CHAPTER IV RESEARCH FINDING AND DISCUSSIONS

Similar documents
CHAPTER IV RESEARCH FINDING AND ANALYSIS

x = , so that calculated

Chapter 3 Describing Data Using Numerical Measures

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

[The following data appear in Wooldridge Q2.3.] The table below contains the ACT score and college GPA for eight college students.

Topic- 11 The Analysis of Variance

Chapter 12 Analysis of Covariance

Statistics Chapter 4

Statistics II Final Exam 26/6/18

Problem Set 9 Solutions

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

Answers Problem Set 2 Chem 314A Williamsen Spring 2000

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

ANOVA. The Observations y ij

Topic 23 - Randomized Complete Block Designs (RCBD)

ANSWERS CHAPTER 9. TIO 9.2: If the values are the same, the difference is 0, therefore the null hypothesis cannot be rejected.

Learning Objectives for Chapter 11

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

Statistics for Economics & Business

Lecture 6 More on Complete Randomized Block Design (RBD)

Economics 130. Lecture 4 Simple Linear Regression Continued

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Chapter 13: Multiple Regression

Using Multivariate Rank Sum Tests to Evaluate Effectiveness of Computer Applications in Teaching Business Statistics

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

/ n ) are compared. The logic is: if the two

CHAPTER IV RESEARCH FINDINGS AND ANALYSIS

Statistical tables are provided Two Hours UNIVERSITY OF MANCHESTER. Date: Wednesday 4 th June 2008 Time: 1400 to 1600

Definition. Measures of Dispersion. Measures of Dispersion. Definition. The Range. Measures of Dispersion 3/24/2014

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Joint Statistical Meetings - Biopharmaceutical Section

Statistics for Business and Economics

A LINEAR PROGRAM TO COMPARE MULTIPLE GROSS CREDIT LOSS FORECASTS. Dr. Derald E. Wentzien, Wesley College, (302) ,

Lecture 4 Hypothesis Testing

Negative Binomial Regression

Module 9. Lecture 6. Duality in Assignment Problems

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu

Statistical Inference. 2.3 Summary Statistics Measures of Center and Spread. parameters ( population characteristics )

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Chapter 11 Analysis of Variance - ANOVA. Instructor: Ivo Dinov,

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

Analytical Chemistry Calibration Curve Handout

FUZZY FINITE ELEMENT METHOD

Lecture Notes on Linear Regression

Lecture 20: Hypothesis testing

Chapter - 2. Distribution System Power Flow Analysis

Modeling and Simulation NETW 707

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

AS-Level Maths: Statistics 1 for Edexcel

1-FACTOR ANOVA (MOTIVATION) [DEVORE 10.1]

Basic Business Statistics, 10/e

Chapter 5 Multilevel Models

CS-433: Simulation and Modeling Modeling and Probability Review

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 14 Simple Linear Regression

CHAPTER 6 GOODNESS OF FIT AND CONTINGENCY TABLE PREPARED BY: DR SITI ZANARIAH SATARI & FARAHANIM MISNI

Polynomial Regression Models

Lecture 16 Statistical Analysis in Biomaterials Research (Part II)

STATISTICS QUESTIONS. Step by Step Solutions.

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

Gravitational Acceleration: A case of constant acceleration (approx. 2 hr.) (6/7/11)

Chapter 11: I = 2 samples independent samples paired samples Chapter 12: I 3 samples of equal size J one-way layout two-way layout

The Study of Teaching-learning-based Optimization Algorithm

Statistics and Quantitative Analysis U4320. Segment 3: Probability Prof. Sharyn O Halloran

THERMAL DISTRIBUTION IN THE HCL SPECTRUM OBJECTIVE

Methods of Detecting Outliers in A Regression Analysis Model.

4.1. Lecture 4: Fitting distributions: goodness of fit. Goodness of fit: the underlying principle

Kernel Methods and SVMs Extension

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Lecture Notes for STATISTICAL METHODS FOR BUSINESS II BMGT 212. Chapters 14, 15 & 16. Professor Ahmadi, Ph.D. Department of Management

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X).

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Cathy Walker March 5, 2010

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

Comparison of Regression Lines

Here is the rationale: If X and y have a strong positive relationship to one another, then ( x x) will tend to be positive when ( y y)

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

Chapter 6. Supplemental Text Material

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

STAT 511 FINAL EXAM NAME Spring 2001

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

Color Rendering Uncertainty

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

BIO Lab 2: TWO-LEVEL NORMAL MODELS with school children popularity data

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Outline. Unit Eight Calculations with Entropy. The Second Law. Second Law Notes. Uses of Entropy. Entropy is a Property.

3) Surrogate Responses

NEW ASTERISKS IN VERSION 2.0 OF ACTIVEPI

NANYANG TECHNOLOGICAL UNIVERSITY SEMESTER I EXAMINATION MTH352/MH3510 Regression Analysis

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH

Gasometric Determination of NaHCO 3 in a Mixture

STAT 3008 Applied Regression Analysis

is the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors

Bayesian Learning. Smart Home Health Analytics Spring Nirmalya Roy Department of Information Systems University of Maryland Baltimore County

x i1 =1 for all i (the constant ).

Transcription:

CHAPTER IV RESEARCH FINDING AND DISCUSSIONS A. Descrpton of Research Fndng. The Implementaton of Learnng Havng ganed the whole needed data, the researcher then dd analyss whch refers to the statstcal data analyss to fnd out whether or not there s a dfference of students understandng on vocabulary of Adjectve between students taught usng song and students taught wthout usng song. The mplementaton of ths research was dvded nto two classes. They were expermental class (VIII B) and control class (VIII C). Before the actvtes were conducted, the researcher explaned the materal and the lesson plan of learnng. In ths research, there were two tests; pre-test and post-test. The pre-test was gven before the students follow the learnng process that was provded by the researcher. The researcher wll gve pre-test to both classes to know how understand the students n the lesson. The test was gven to the students was lstenng test. The teacher asked the students to lsten some songs and texts to fll the blanks of vocabulary of Adjectve.. In treatment, the researcher wll teach the control class by usng conventonal method and expermental class by usng song. After dong the treatment, the researcher wll gve to both classes post-test that the students ha to revse ther exercse to revew fll n the blanks of vocabulary of Adjectve s approprate wth what they lsten. The post-test obtaned the data that wll be analyzed.. Analyss of Pre requste test Before the researcher determnes the sample, the wrter should conduct a normalty and homogenety test by choosng two classes. They are between class VIII B (Expermental Class) between class VIII C 7

(Control Class) as the sample. Ths test conducted to determne whether the sample are homogenous or not. After conducted the test, data analyss was carred out to fnd out the homogenety of the sample. The Data Analyss of Pre-test Value of the Expermental and the Control Class Table 4. The lst of pre-test value of the expermental and the control classes Control Class Expermental Class No Code Code Pre test Post test Pre test Post test C- C 80 E- 53 76 C- 50 70 E- 63 80 3 C-3 70 90 E-3 70 90 4 C-4 56 76 E-4 60 86 5 C-5 60 76 E-5 53 73 6 C-6 63 80 E-6 70 93 7 C-7 53 70 E-7 63 90 8 C-8 66 80 E-8 66 80 9 C-9 56 76 E-9 53 73 0 C0 70 96 E-0 73 00 C- 53 70 E- 58 83 C- 66 96 E- 70 93 3 C-3 63 90 E-3 56 80 4 C-4 50 70 E-4 63 90 5 C-5 73 90 E-5 53 76 6 C-6 56 80 E-6 73 93 7 C-7 50 70 E-7 66 90 8 C-8 60 86 E-8 53 76 9 C-9 53 76 E-9 70 00 8

0 C-0 66 90 E-0 73 90 C- 63 80 E- 66 83 C- 56 76 E- 60 80 3 C-3 66 96 E-3 70 93 4 C-4 60 86 E-4 66 90 5 C-5 63 90 E-5 60 83 6 C-6 56 86 E-6 73 00 7 C-7 63 86 E-7 63 83 8 C-8 60 76 E-8 58 93 Sum 684 88 775 47 Average 60.486 8.749 63.3986 86.343 Varance 40.49735 7.375 48.4735 64.59656 Standard Devaton Mnmum score 6.363753 8.4458 6.94603 8.03799 50 70 53 73 Maxmum score 73 96 73 00 Range Length of the class 3 6 0 7 4 5 4 5 From the table above, we know that there were 8 students n both expermental class and control class. So, there were 56 students from two classes. The mnmum and maxmum score of pre test n control class were 50 and 70. And the mnmum and maxmum score of post test n control class were 53 and 73. In expermental class, the mnmum score both of pre test and post test were 53 and 73. And the maxmum score, both of pre test and post test were 73 and 00. The average of control class n pre test and post test were 60.4 and 8.7. And the average of expermental class n pre test and post 9

test were 63.39 and 86.3. We conclude that there were dfferent student s achevement score n pre test and post test, both of expermental and control classes were ncreasng score. a. Search for the normalty of ntal data n the control class and the expermental class. The normalty test s used to know whether the data obtaned s normally dstrbuted or not. Test data of ths research to fnd out the dstrbuton data s used normalty test wth ch square. ) The result of pre request test of control class Based on the result of pre requste of Class VIII C as control class, the hghest score acheved s 73 and lowest s 50. It means that the range (R)= 3, the number of class s 6, and the length of the class s 4. The result of the calculaton above s, then nputted nto the frequency dstrbuton as follow: Table 4.. Normalty pre test of control class Class f X X f.x f.x 50 53 6 5.5 65.5 309 593.5 54 57 5 55.5 3080.5 77.5 540.5 58 6 4 59.5 3540.5 38 46 6 65 6 63.5 403.5 38 493.5 66 69 4 67.5 4556.5 70 85 70 73 3 7.5 5.5 4.5 5336.75 Sum 8 690 033 The table descrbe that there are sx nterval classes of pre test n control class. The mnmum score s 50 and maxmum score s 73. The length of each class s 4. f s frequency means students 30

score of expermental class n pre test of each nterval class. X s mddle score of each nterval class.. a) Calculatng of the average X ( x ) : X _ = f f x = 690 = 60.35743 8 b) Calculate varance S = n f χ ( fχ ) n( n ) = 8.033 (690) 8(8-) = 45.4603746 c) Calculate standard devaton = 45.4603746 = 6.7446674 Table 4.3. Dstrbuton frequency of control group Class Bk Z P(Z ) 49.5 -.6 0.4463 50 53 -,4 53.5 -.0 0.3454 54 57 -,39 57.5-0,4 0.64 58 6-0,65 6,5 0.7 0.0673 6 65 0,0 Wde Area E O ( O E ) 0.009 3.0 6.996 0.83 5.4 5 0.0354 0.0968.9 4 0.49 0.099 6.3 6 0.040 E 3

65,5 0.76 0.77 66 69 0,85 0.353 4. 4 0.0008 69,5.36 0.45 70 73,60 0.069.9 3 0.708 73,5.95 0.4744 #REF! X² = 4.0855 For = 5%, wth dk = 6-3= 3 t s obtaned X² tabel = 7.8. If X² < X² table the data s n the normal dstrbuton, because of X² count = 4.0855 s lower than X² tabel = 7.8, based on the result above the data s the normal dstrbuton. ) The result of pre request test of expermental class. Based on the result of pre requste of Class VIII B as expermental class, the hghest score acheved s 73 and lowest s 53. It means that the range (R)= 0, the number of class s 6, and the length of the class s 4. The result of the calculaton above s, then nputted nto the frequency dstrbuton as follow: Table 4.4. Normalty test of pre test of expermental class. Class f X X f.x f.x 53 56 6 54.5 970.5 37 78.5 57 60 5 58.5 34.5 9.5 7.5 6 64 4 6.5 3906.5 50 565 65 68 4 66.5 44.5 66 7689 69 7 5 70.5 4970.5 35.5 485.5 73 76 4 74.5 5550.5 98 0 Sum 8 786 599 3

The table descrbe that there are sx nterval classes of pre test n expermental class. The mnmum score s 53 and maxmum score s 73. The length of each class s 4. f s frequency means students score of control class n pre test of each nterval class. X s mddle score of each nterval class.. a) Calculatng of the average X ( x ) : X _ = f f x = 786 = 63.78574 8 b) Calculate varance S = n fχ n( n ) ( fχ ) = 8.599 (786) 8.(8-) = 5.06455 c) Calculate standard devaton = 5.06455 = 7.43804 Table 4.5. Dstrbuton frequency of expermental group Class Bk Z P(Z ) 5.5 -.58 0.449 Wde Area E O ( O E ) E 53-56 57-60 6-64 56.5 -.0 0.346 60.5-0.46 0.77 0.0968.7 6 3.990 0.689 4.7 5 0.055 0.374 3.8 4 0.006 33

65-68 69-7 73-76 64.5 0.0 0.0398 68.5 0.66 0.454 7.5. 0.3888 76.5.78 0.465 #REF! 0.055 5.8 4 0.535 0.434 4.0 5 0.46 0.0737. 4.87 X² = 6.6065 For = 5%, wth dk = 6 3 = 3 t s obtaned X² tabel = 7,8. If X² count < X² table, so the data s n the normal dstrbuton, because of X² count = 6.6065 < X² table = 7,8, so the data s the normal dstrbuton. Based on the result of the normalty test of expermental class and control class, t can be seen that two classes are normal dstrbuton, because X² count < X² table, so the data s n the normal dstrbuton. b. Search for the homogenety of ntal data n the control class and expermental class. Homogenety test s used to fnd out whether the group s homogeneous or not. The data of ths research uses Bartlett test. Hypothess: H o : σ = σ H a : σ σ Table 4.6. Homogenety test of pre test of expermental and control classes 34

Varants sources Control class Experment class Sum 684 775 N 8 8 60.4 63.39 Varant (S ) 40.50 48.5 Standard devaton(s) 6.36 6.95 Sample Dk Table 4.7 Bartlett Test /dk S Log S dk.log S dk * S 7 0.0370 40.497.607 43.40 093.48 7 0.0370 48.47.683 45.454 30.678 Sum 54 88.854 396.07 ) The merger varant of populaton group ( n ) S S = n ( ) = 396.07= 44.3735 54 ) The value of B B = (Log S ) S (n - ) =.6474. 54 = 88.94407 3) X value = (Ln 0) { B - S(n-) log S} =,30585 (88.94407 88.854) = 0.06705 For = 5% wth dk = k- = - = s obtaned X table = 3,84. If X count< X table so the data s homogeneous. Because X count =3.6858 s lower than X table = 3, 84, so the data s homogeneous. 35

c. Searchng for the average smlarty of the ntal data between the control and expermental class. To analyze the smlarty of average, the researcher uses t-test. Table4.8 The average smlarty test of pre test of the expermental and control classes x s Sampel N S T 6 63.39 48.5 8 6.666.8554 60.4 40.50 8 Based on the computaton of the homogenety test, the expermental class and control class have same varance. So, the t test formula: t( α ) < t < t ( α ) Ho s accepted f Wth =5% and dk = n = 54. + n t table =,0094. Because t count =.8554 < t table =.00, so there s a smlarty of average. d. Searchng for normalty data of post test of the control and expermental class ) The result of post test of expermental class Based on the result of post test of Class VIII B as expermental class, the hghest score acheved s 00 and lowest s 73. It means that the range (R) = 7, the number of class s 6, and the length of the class s 5. The result of the calculaton above s, then nputted nto the frequency dstrbuton as follow: Table 4.9 The normalty test table of post test of expermental group 36

Class f X X f.x f.x 73 77 5 75 565 375 85 76 8 4 80 6400 30 5600 83 87 5 85 75 49 365 88 9 6 90 800 540 48600 93 97 5 95 905 475 455 98 00 3 99 980 97 9403 Sum 8 43 978 a) Calculatng of the average X ( x ) : X _ = f f x = 43 = 86.85743 8 b) Calculate varance S = n f χ n( n ) ( fχ ) = 8. 978 (43) 8. (8-) = 64.4973545 c) Calculate standard devaton = 64.4973545 = 8.0304499 Table 4.0. Dstrbuton frequency of expermental group Class Bk Z P(Z ) 7.5 -.79 0.463 Wde area 73 77 -.76 0.085.4 5.877 E O ( O E ) E 37

77.5 -.7 0.3780.5 78 8 -.3 0.77 4.8 4 0.360 8.5-0.54 0.063 5.0 83 87-0.50 0.744 4.9 5 0.008 87.5 0.08 0.039 3.8 88 9 0.4 0.70 6.4 6 0.098 9.5 0.70 0.589 6.3 93 97 0.77 0.486 4. 5 0.693 97.5.33 0.4075 3.9 98-00.40 0.0479.3 3.055 00.5.70 0.4553.7 #REF! X² = 5.604 For = 5%, wth dk = 6-3= 3 t s obtaned X² tabel = 7,8. If X² count < X² table, so the data s n the normal dstrbuton, because of X² count = 5.604 < X² table =7, 8, so the data s the normal dstrbuton. ) The result of post test of control class Based on the result of post test of Class VIII C the hghest score acheved s 96 and lowest s 70. It means that the range (R)= 6, the number of class s 6, and the length of the class s 5. The result of the calculaton above s, then nputted nto the frequency dstrbuton as follow Table 4. The Normalty test of post test of control class Class f X X f.x f.x 70 74 5 7 584 360 590 75 79 6 77 599 46 35574 80 84 5 8 674 40 3360 38

85 89 4 87 7569 348 3076 90 94 5 9 8464 460 430 95 99 3 97 9409 9 87 Sum 33 95937 a) Calculatng of the average X ( x ) : X _ = f f x = 33 = 83.5 8 b) Calculate varance S = n f χ n( n ) ( fχ ) = 8. 95937 (33) 8. (8-) = 69.67596 c) Calculate standard devaton = 69.67596 = 8.3470 Table 4.. Dstrbuton frequency of control class Class Bk Z P(Z ) 69.5 -.65 0.450 Wde area E O ( O E ) E 70 74 -.76 0.0975.9 5.475 74.5 -.05 0.357.5 75 79 -.3 0.794 5.4 6 0.07 79.5-0.45 0.734 5.0 39

80 84-0.50 0.39 3.4 5 0.7349 84.5 0.5 0.0595 3.8 85 89 0.4 0.35 6.4 4 0.906 89.5 0.75 0.730 6.3 90 94 0.77 0.38 4. 5 0.768 94.5.35 0.4 3.9 95-99.40 0.063.9 3 0.6480 99.5.95 0.474.7 #REF! X² = 4.005 For = 5%, wth dk = 6-3= 3 t s obtaned X² tabel = 7.8. If X² count < X² table, so the data s n the normal dstrbuton, because of X² count = 4.005< X² table =7.8, so the data s the normal dstrbuton. Based on the result of post test of the normalty test of expermental class and control class, t can be seen that classes are normal dstrbuton, because X² count < X² table, so the data s n the normal dstrbuton. e. Search for the homogenety of control class and expermental class. Table 4.3 Homogenty of post test of expermental and control classes Varants Sources CONTROL EXPERIMENT Sum 88 398 N 8 8 8.7 85.64 Varance (S ) 7.3 67.50 Standart devaton (S) 8.45 8. 40

Based on the table above, we know that total score, both control and expermental class are 88 and 398. The average of control class and expermental class are 8,7 and 85.64. Table 4.4. Bartlett test Sample Dk /dk S Log S Sum dk.log S dk * S 7 0.0370 7.33.853 50.037 95.74 7 0.0370 67.497.89 49.39 8.49 54 99.48 3748.43 ) The merger varant of populaton group ( n ) S S = n ( ) = 3748.43= 69.4005 54 ) The value of B B = (Log S ) S (n - ) =.8444. 54 = 99.436807 3) X value = (Ln 0) { B - S(n-) log S} =,30585 (99.436807 99.48) = 0.00506 For = 5% wth dk = k- = - = s obtaned X table = 3.84. If X count < X table so the data s homogeneous. Because X count = 0.00506 s lower than X table = 3.84, so the data s homogeneous. f. Testng the smlarty of average between expermental and control class. To analyze the smlarty of average, the researcher uses t-test. 4

The hypothess: : µ = µ H o H Where, t = t = Wth : µ > µ s µ µ x = The average of expermental class = The average of control class x + n n 86.3 8.7 8.4376 s = 8 + 8 =.09 ( n ) s + ( n ) n + n s s = ( 8 ).64.60 + ( 8 ).7.3 8 + 8 = 8.4376 Table 4.5 The average smlarty of post test of expermental class and control classes Source of varance Experment Control T Mean 86.3 8.7 Varance 64.60 7.3 (s) 8.04 8.45 8.4376.09 N 8 8 s For α = 5% wth dk = 8 + 8 - = 54 s obtaned 0.97556 =,67 and =.09. 4

The test crteron s: H a s accepted f > t table by degrees of freedom of df = n + n ) and by the chance of 0.05 level of sgnfcance. Because > ( t table (.09 >.67 ) t means that H o s rejected and H s accepted. It means that usng song s more effectve than explanaton only n teachng vocabulary of Adjectve. B. Dscusson of the Research Fndngs The technque of teachng s one of the factors that nfluence the result of the study. In the process of teachng, the teachers must choose approprate technque, so the students wll enjoy the lesson. Based on the result of tests, the process of learnng Englsh usng song as a medum to vocabulary of Adjectve n SMP N Gubug n academc year of 0/03 could help the students to understand some words of vocabulary of Adjectve effectvely, so they could mprove ther understandng on vocabulary of Adjectve. Besdes, the students who had been taught usng song felt more fun and enjoy. They were not bored n the classroom durng the process of teachng learnng. Teachng learnng process n the expermental class used song n teachng vocabulary of Adjectve. In the process of teachng learnng, the teacher gave a worksheet to the students; there s a song lyrc that blank words of adjectve. The teacher played the musc. And the students had to lsten the song carefully and they have to fll blank words of song lyrc n the worksheet. In the end of the learnng, the teacher took worksheets of the students and reflected the materal that had been learnt. Meanwhle, teachng learnng process n the control class was mplemented through conventonal method. In the process of teachng learnng, the teacher explaned the pattern the materal of adjectve to the students. Then the teacher asked to the students to wrte some words that they known on ther paper. In the end of learnng, the teacher gave homework to the students based on the materal. 43

The result of the research shows that the expermental class (the students who are taught usng song) has the mean value (85.64) meanwhle the control class (the students who are not taught usng song) has the mean value (8.7). It can be sad that the achevement score of expermental class s hgher than control class. The data were obtaned from the students achevement score of the tests. They were pre test and post test scores from the expermental and the control class. The average score of pre test for expermental class was 63.39. And the average score of pre test for control class was 60.4. The followng was the smple table of pre and post test students average score. Table 4.6 The pre test and post test students average score of the expermental and control class. Class The Average of Pre test The Average of Post test Experment 63.39 85.64 Control 60.4 8.7 Based on the result of pre-test and post-test, t could be concluded that usng song was effectve to teach vocabulary of Adjectve at the eght graders of SMP N Gubug n academc year of 0/03. It can be seen from the result of analyss by usng t- test formula:. The achevement of experment group before treatment s rather same wth control group before treatment. It can be seen from the mean of pre test of expermental group (63.39) and control group (60.4) before the treatment. There s no sgnfcant dfference n students achevement between experment and control group.. The achevement of expermental group after treatment s better than experment group before treatment. It can be seen from the mean of post- 44

test of expermental group (85.64). It s hgher than experment group (63.39) before the treatment. 3. The achevement of control group before treatment s lower than control group after treatment. It can be seen from the mean of pre-test of control group (60.4). It s lower than the mean of post-test of control class (8.7) after the treatment. 4. The achevement of expermental group after treatment better than control group after treatment. It can be seen from the mean of post test of the expermental group (85.64). It s hgher than the mean of post test of control group (8.7) after the treatment. 5. The case n both groups s the same that there s an mprovement n each group s cogntve achevement. However, the mprovement on control group s not as much as on the expermental group. It s convnced by the statstcal result of the hypothess test. The test by means of t-test formula shown that t count =.09 > t table =.67 at 0.05 level of sgnfcance by 56 degrees of freedom. It means that the usng of song s more effectve to mprove the students understandng on vocabulary of Adjectve than usng conventonal method (explanaton only). So, t could be concluded that usng song s effectve to facltate students understandng on vocabulary of Adjectve n expermental group. It can be seen at mean of both groups. There s sgnfcant dfference n the students vocabulary achevement between experment and control group. C. Lmtaton of the Research. The researcher realzes that ths research has not been done optmally. There were obstacles faced durng the research process. Some lmtatons of ths research are:. Relatve short tme of research makes ths research could not be done maxmum. 45

. The research s lmted at SMP N Gubug. So that, when the same research wll be gone n other schools. It s stll possble to get dfferent score. Consderng all those lmtatons, there s a need to do more research about teachng vocabulary of Adjectve usng song, so that the more optmal result wll be ganed. 46