + (-6.487)

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The percentage of cases correctly classified or "hit rates" was 81.1 % for passing the CLE. The cross- validated classification showed that overall 66.7 % were correctly classified.


Six subjects were identified as good predictors for success in the CLE and these were (in decreasing order of importance): Chem 161, Chem 160, Chem 123, Chem 104, Chem 154, and Chem 101. A discriminant function (equation) was also generated. Teachers, school administrators and students alike may then exert more effort to devise strategies to improve student performance on these identified subjects without of course jeopardizing their performance with the other subjects.


The sample size may be increased to generate a more reliable discriminant function. The generated equation may also be further validated by finding its "hit rates" through the use of data not covered in this research. The generated equation may also be used to identify students at risk (i.e. high chance to fail in the CLE) and devise appropriate interventions.