Predictors Of Success In Chemist Licensure Examination Chemistry Essay

A discriminant analysis was performed to place variables that can successfully foretell passing of Bachelor of Science in Chemistry alumnuss of Western Mindanao State University, Zamboanga City, Philippines in the Chemist Licensure Examination. Thirteen forecasters were used which include concluding classs of pupils in three Inorganic Chemistry topics, two Organic Chemistry topics, one Biochemistry topic, four Analytic Chemistry topics and two Physical Chemistry topics, plus the College Entrance Test consequences of the pupils. The discriminant map revealed a important association between groups ( those who passed and those who failed ) and ten of the forecasters, accounting for 49.56 % of between group variableness. Structure matrix revealed that six topics were identified as good forecasters: Chem 161, Chem 160, Chem 123, Chem 104, Chem 154, and Chem 101 ( arranged in diminishing importance ) .


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The Chemist Licensure Examination ( CLE ) in the Philippines is taken by qualified appliers who aspire to register as chemist. The Republic Act No. 754, besides 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, enrollment and behavior of the scrutiny ( PRC, 2009 ) . The scrutiny is being held in September at Manila and covers the undermentioned topics: Inorganic Chemistry, Organic Chemistry, Analytical Chemistry and Physical Chemistry. The qualified applier is specified to hold finished a grade of Bachelor of Science ( B.S. ) in Chemistry or its tantamount grade.

The Western Mindanao State University ( WMSU ) in Zamboanga City, Philippines, commenced offering the B.S. Chemistry class plan at the College of Science and Mathematics ( CSM ) in 1976. For old ages, it has produced B.S. Chemistry graduates now in the field of instruction, industry, and research. Unfortunately, informations on these registered and non-registered chemists who took their Chemistry undergraduate class from WMSU since so is non readily available.

The CLE consequences of WMSU for the last nine old ages ( 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 keep or better these consequences.

Several steps have been undertaken by the Chemistry Department and CSM functionaries to turn to issues refering the betterment of CLE go throughing rate. These include module development ( i.e. module members are encouraged to prosecute alumnus surveies and/or attend short-run classs ) ; alteration of the course of study for the Bachelor of Science in Chemistry class plan ; betterment of research lab suites ; and acquisition of much-needed research lab equipment.

The above-named steps could good turn to betterment of WMSU CLE go throughing rate. However, another extra tool that can be explored is application of discriminant analysis to foretell success of chemical science pupils in go throughing the CLE. Based on bing record of pupils, a discriminant map can be generated to find good forecasters of success. These bing records could be classs of pupils in their chemical science or allied topics, college entryway trial ( CET ) consequences, and others.

Knowing the pupil ‘s opportunity of success in CLE which may be based on certain topics can be used as a benchmark by instructors in coordination with the school decision makers to invent subsequently on some intercessions in category such as tutorial or usage of other appropriate instruction schemes. It may besides motivate pupils to exercise more attempt in understanding these specific topics. Therefore, it is worthwhile to find good forecaster variables for success in CLE & A ; bring forth a discriminant map ( equation ) .

Discriminant map analysis is a signifier 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 map analysis exist: descriptive discriminant map analysis ( DDA ) and prognostic discriminant map analysis ( PDA ) . One can either utilize the two depending on the intent of the research worker, although commixture of DDA and PDA is executable. By and large, if one aims to foretell or explicate tonss on the uninterrupted variables utilizing group rank, so DDA is applied. But if one aims to foretell group rank utilizing the tonss on the uninterrupted variables, so PDA is more appropriate. Stevens ( 1996 ) contrasts PDA from DDA in the undermentioned mode: “ in the prognostic discriminant analysis, the focal point is on sorting topics into one of several groups ( or to predicate group rank ) , whereas in descriptive discriminant analysis, the focal point is on uncovering major differences among the groups ” .

In this survey, DDA was performed to place variables ( classs in Chemistry topics ) that could successfully foretell passing of WMSU B.S. Chemistry pupils in CLE.



The sample included 37 ( stand foring 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 persons who passed the CLE and 16 persons who failed the CLE ( merely the first taken CLE consequence was considered in this survey ) . The independent variables were the Chemistry capable classs in Inorganic Chemistry, Organic Chemistry, Biochemistry, and Physical Chemistry ; and the CET percentile rank.

Data Collection

The information for this survey were based from the pupil ‘s academic records at the Chemistry Department and rating sheets of CSM-WMSU, from 1999 to 2009 academic Sessionss. The CET consequences were requested from the Testing and Evaluation Center ( TEC ) -WMSU.

Datas Analysis

Preliminary statistical analysis was performed to find important differences between agencies of those who passed the CLE and those who failed for all 13 independent variables. After extinguishing some variables, discriminant analysis was used to place good forecasters of success in CLE.


Discriminant analysis was used to find good forecasters of success of WMSU B.S. Chemistry graduates in CLE. A sum of 13 independent forecaster variables were ab initio used. These independent variables are the classs of pupils in their chemical science classs plus their CET consequence. Table 1 lists these classs.

Table 1. Forecaster variables used


Chemistry Course/


Capable Area



Inorganic Chemistry


Chem 103


Chem 104


Chem 120

Organic Chemistry


Chem 121


Chem 123



Chem 151

Analytic Chemistry


Chem 152


Chem 153


Chem 154


Chem 160

Physical Chemistry


Chem 161



Preliminary analysis revealed that Chem 152, Chem 153, and CET were non able to significantly distinguish the two groups ( those who passed the CLE and those who failed ) . Consequently, they were omitted in the succeeding analysis.

Table 2 shows the construction matrix consequences and the sum-up of the discriminant analysis performed to find good forecasters of success of chemical science alumnuss in CLE. The canonical discriminant map produced the undermentioned values: canonical correlativity, Wilk ‘s lambda, standardized map coefficients and categorization consequences utilizing 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


( Constant )

Box ‘s M Sig.

Group Centroids ( passed )

( failed )

Correctly Classified Cases

Correct Predicted

Incorrect Predicted

Sample Size

Cross Validated

Correct Predicted

Incorrect Predicted

Canonic Correlation

Wilk ‘s Lambda

Statistical Sig.































81.1 %

18 ( 12 )

4 ( 3 )


62.2 %

14 ( 9 )

7 ( 7 )




a Pooled with-in groups correlativities between know aparting variables and standardised canonical discriminant maps

Variables are absolute size of correlativity with map

B Variable omitted from analysis

The construction matrix indicated the comparative importance of the forecasters. Results show that all analyzed variables were of import although six topics were identified as good forecasters of success in CLE. The best forecaster was Chem 161, followed by Chem 160, so Chem 123, Chem 104, Chem 154, and Chem 101.

Box ‘s M indicated that the premise of equality of covariance matrices was non violated. Furthermore, consequences of the discriminant analysis revealed that the map generated was statistically important ( p & lt ; .05 ) holding Wilk ‘s lambda of.504. The canonical correlativity between the discriminant map and go throughing the CLE was.704.

The standardised coefficients of the independent variables are shown at the 3rd column of Table 2. The discriminant map ( equation ) hence 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 )

The per centum of instances right classified or “ hit rates ” was 81.1 % for go throughing the CLE. The cross- validated categorization showed that overall 66.7 % were right classified.


Six topics were identified as good forecasters for success in the CLE and these were ( in diminishing order of importance ) : Chem 161, Chem 160, Chem 123, Chem 104, Chem 154, and Chem 101. A discriminant map ( equation ) was besides generated. Teachers, school decision makers and pupils likewise may so exercise more attempt to invent schemes to better pupil public presentation on these identified topics without of class endangering their public presentation with the other topics.


The sample size may be increased to bring forth a more dependable discriminant map. The generated equation may besides be farther validated by happening its “ hit rates ” through the usage of informations non covered in this research. The generated equation may besides be used to place pupils at hazard ( i.e. high opportunity to neglect in the CLE ) and invent appropriate intercessions.


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