Pre-Licensure Examination for Teachers: An Application of Rasch Analysis

138 The International Journal of Educational and Psychological Assessment September 2012, Vol. 11(2)

2012 Time Taylor Academic Journals ISSN 2094-0734

Pre-Licensure Examination for Teachers: An Application of Rasch Analysis

Amalia E. Roldan

Occidental Mindoro State College

Carlo Magno

De La Salle University, Manila

Abstract

The purpose of the present study is to construct and validate a pre-licensure

examination for the primary and secondary pre-service teachers that include the

professional education courses based on the areas covered in the Professional

Regulation Commission (PRC) in the Philippines using the Rasch analysis. The

professional education courses included were: (1) The teaching profession, (2)

social dimension of education, (3) principles of teaching, (4) educational

technology, (5) curriculum development, (6) facilitating human learning, (7) child

and adolescent development, and (8) assessment of student learning. The proposed

test will be useful in the review sessions conducted among graduating and graduate

students in preparation for their actual licensure examination. The test was

administered to 100 graduating students of Teacher Education department in a

province in the Philippines. Rasch measurement was utilized to substantiate the

validity of the test. The test obtained moderate person reliability (.57) and high

items reliability (.96). All the items fit the Rasch analysis except for one items in the

test. The person separation index (1.14, RMSE=.26) can moderately discriminate

the person ability while the item separation index (4.97, RMSE=.29) can highly

discriminate. The item map showed that person abilities are clustered in the middle

of the scale while the items were spread all over the scale. Two extreme items were

found on top and at the bottom of the scale.

Keywords: Rasch model, licensure examination, professional education courses

Introduction

As stated in the Philippines Commission on Higher Education (CHED) Memorandum Order (1999), the main mission of teacher education is the preparation of globally competitive teachers who are imbued with ideals, aspirations

and values and are adequately equipped with pedagogical knowledge and skills. Quality education needs to be provided in teacher training institution so that

preservice teachers will be prepared for their teaching profession. Teacher training

institutions are expected to provide strong foundation towards developing

preservice teachers knowledge and skills (Libman, 1990; Magno, 2010a). One way to assure that teachers are competent in the profession is gauged through the

licensure examination results (Angrist & Guryan, 2008). In preparation for the

licensure examination, the present study constructed and validated a pre-licensure



examination for the elementary and secondary preservice teachers that include the

professional education courses based on the prescribed curriculum of the PRC

using the Rasch analysis.

The Licensure Examination and the Teaching Performance

Teacher quality is a key element of student academic success, but little is

known about how specific teacher attribute like licensure examination profile

influences classroom outcomes. Reviews emphasizes the relevance of the licensure

examinations results on teachers academic success (Gitomer, Lathan, & Ziomek, 1999) and teaching performance (Goldhaber & Brewer, 2001; Buddin &

Zamarros, 2009). For example, Gitomer, Lathan, and Ziomek (1999) found that teacher academic ability varies widely by the type of licensure sought. Their ability

is reflected in the way they teach. Students whose teachers possess a B.A. or M. A.

in mathematics outperformed other students in mathematics. Students whose teachers have any kind of certification (standard, emergency, alternative, etc.)

outperformed students whose teachers have no certification or are certified in

different subjects. The study by Gitomer, Lathan, and Ziomek (1999) shows that

having certification in teaching such as a licensure exam brings about success in the

achievement of their students.

Furthermore, in the primary level (elementary), Buddin and Zamarros (2009) examined whether teacher licensure test scores and other teacher attributes

affect elementary student achievement. The results were based on longitudinal

student-level data from Los Angeles. California requires three types of teacher

licensure tests as part of the teacher certification process: A general knowledge test,

a subject area test (single subject for secondary teachers and multiple subject for

elementary teachers), and a reading pedagogy test for elementary school teachers.

The student achievement analysis is based on a value-added approach that adjusts

for both student and teacher fixed effects. The results show large differences in

teacher quality across school districts, but measured teacher characteristics explain

little of the difference. However, teacher licensure test scores are unrelated to

teacher success in the classroom. Similarly, student achievement is unaffected by

whether classroom teachers have advanced degrees. Teacher experience is

positively related with student achievement, but the linkage is weak and largely

reflects poor outcomes for teachers during their first year or two in the classroom.

The kind of preparation done for the teacher training institutions can be

relevant for their performance in the licensure examination. Houck and Kitche

(2010) highlighted in their study about the modal teacher preparation curriculum.

Content studies is one of the key aspects of the teacher preparation because

stakeholders should support the notion that teachers must know the content they will teach, and they must be aware of how best to teach it (Cruickshank, 1996, p. 11). Their study determined if any amount of quality coursework preparation of

preservice agriculture teachers influences their content knowledge. Findings

indicated that most preservice teachers are meeting an adequate content knowledge

level based on the Praxis II exam scores. They concluded in the study that



moderate relationship exists between Praxis II agriculture exam and agriculture

content preparation.

Unlike Houck and Kitches conclusion of moderate relationship that existed between Praxis II agriculture exam and agriculture content preparation.

Arenillo and Arenillo (2009) found in their study that there was a significant

relationship between the board performances of graduates with their knowledge

obtained in their preservice education. Based on this premise, it can be noted that

students with inadequate knowledge would have a less chance of passing the board

examination. This limits their opportunity for advancing in their teaching career.

The Licensure Examination for Teachers in the Philippines

In the Philippines, Republic Act No. 3687, known as Professionalization

Act for teachers is implemented to strengthen, regulate and supervise the practice

of teaching profession by prescribing a license to teachers certified by the

Professional Regulation Commission (PRC). The licensure examination is one of

the hurdles that a teacher education graduate take to obtain a license for their

career. The PRC has the responsibility to ensure that the examination meets

technical, professional, and legal standards, and protect the health, safety and

welfare of the public by assessing candidates abilities to practice competently. Once a candidate has passed the Licensing Examination for Teachers (LET), the PRC

grants the license, thus assuring the public that the licensee is minimally qualified to

practice teaching at the time of initial licensure.

The purpose of the licensure examination is to identify persons who possess

the minimum knowledge and experience necessary to perform task on the job

safely and competently. These licensing examinations are very different from

academic or employment examinations. Academic examinations assess how well a

person can define and comprehend the terms and concepts. Employment

examination can rank order candidates who possess the qualifications for the job.

The licensure examinations also assess how the academic programs harmonized

their process of developing, maintaining and administering a wholesome academic

instruction to ensure students readiness to pass the standard of licensure

examinations.

The board examination outputs of Higher Education Institutions (HEIs)

both private and public are used to evaluate the academic competence of

educational programs offered. The examination results serve as an index of

performance of the HEIs in general. Institutions which always produce top

performers in various programs tend to be identified as the most credible and

having the most reliable in offering quality instruction. On the other hand,

institutions which continuously have board performance outputs lower than the

national passing percentage are compelled to shape up by revisiting and improving

their academic inputs.

In addition, when the CHED implemented more stringent mechanisms in

ensuring quality in HEIs, licensure performance served as key parameters in

measuring the capability of an institution. For instance, in the evaluation of Agency

Performance Report Rating (APR), board performance outputs is one of the salient



indicators in the different key result areas under instruction. Moreover, the

implementation of HEIs leveling, normative financing and even in accreditation of

courses, the licensure performance outputs is considered one of the important

parameters in determining the efficiency and effectiveness of the institution.

The present study is undertaken to contribute on the priority concern of the

HEIs. The findings of the study can be used in the review sessions conducted

among the graduating and graduates of the colleges and universities for their

preservice teachers taking the LET. Currently, there are several published

examinations that are meant to prepare preservice teachers for the LET but none

of them reported the validity, reliability, and functioning of the items based on

sound measurement theories. The instrument that was constructed in the study can

be used to assess the possible performance of the preservice teachers in taking the

actual LET particularly in the professional education courses. Given the

instrument, the students can be familiarized with the types of items given in the

actual licensure exam since the content of the test is based on the specification

provided by the PRC. The latest teacher education curriculum was the source or

framework of the items included in the study.

The New Teacher Education Curriculum

In response to the problem of unqualified and ill-prepared beginning

teachers and the rate of passing in the licensure examination for teachers (LET)

which ranges from 20 to 30% per year (Pedro, 1996), the Commission on Higher

Education (CHED) initiated guidelines for the undergraduate teacher education

programs in a Memorandum Order (CMO) 30 Series 2004 otherwise known as the

New Teacher Education Curriculum. The aim of the pre-service teacher

curriculum is to prepare professional teachers for practice in primary and

secondary schools in the Philippines. The memorandum enumerated the 11

competencies expected of graduates of the Bachelor of Elementary Education or

BEEd and Bachelor of Secondary Education or BSE. Elementary school teachers

are trained to be generalists, that they may be able to teach all the five prescribed

learning areas (or subjects) of the basic education curriculum; while secondary

school teachers are trained to be specialists in one of the five prescribed learning

areas (Fajardo, 2007). The Competency standards are as follows:

Graduates of the BEEd (and BSEd) program are teachers who

1. Have the basic and higher level literacy, communication, numeracy, critical thinking, learning skills needed for higher learning;

2. Have a deep and principled understanding of the learning processes and the role of the teacher in facilitating these processes in their students;

3. Have a deep and principled understanding of how educational processes relate to larger historical, social, cultural, and political processes;

4. Have a meaningful and comprehensive knowledge of the subject matter they will teach;



5. Can apply a wide range of teaching process skills (including curriculum development, lesson planning, materials development, educational assessment,

and teaching approaches);

6. Have direct experience in the field/classroom (e. g., classroom observations, teaching assistance, practice teaching);

7. Can demonstrate and practice the professional and ethical requirements of the teaching professions;

8. Can facilitate learning of diverse types of learners, in diverse types of learning environments, using a wide range of teaching knowledge and skills;

9. Can reflect on the relationships among the teaching process skills, the learning processing in the students, the nature of the content/subject matter, and the

broader social forces encumbering the school and educational processes in

order to constantly improve their teaching knowledge, skills, and practices;

10. Can be creative and innovative in thinking of alternative teaching approaches, take informed risks in trying out these innovative approaches and evaluate the

effectiveness of such approaches in improving student learning; and

11. Are willing and capable to continue learning in order to better fulfill their mission as teachers.

The courses are classified into three components, namely, general

education courses, professional education courses and specialization/content

courses. General education courses provide basic knowledge in broad fields which

provide the foundation on which professional courses are anchored. This is to

compensate for knowledge, skills, and attitudes that are not developed and taught

during the short pre-collegiate or pre university level (only 10 years). Professional

education courses aim to develop the range of knowledge and skills needed in the

practice of the teaching profession. There are three categories of courses in this

component: (1) theory and concept courses, (2) methods and strategies courses,

and (3) field study courses. The last component course is the specialization and

content courses which provide basic but essential knowledge in the five prescribed

learning areas in elementary school education.

The summary of CHED curriculum for Preservice Teacher Education

(BSE) is as follows: Part I is the General Education with a total of 63 units; Part II

is the Professional Education with 57 units and consists of four areas; (1)

Theory/Concepts (Child and Adolescent Development (3units), Facilitating

Learning (3 units), Social Dimensions of Education (3 units) and, the Teaching

Professions (3 units); (2) Methods/Strategies (Principles of Teaching 1 and 2 (6

units), Assessment of Leaning 1 and 2 (6 units), Educational Technology 1 and 2 (6

units), Curriculum Development (3 units), Developmental Reading 1 and 2 (6

units); (3) Field Study (Field Study 1-6, Practice Teaching (6 units); and (4) Special

Topics (3 units); and Part III is the Specialization/Major (63 units) with the total of

183 units.



The Rasch Model

A variety of national tests are undertaken in the Philippines but none of

these test report findings based on the functioning of items. Magno and Gonzales

(2011) in their report to UNESCO on the development in Philippine education

described that when schools started to privatize testing, more so that information

about tests psychometric performance became scarce. One of the movements in the development of national tests in the Philippines is the analysis of test data using

Item Response Theory (Magno, 2010b). National examinations such as the

licensure examination and the review forms becomes more credible when their

actual item reliabilities and test functioning are made known to users. In the present

study, the one parameter Rasch model is used to determine the functioning of test

for preservice teachers.

The Rasch model is am analysis that is referred to as one-parameter logistic

model in the item response theory literature. The model estimates the probability

of a correct response to a given item function of item difficulty and person ability

(Hambleton, Swaminathan, & Rogers, 1991). The primary output of Rasch analysis

is a set of item difficulty and person ability values placed along a single interval

scale. Items with higher difficulty scores are less likely to be answered correctly, and

items with lower scores are more likely to provide correct responses, and those with

lower ability are less likely to do so (Magno & Ouano, 2009).

Rasch analysis (a) estimates the difficulty of dichotomous items as the

natural logarithm of the odds of answering each item correctly (a) log odds, or logit

score), (b) typically scales these estimates to mean = 0, then (c) estimates person

ability scores on the same scale. In the analysis of dichotomous items, item

difficulty and person ability are defined such that when they are equal, there is 50%

chance of correct response. As the person ability exceeds item difficulty, the chance

of a correct response increases as a logistic ogive function, and as item difficulty

exceeds person ability, the chance of success decreases. The formal relationship

among response probability, person ability, and item difficulty is given in the

mathematical equation by Bond and Fox (2001). A graphic plot of this relationship

known as item characteristic curve (ICC), is given for three items of different

difficulty levels. One useful feature of the Rasch model is referred to as parameter

separation or specific objectivity (Bond & Fox, 2001). The implication of this

mathematical property is that, at least in theory, item difficulty values do not

depend on the sample used to estimate them, nor do person ability scores depend

on the particular items used to estimate them. In practical terms, this means that

given well-calibrated sets of items that fit the Rasch model, robust and directly

comparable ability estimates may be obtained, from different subsets of items. This,

in turn, facilitates both adaptive testing and the equating of scores obtained from

different instruments (Magno, 2009).

The Present Study

The purpose of the present study is to construct and validate a pre-

licensure examination for the elementary and secondary preservice teachers that



include the professional education courses based on the prescribed curriculum of

the PRC using the Rasch analysis. It is the intention of the researchers to focus on

each particular area covered in the LET of PRC. The researchers decided to

construct a test that assesses the students possible performance in the professional education courses. Based on the PRC results, majority of the students got lowest

mean scores in the professional education subtest.

The following are the subject areas included in the test: (1) The teaching

profession, (2) social dimension of education, (3) principles of teaching, (4)

educational technology, (5) curriculum development, (6) facilitating human

learning, (7) child and adolescent development, and (8) assessment of student

learning. The proposed test can be a useful instrument in the review sessions

conducted among graduating and graduate students in preparation for their actual

Licensure Examination.

Method

Participants

The participants were 100 preservice teachers of one state college in the

Philippines. These students are graduating students for the school year 2010 2011 and are expected to take their licensure examination after their graduation.

Instrument

The instrument which is a pre-licensure examination for preservice teachers

is composed of 90 multiple items that was constructed by the researchers. The test

includes items that assess the preservice teachers knowledge and skills Table 1 shows the table of specification of the test.

Table 1

Table of Specification Content Areas Weights Cognitive Domain No. of

Items Knowledge Application Analysis

1. Teaching Profession 5% 1-2 3-5 5 2. Social Dimension of

Education

17% 6-7 8-20 15

3. Principles of Teaching 17% 21-35 15 4. Educational Technology 11% 36 -45 10 5. Curriculum Development 11% 46-50 52-55 51 10 6. Facilitating Learning 11% 56-59 60-65 10 7. Child and Adolescent

Development

11% 66-70 71-75 10

8. Assessment of Student Learning

17% 76-90 25

Weights 100% 20% 59% 21%

Total 18 53 19 90

The table of specifications includes 18 items (20%) that measures

knowledge, 53 items (59%) application and 19 items (21%) analysis. These three



cognitive domains were measured following the objectives and subject areas

included in the specifications of the PRC on LET. The items were content

validated by a panel of faculty teaching education courses who served as reviewers.

The reviewers came from different reputable colleges and universities in the

Philippines with good performance in the LET.

Procedure

The first draft of the test was reviewed by some experts to establish the

content validity. The comments and suggestions were integrated in writing the final

draft of the test. The final draft of the test was administered to the 100 graduating

students of a teacher education department in a state college during their in-house

review for the LET. The researchers administered the test among the group and

clearly instructed the students on how to answer the test. After the administration of

test, the teachers and students were debriefed about the purpose of the study.

Data Analysis

To describe the distribution of the scores, the mean and standard deviation

were reported. The internal consistency of the items was determined using the

Cronbachs alpha. Item and person reliability estimates were calculated separately using the Rasch model using the Winsteps software.

Rasch model was utilized to determine the item difficulty of the test based

on the results of logistic measure indices. The items with positive logits indicate that

items are difficult and have the probability to be answered by persons with high

ability. On the other hand, the items with negative logits are easy items and have

the probability to be answered by persons with low ability. Item in fit and outfit

were determined to assess how many of the items fit the analysis as good items and

does not fit as items to be revised or deleted. To determine the items that fit in the

Rasch analysis, the outfit and infit mean square (MNSQ) indices should be within

the acceptable range of 0.70 1.30 (Bond & Fox, 2007). Misfitting items are signs of multidimensionality and model deviance. High values of item MNSQ indicate a

lack of construct homogeneity with other items in a scale, whereas low values indicate redundancy with other items (Linacre & Wright 1994). An item map was generated to determine how the items are spread in the entire range of the test. It is

also used to show how the test can discriminate persons ability as to high and low abilities.

Results

Descriptive statistics were reported in Table 1. The mean scores of the

eight areas of the review test range from .40 to .84 (closer to a value of 1.0 indicates

large proportion of correct answers). Majority of the students obtained correct

answers on the items in the area of teaching profession and lowest on educational

technology. The rest of the test components got means below the median range

except for the principle of teaching and child adolescent development. The whole

test obtained mean score of .51 and a standard deviation of .07. The mean score is



above the median range (0.65) which indicates that students got more correct

responses than the incorrect scores.

The internal consistency of the test and its components were established

using both the Cronbachs alpha and Rasch analysis. The Cronbachs alpha coefficients of the test and its components range from .00 to .59 which indicates low

to moderate reliability. Results of the Rasch analysis show internal consistency of

the items and low for person reliabilities.

Table 2

Descriptive Statistics of the Eight Areas of the Pre-licensure Examination M SD Cronbachs

alpha

Person

Reliability

Item

Reliability

1. The Teaching Profession .84 .167 .00 .00 .84 2. Social Dimension of

Education

.45 .135 .28 .24 .95

3. Principle of Teaching .60 .156 .43 .41 .94 4. Educational Technology .40 .147 .21 .18 .96 5. Curriculum Development .45 .149 .09 .00 .96 6. Facilitating Learning .44 .38 .00 .00 .94 7. Child and Adolescent

Development

.60 .167 .39 .30 .95

8. Assessment of Student Learning

.48 .146 .22 .22 .92

The Whole Test .65 .070 .59 .57 .96

Using the Rasch Model, the item and person separation and reliability were

examined. The real person separation is 1.14 with RMSE of .26. This indicates that

the test can moderately discriminates among persons. The person separation

indicates the number of groups of students that can be separated according to their

abilities (high and low ability groups).

For the items, the reliability is high (.96). The item separation of 4.97

indicates that the items can discriminate between the high ability and low ability.

There is a wide spread of difficulty in the items as the standard deviation of item

difficulty estimates 1.48 logits and the separation of 4.97. The item difficulties of

the test were determined based on the measure or logistic measures for item

difficulty.

The item difficulties are reported based on the logits obtained per item (see

Table 5). The negative logits indicate that the items are easy and have the

probability to be answered by persons with low ability. On the other hand, positive

logits indicate that the items are difficult and have the probability to be answered by

persons with high ability.



Table 5

Difficulty Index, Infit and Outfit of the Test

Test Component Logistic

SE

INFIT OUTFIT

Item no. Measure MNSQ z MNSQ Z TP 1 1.39 0.31 0.94 -0.64 0.94 -0.64

2 0.88 0.33 0.95 -0.37 0.95 -0.35 3 0.88 0.33 1.13 1.09 1.14 1.06 4 -2.45 1.02 0.98 0.29 0.55 -0.11 5 -0.71 0.48 1 0.11 0.99 0.13

SDT 6 -2.23 0.33 0.96 -0.15 0.84 -0.45

7 -1.93 0.3 0.97 -0.13 0.89 -0.36

8 0.35 0.26 0.93 -0.72 0.86 -1.01

9 -0.04 0.25 1.01 0.1 1.05 0.51

10 2.32 0.44 0.92 -0.14 0.58 -0.86

11 1.28 0.31 1.09 0.53 1.65 2.14

12 -0.65 0.25 0.92 -1 0.88 -1.2

13 4.23 1.01 1.03 0.35 1.86 0.97

14 1.09 0.3 1 0.08 0.95 -0.15

15 -1.44 0.27 1.03 0.31 1 0.06

16 -2.46 0.35 1.01 0.13 1.05 0.26

17 -0.22 0.25 1.15 1.84 1.21 2.04

18 -0.4 0.25 1.04 0.49 1.05 0.55

19 -0.59 0.25 0.92 -1.05 0.89 -1.04

20 0.69 0.27 0.96 -0.27 0.97 -0.09

PT 21 -1.43 0.33 0.91 -0.37 0.83 -0.43

22 3.04 0.41 1.14 0.53 1.11 0.37

23 -0.07 0.25 1.03 0.33 1 0.05

24 0.68 0.25 1.03 0.37 0.99 -0.02

25 -1.22 0.31 0.97 -0.09 0.91 -0.22

26 0.74 0.25 0.83 -2.07 0.79 -1.84

27 -0.34 0.26 1.11 1.04 1.24 1.43

28 -0.34 0.26 0.96 -0.35 0.93 -0.41

29 1.63 0.28 1 0.08 1.03 0.2

30 -0.34 0.26 0.91 -0.83 0.81 -1.22

31 0.12 0.25 1.1 1.12 1.12 0.97

32 -0.94 0.29 0.76 -1.61 0.59 -1.88

33 1.48 0.27 1.23 1.77 1.36 1.78

34 -1.22 0.31 1.11 0.64 1.17 0.64

35 -1.79 0.37 0.92 -0.25 0.77 -0.47

ET 36 2.42 0.49 0.82 -0.36 0.62 -0.47

37 -1.56 0.27 1.08 0.74 1 0.08

38 -0.64 0.25 0.96 -0.48 0.92 -0.51



Cont. Table 5

39 0.79 0.3 1.06 0.43 1.14 0.6

40 1.54 0.37 1.07 0.37 1.52 1.26

41 2.42 0.49 0.94 -0.03 0.65 -0.41

42 -1.35 0.26 0.91 -0.89 0.88 -0.6

43 -2.02 0.29 0.96 -0.26 0.96 -0.05

44 -0.05 0.26 1.13 1.24 1.42 2.41

45 -1.56 0.27 0.91 -0.79 0.86 -0.61

CD 46 -0.61 0.25 1.05 0.55 1.03 0.31

47 -0.93 0.26 0.81 -2.01 0.73 -2.09

48 -1.43 0.28 1 0.02 1.34 1.66

49 -1.36 0.28 0.97 -0.19 0.93 -0.34

50 -2.68 0.39 1.17 0.67 1.4 0.99

51 2.58 0.47 1.12 0.44 1.73 1.22

52 1.91 0.37 0.97 -0.04 1.13 0.45

53 0.83 0.28 1.01 0.11 1.02 0.19

54 0.02 0.25 0.94 -0.72 0.88 -1.03

55 1.66 0.34 0.99 0.02 0.85 -0.34

FL 56 -1.66 0.3 0.95 -0.22 0.88 -0.47

57 -2.18 0.35 0.96 -0.09 0.86 -0.36

58 -0.17 0.24 0.98 -0.24 0.99 -0.08

59 -0.11 0.24 0.95 -0.62 0.97 -0.4

60 0.3 0.25 1.09 1.11 1.15 1.43

61 -0.06 0.24 1.05 0.69 1.04 0.51

62 0.43 0.25 0.9 -1.22 0.84 -1.43

63 1.56 0.31 0.99 -0.01 0.9 -0.3

64 2.01 0.36 1.15 0.65 1.87 2.12

65 -0.11 0.24 0.96 -0.57 0.94 -0.73

CAD 66 1.91 0.29 1.08 0.58 1.61 1.9

67 -0.18 0.27 0.9 -0.83 0.86 -0.8

68 -2.13 0.43 0.84 -0.45 0.7 -0.45

69 1.91 0.29 1.04 0.35 1.27 0.97

70 -0.56 0.28 0.87 -0.98 0.85 -0.67

71 -1.8 0.38 1.16 0.66 0.93 -0.01

72 -0.72 0.29 0.92 -0.53 0.87 -0.49

73 -0.18 0.27 1.11 0.95 1.09 0.54

74 0.23 0.26 1.05 0.59 1.13 0.89

75 1.51 0.27 0.94 -0.45 0.85 -0.63

ASL 76 -1.2 0.27 0.97 -0.14 1.02 0.18

77 -0.85 0.26 1 0.08 1.05 0.37

78 1.22 0.28 1.07 0.55 1.09 0.49

79 1.06 0.27 1.04 0.37 0.99 0.01



Cont. Table 5

80 -0.01 0.24 0.99 -0.05 0.99 -0.08

81 0.34 0.25 0.88 -1.5 0.84 -1.6

82 0.59 0.25 1.05 0.52 1.03 0.25

83 -0.18 0.24 1.06 0.8 1.08 0.91

84 -1.86 0.31 0.76 -1.13 0.66 -1.42

85 -0.07 0.24 0.96 -0.52 0.96 -0.39

86 1.14 0.28 1.04 0.36 1.19 1.01

87 0.17 0.24 0.97 -0.37 1 -0.01

88 1.56 0.31 1.11 0.62 1.05 0.26

89 -0.92 0.26 1.06 0.53 1.04 0.32

90 -0.99 0.26 1.04 0.34 1.04 0.3

Note. TF= Teaching profession; SDT= Social dimension of Teaching; PT- Principles of teaching;

ET= Educational Technology; FL= Facilitating Learning; CD= Curriculum Development;

CDA=Child Adolescent Development;

To determine if the items under each domain has a unidimensional

structure, the item fit mean square (MNSQ) was obtained. The results of the fit

statistics show that seven items out of 90 items do not fit the Rasch Model following

the criteria suggested by Bond and Fox (2007). Items which do not fit the model

have outfit and infit mean square (MNSQ) indices outside the acceptable range of

0.70 1.30. Misfitting items are signs of multidimensionality and model deviance. High values of item MNSQ indicate a lack of construct homogeneity with other items in a scale, whereas low values indicate redundancy with other items (Linacre & Wright 1994). Generally, few items did not fit the Rasch model. Only

one items do not fit the model in the social dimension in teaching with outfit

MNSQ value of 1.65, item 33 of principle of teaching (Outfit MNSQ = 1.36), items

40 and 44 of educational technology with outfit MNSQ of 1.40 and 1.73

respectively. There were two items in curriculum development, the items number

50 and 51 with outfit MNSQ of 1.40 and 1.73 and item number 64 of facilitating

learning with outfit MNSQ of 1.87. All misfitting items have high values of outfit

MNSQ that indicate of the lack of homogeneity.

The itemperson map indicates the spread of the items over the entire range of the test. The numbers on the right indicate items and numbers on left

indicate persons. Items and persons placed on top of the scale are more difficult

and more competent, respectively. As one goes down the scale, items become

easier and individuals become less competent.

As shown in the map, majority of the individuals are clustered towards the

center of the scale while items are spread all over the scale with two extreme scores,

item 13 on the top (very easy item) and item 4 below (difficult item). The person

position on the scale indicates homogeneity of their ability. Thus, the test can

moderately discriminates the person ability as to high or low abilities. On the other

hand, items are normally distributed. The item and person means are matched in

the item map.



Figure 1. Item-Person Map



Discussion

It was found in the study that the pre-licensure examination constructed is a

test that supports assumptions of the Rasch model. The item reliability of the test is

high and can moderately discriminate person ability as to high or low abilities. All

the items satisfied the criteria of fit to the Rasch model except for the 7 items. Most

of the items have a good fit showing that students with high ability are able to get

correct answers to difficulty items, and those with lower ability are most likely not to

answer the difficult items. One example of misfitting items is Item number 11 of

social dimension in teaching. The item is stated as Teacher Anna teaches to his pupils that pleasure is not the highest good. Teachers teaching is against what philosophy? The options given are a) Existentialism, b) Realism, c) Epicureanism and d) Empiricism. This item does not fit the model due to the large value of outfit

MNSQ which is 1.65. The value is outside the acceptable range of 0.7 to 1.30 in

the criteria of fitting items suggested by Bond and Fox (2007). This item and the

other six misfiting items lack the homogeneity. The items have low level of internal

consistency with the rest of the items in the test. It was revealed that most of the

items fit the Rasch analysis which indicates unidimensionality of the test or that the

test really measures what it intends to measure (the same construct). This was

supported by the item map showing the normal distribution of the items as they

were spread all over the scale with only two extreme items found on top and at the

bottom of the scale.

The findings of the research pointed out that the Rasch Analysis is a

powerful tool in determining the validity of a teacher-made test. The relevance of

the test instrument constructed by this researcher is for the purpose of satisfactorily

complying with the CHED Memorandum on licensure examinations for teachers

and providing the OMSC with a valid pre-licensure test which could be

standardized at the national level.

It is highly recommended to administer the same instrument to

education students from different schools to create a bigger sample of examinees.

Future researches on the said instrument are likewise recommended particularly in

establishing the predictive validity of the same. The construction and validation

using Rasch analysis on the other two components of the LET, the general and

specialization courses are also recommended.

Tests with the purpose of practicing students for the licensure exam should

have appropriate indicators that they are valid and reliable measures. Educational

institutions and review centers when coming up with test items for the LET review

should consider showing evidence that the items are performing appropriately.



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About the Authors

Ms. Amalia Roldan is currently a faculty of Occidental Mindoro State College. She is currently finishing her PhD in Educational Psychology major in Quantitative

Research in De La Salle University, Manila.

Dr. Carlo Magno is a faculty of the College of Education in De La Salle University,

Manila. Most of his publication is in line with measurement and assessment studies.

He has developed several measures useful in the educational context. Further

correspondence can be addressed to him at [email protected]

Documents

Pre-Licensure Examination for Teachers: An Application of Rasch Analysis