Chapter 3 Methods and Procedures This Chapter

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CHAPTER 3 METHODS AND PROCEDURES

This chapter briefly presents the different methods and procedures used by the researcher researcher in doing his investigati investigation. on. It consists consists of the research design, the research research locale locale,, and the subjects subjects..

It also includes includes the instrume instruments nts used in the collect collection ion and

gathering of data, as well as the statistical tools used in processing and analyzing the data.

Research Method

This study utilized utilized the descriptive descriptive correlati correlational onal design. Sanchez Sanchez (1998) stated that descriptive research includes all studies that purport to present facts concerning the nature and status of anything – a group of persons, a number of objects, a set of  conditions, a class of events, a system of thought or any other kind of phenomena which one may wish to study. study. In this study, study, the nature and status status of the Medical Technology Technology graduates were determined. The study also employed a correlational design in order to determine the extent to which the different variables are related to each other in the population of interest. Through this method, the researcher was able to ascertain how much variation is caused   by each of the independ independent ent variabl variables es to the dependen dependentt variab variable. le. The magnitud magnitudee and direction of the relationship was determined and was used for further computations to  predict the value of the dependent variable. The The impa impact ct of the the acad academ emic ic,, clin clinic ical al and and semi semina narr rati rating ngs, s, as indep indepen enden dentt variables, on the dependent variable, board examination performance of the Medical Technology graduates, was measured and the formers’ predictive value determined.

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Subjects and Locale of the Study

The subjects of the study were the medical technology graduates of Angeles University Foundation who graduated from 1995 – 2000. Each of the subjects should have taken the licensure examination on the same year as their graduation, that is, they should have graduated March and have taken the board examination on September of the year they graduated graduated regardless regardless of whether the former passed passed or not. All graduates graduates who have re-enrolled a failed subject from a school other than Angeles University Foundation were disqualified. There were a total of one hundred sixty nine (169) medical technology graduates who were considered in the study. The study was conducted at Angeles University Foundation particularly at the Dean’s Office of College of Allied Medical Professions, the Office of the University Regist Registrar rar and at the Record Recordss Secti Section on of the Profes Professio sional nal Regulat Regulation ion Commis Commissio sion, n, Morayta, Manila. The College of Allied Medical Professions opened its doors to the first batch of  students for both Medical Technology and Physical Therapy on June 1990 and has since  been in the pursuit of academic excellence. The academic programs cited were given the stamp of approval by the Professional Regulation Commission and were later granted government recognition on June 15, 1992 and August 25, 1993 respectively. At pres present ent the the two two cours courses es are are recog recogni nized zed by the the Prof Profes essi sion onal al Regu Regula lati tion on Commission as the college ranked 3rd among 68 schools offering Medical Technology 8th out of 112 schools which offer Physical Therapy.

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Research Instruments

The researcher gathered data by examining, verifying verifying and analyzing analyzing the grading grading sheets from the College College of Allied Medical Professions Professions and of the Registrar’s Office. The offici official al printo printout ut of the board board examin examinati ation on perfor performan mance ce of the medica medicall technol technology ogy graduates had also undergone the same process. Upon approval of the request letter, the researcher gathered the grading sheets of  the the foll follow owin ing g subj subjec ects ts:: Clin Clinic ical al Chem Chemis istr try y 1 & 2, Micr Microbi obiol ology ogy,, Para Parasi sito tolo logy gy,, Hematology, Serology, Blood Banking, Histopathology, and Medical Technology Laws and Ethics. The A data matrix table was prepared to encode all the data needed in the study. The data data matrix matrix was used together together with a data-c data-codi oding ng manual. manual. The data encoded encoded on the matrix table included the year the students graduated, their names, academic ratings in the different subject areas, their internship grades, seminar grades, and board examination  performance which is inclusive of all ratings per subject taken and the general weighted average.

Data Collection

The initial phase of the study was the gathering of data pertaining to the medical technol technology ogy graduat graduates es of Angeles Angeles Univer Universit sity y Foundat Foundation ion,, Colleg Collegee of Allied Allied Medica Medicall Professions from academic academic year 1995 – 2000. A letter was sent to the the Dean of CAMP to seek permission permission to review review the records of the 1995 to 2000 graduates. graduates. The researcher  researcher  likewise requested for an endorsement letter to be presented to the Professional regulation Commission and to the Registrar so that records of the medical technology graduates’

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 board examination performance as well as the academic, clinical and seminar ratings can  be availed of respectively. An endorsement letter from the Dean of CAMP presented to the Registrar enabled the researcher to access the grading sheets of the subjects for their grades in the different Medical Medical Technology Technology subject areas. areas. Comparison Comparison was made between between the data obtained from the Registrar’s Office and CAMP. For the medical technology graduates’ board examination ratings, the researcher    presented the endorsement letter of the Dean of CAMP to the section chief of the Educational Task Force of the Professional Regulation Commission. All data collected were encoded using a data matrix table prepared by the researcher.

Data Processing and Analysis

A. The data gathered gathered were tallied, tallied, tabulated, tabulated, analyzed analyzed and interpreted interpreted.. The data for the academic, clinical and seminar ratings were grouped based on the following (CAMP Bulletin 2000): 97

–

Excellent

91 – 96

–

Very Good

82 – 90

–

Good

77 – 81

–

Satisfactory

75 – 76

–

Passed

below 75

–

Failed

To analyze and describe the data obtained, the researcher made use of a computer    program program called Statisti Statistical cal Package for the Social Sciences Sciences (SPSS (SPSS version 9.05). The statistical tools that were employed are as follows:

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1. Freq Frequen uency cy Dist Distri ribu buti tion on A frequency distribution is a grouping of data into categories showing the number of observations observations in each category (Utzurrum, (Utzurrum, 1997). This statisti statistical cal tool was employed to describe the board examination ratings and scores in each of the subject subject areas given during the licensure examination examination which includes includes Clinical Clinical Chemistry Chemistry,, Microbiolo Microbiology-Pa gy-Parasit rasitology ology,, Hematology Hematology,, Serology-B Serology-Blood lood Banking, Banking, and Histopathol Histopathology-Me ogy-Medical dical Technology Technology Laws Laws and Ethics. Ethics. The academic academic and clinical ratings were not described using this statistical tool since the CAMP Bulletin provided the categories for classification of the data.

2. Perc Percent entag agee Distr Distrib ibut utio ion n

Percentage distribution was used in the analysis of frequency distribution data. This statistica statisticall tool characterized characterized all variables under study, study, which includes the academic, clinical, and seminar ratings as well as the board examination  performance of the subjects. The percentage distribution distribution is computed by dividing the number of responses by the total number of responses multiplied by 100. The formula for percentage is as follows: %=

number of responses total number of respondents

X 100

3. Mean Mean is defined as a measure of central tendency wherein it is the point on the score scale which is equal to the sum of scores divided by the number of 

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respondents (Cassens, 1987). Subjected to these tests tests were the academic, academic, seminar  and clinical ratings as well as the board examination performance of the Medical Technology graduates. The mean, for grouped data, may be computed as (Downie, 1983): X = Σ Xifi  N Where: X = mean Xi = midpoint fi = frequency  N = number of cases

3. Standard Deviation The standard deviation deviation is the positive positive square root of the variance variance (Reyes, (Reyes, 1996). It is the most useful measure of dispersion (Cassens, 1987) and was used to describe the variation and scatter of values of the variables academic, clinical, and seminar ratings. This statistical statistical tool also described described the degree of dispersion dispersion of the board examination ratings. The standar standard d deviati deviation on for groupe grouped d data data was determ determine ined d as (Downi (Downie, e, 1983): s=

2

2

NΣ X – (Σ X) √ N (N-1)

Where: s = standard deviation  N = number of cases X = value for the observation Σ = summation symbol

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B. To test the null hypothesi hypothesis, s, the following following inferential inferential statis statistics tics were employed employed::

1.

Pearson r  To determine the relationship between two quantitative variables, the Pearson Product Product Moment Correlation Correlation Coefficien Coefficientt was used.

The relationship relationship between between

each each of the the foll followi owing ng vari variab able less and and the the boar board d exam examin inat atio ion n rati rating ngss were were determined using this statistical tool. A. Acade Academi micc Rati Rating ngss B. Semi Seminar nar Rati Rating ngss C. Clin Clinic ical al Rati Ratings ngs

Formula:  NΣ XY – (Σ X) (Σ Y) r=

√ [NΣ X2 – (Σ X)2] [NΣ Y2 - Σ Y)2]

Where:  N = number of cases or observations X = value of the independent or predictor variable Y = value of the dependent or criterion variable r = Pearson product moment correlation coefficient The Guilford Coefficient values were used to determine the degree of  relati relations onship hip betwee between n the variab variables les as reflec reflected ted by the Pearso Pearson n r correl correlati ation on coefficient. The coefficient values and interpretation are as follows:

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Value

Interpretation

0

No correlation

0.21 – 0.40

-

Weak or low correlation

0.41 – 0.60

-

Moderate correlation

0.61 – 0.80

Strong or high correlation

0.81 – 0.99

-

Very strong or very high correlation

1 .0

-

Perfect relationship

After the correlation coefficients are computed, the algebraic signs, either   positive or negative, were interpreted as follows: (+) = Direct relationship which indicates a parallel increase or decrease in values.

The variables follow the same rhythm or direction of movements. (-) = Inverse relationship where the variables move in opposite direction. When

one increases in value, the other variable decreases.

2. Pred Predic icti tive ve Valu Valuee The predictive value is defined as the variation caused by the independent variables, variables, on the board examinati examination on performance. performance. It is computed computed getting getting the squa square red d valu valuee of the the Pear Pearso son n prod produc uctt mome moment nt corre correla lati tion on coeff coeffic icie ient nt and and multiplying it by 100. The formula is as follows: 2

Predictive value = r  x 100

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Linear Regression Analysis. This is a statistical tool employed in order to discover the effect of one variable on another variable (Parel, 1986). The test also performs correlational analysis (Pearson r) and is similar to simple correlational analysis, but whilst correlation analysis allows us to conclude how strongly two variables relate to each other (both magnitude and direction), linear linear regres regressio sion n will will answer answer the questi question on by how much much will will y (depen (dependent dent variab variable) le) change, change, if x (predi (predicto ctorr or independen independentt variab variable) le) changes. changes.

Linear  Linear 

regression regression gives a measure measure of the effect x has on y, or it allows the researcher researcher to  predict y from x (Dancey, 1999). When When linear linear regres regressio sion n analys analysis is is perfor performed med,, a regres regressio sion n equatio equation n is obtained, which shows the way in which y changes as a result of change in x. The general formula is as follows (Dancey, 1999):

Y = a + bx where: Y = is the variable variable to be predicted predicted x = is the score on the variable x  b = is the value for the slope of the line a = is the value of the constant or intercept The value for the intercept or constant, which is a, may be computed as follows (Reyes, 1996): a = X – bY where:

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a = value for the constant or intercept and makes the mean of the actual or observed values equal to the predicted values of Y  b = value for the slope of the line and indicates the amount of  change in Y per unit change in X. X = mean of the observation for the predictor variable Y = mean of the observation for the dependent variable

The value for b was determined as:   b=

nΣ XY - Σ XΣ Y 2

2

nΣ X – (Σ X)

Where:

 b = value for the slope of the line n = total number of observations or cases c ases X = observation or values for the predictor variable Y = observation or values for the dependent variable

4. Mult Multip iple le Regre Regress ssio ion. n. Multip Multiple le regressi regression on is an extens extension ion of linear linear regress regression ion..

In order to

discover the ways in which several variables (called independent or predictor  variables) variables) are related related to another (called (called the dependent or criterion criterion variable), variable), this method is made use of. This technique is able to to give information on the ways in which the independent variables combined relate to the dependent variable, and how each of the variables relate to the dependent variable, separately (Dancey, 1999).

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The regression equation is just an extension of the linear regression and is as follows: y = a + b1x1 + b2x2 + b3x3 where:

y is the variable to be predicted x1 is the score on the variable x1 x2 is the score on the variable x2 x3 is the score on the variable x3  b is the value for the slope of the line a is the value of the constant or intercept

The independent variables academic, clinical and seminar ratings were the   predictor variables and board examination rating as the dependent or criterion variable. Upon measurement of the significance of the result, the following basis was used to determine determine the rejection rejection or acceptance of the null hypotheses. hypotheses. This  basis was used in all of the hypotheses formulated in this study. Rejection Rejection of null hypothesis hypothesis – reject the null hypothesis if the computed

significance level is lower than 0.05. (Dancey, 1999) Acceptance of the null hypothesis – accept the null hypothesis if the

computed significance level is higher than 0. 05. (Dancey, 1999)