Normal view MARC view ISBD view

Application of factor analysis and multiple regression analysis in evaluating secondary students' performance in mathematics at Alfonso National High School / Athena Aphrodite G. Salazar and Blesse M. Tibayan.

By:

Salazar, Athena Aphrodite G [author]

Contributor(s):

Material type: Text

TextLanguage: English Publication details: Indang, Cavite : 2004. Cavite State University- Main Campus,Description: x, 53 pages : illustrations ; 28 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

DDC classification:

519.5 Sa5 2004

Online resources:

Click here to view thesis abstract and table of contents

Production credits:

College of Arts and Science (CAS)

Abstract: SALAZAR, ATHENA APHROD [TE GONZALES and TIBAYAN, BLESSE MARJES. "Application Of Factor Analysis and Multiple Regression Analysis in Evaluating Secondary Students' Performance In Mathematics At Alfonso National High School". BS Applied Mathematics. Cavite State University, Indang, Cavite. April 2004. Adviser: Mrs. Lani Rodis. The study entitled "APPLICATION OF FACTOR ANALYSIS AND MULTIPLE REGRESSION ANALYSIS IN EVALUATING SECONDARY STUDENTS' PERFORMANCE m MATHEMATICS AT ALFONSO NATIONAL HIGH SCHOOL" was conducted at Alfonso National High School from January to February 2004. This study aimed to 1) identify the underlying factors with the use of factor analysis; 2) determine the relationship of the underlying factors with the academic performance of the students using multiple regression analysis; and 3) identify the best predictor of mathematics performance of students. A total of 292 students from Alfonso National High School was considered as subject of this study. Their corresponding grades in English, Science and Filipino and Mathematics from first grading to third grading were gathered. Questionnaires were also distributed to each of the respondents to determine their scores on the other variables. There were 13 variables considered in this study including the students' study habits. For factor analysis, five factors were extracted using Principal Component Method. The factor matrix was further rotated using Varimax Rotation of Factors. From the factor matrix, the variables that loaded heavily were identified and the five extracted factors were defined. From the five extracted factors, the highest loading in each factor were considered as the independent variable to be used for the first multiple regression analysis wherein the dependent variable is the academic performance of the students in mathematics. The five independent variables were academic performance in English, monthly family income, position in the family, time allotted for studying at night and the number of mathematics books. For the first multiple regression analysis, using the factor scores of the five independent variables, two variables were found as predictors of the mathematics performance of students. The two variables are academic performance in English and the number of mathematics’ books. For the second multiple regression analysis, the variables used as independent variables are academic performance in the three major subjects (English, Filipino and Science and Technology), class size, household size, monthly family income, number of mathematics books, position in the family, time allotted for studying at night, classroom climate, peers influence and the study habits of students. Four variables were retained as predictors of the academic performance of students in mathematics. The four variables were academic performance in the three major subjects (English, Filipino and Science and Technology) and the number of mathematics books. The Regression model I explains 53% of the total variation in the students' mathematics performance while the Regression model Il explains 69.2% of the total variation in the academic performance of students in mathematics.

Average rating: 0.0 (0 votes)

Holdings ( 1 )
Title notes ( 5 )

Holdings
Item type	Current library	Collection	Call number	Materials specified	URL	Status	Notes	Date due	Barcode
Theses / Manuscripts	Ladislao N. Diwa Memorial Library Theses Section	Non-fiction	519.5 Sa5 2004 (Browse shelf(Opens below))		Link to resource	Room use only	SP-2739		00004257

Special Problem (BS Applied Mathematics - - Statistics) Cavite State University.

Includes bibliographical references.

College of Arts and Science (CAS)

SALAZAR, ATHENA APHROD [TE GONZALES and TIBAYAN, BLESSE
MARJES. "Application Of Factor Analysis and Multiple Regression Analysis in Evaluating Secondary Students' Performance In Mathematics At Alfonso National High School". BS Applied Mathematics. Cavite State University, Indang, Cavite. April 2004. Adviser: Mrs. Lani Rodis.
The study entitled "APPLICATION OF FACTOR ANALYSIS AND MULTIPLE REGRESSION ANALYSIS IN EVALUATING SECONDARY
STUDENTS' PERFORMANCE m MATHEMATICS AT ALFONSO NATIONAL
HIGH SCHOOL" was conducted at Alfonso National High School from January to February 2004. This study aimed to 1) identify the underlying factors with the use of factor analysis; 2) determine the relationship of the underlying factors with the academic performance of the students using multiple regression analysis; and 3) identify the best predictor of mathematics performance of students.
A total of 292 students from Alfonso National High School was considered as subject of this study. Their corresponding grades in English, Science and
Filipino and Mathematics from first grading to third grading were gathered. Questionnaires were also distributed to each of the respondents to determine their scores on the other
variables.
There were 13 variables considered in this study including the students' study habits. For factor analysis, five factors were extracted using Principal Component Method. The factor matrix was further rotated using Varimax Rotation of Factors. From the factor matrix, the variables that loaded heavily were identified and the five extracted factors were defined. From the five extracted factors, the highest loading in each factor

were considered as the independent variable to be used for the first multiple regression analysis wherein the dependent variable is the academic performance of the students in mathematics. The five independent variables were academic performance in English, monthly family income, position in the family, time allotted for studying at night and the number of mathematics books. For the first multiple regression analysis, using the factor scores of the five independent variables, two variables were found as predictors of the mathematics performance of students. The two variables are academic performance in English and the number of mathematics’ books.
For the second multiple regression analysis, the variables used as independent variables are academic performance in the three major subjects (English, Filipino and Science and Technology), class size, household size, monthly family income, number of mathematics books, position in the family, time allotted for studying at night, classroom climate, peers influence and the study habits of students. Four variables were retained as predictors of the academic performance of students in mathematics. The four variables were academic performance in the three major subjects (English, Filipino and Science and Technology) and the number of mathematics books.
The Regression model I explains 53% of the total variation in the students' mathematics performance while the Regression model Il explains 69.2% of the total variation in the academic performance of students in mathematics.

Submitted to the University Library 04/26/2004 SP-2739