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Perceived Effects of Biometric System to Teachers

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INTRODUCTION

Background of the Study Globalisation is a complex international system in which migration has a central roll. Even though movement is a prerequisite the globalisation process, there are vast amount of instances established for regulating this movement of people, merchandises, capital, etc. To gain control over the direction and amount of movement is an essential aspect of both security and capitalism. If we look at this issue from another point of view, we can see that the risks posed on integrity, privacy and autonomy are of a pressing character.

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There are extensive plans on implementations of biometric technology within foreseeable future. Currently, the most popular techniques are the ones are based on physical characteristics as fingerprint ridge pattern, hand geometry, retinal recognition, facial recognition, and also behavioural characteristics such as voice verification and signature stoke pattern. Students perform best if teachers are doing their job as its best. A study conducted by Columbia School of Business indicates that students perform worse in years when their teacher is most absent.

Absenteeism and being late of the teachers are some of the common problems of a school which lead to worse performances of students. The key to this problem is to handle and monitor properly their attendances. Biometrics technology has seen as a solution. Biometrics technology has recently seen an increase in deployment as it is implemented into airports, manufacturing facilities, and schools. A technological system that uses information about a person (or other biological organism) to identify that person.

It pursues and provides answers to the questions and dilemmas of tomorrow’s world, which is becoming an increasingly necessary part of the knowledge base of technological understanding. The focus of this study is how this system affects each teacher’s everyday life. Through this research, we will be able to know if biometric system is the ultimate key and if can help for developing efficiency and effectiveness of teachers, contribute to the quality education offer by the university which yields to quality products or quality graduates and to become a competitive citizen in a competitive world.

Statement of the Problem This study investigated the perceived effects of biometric system to teachers as an effective system to monitor attendance as evaluated by the College of Business Administration students at the Roxas City Campus of Capiz State University during the first semester of academic year 2013-2014. 1. What is the demographic profile of the respondents? 2. What are the perceived effects of biometric system to the respondents? 3. What are the perceived effects of biometric system of the respondents when grouped according to demographic profile? 4.

Is there a significant difference in the perceived effects of biometric system to the respondents when grouped according to demographic profile? 5. Is there a significant relationship between respondents’ demographic profile and their perceived effects of biometric system? Hypotheses 1. There is no significant difference in the perceived effects of biometric system to the respondents when grouped according to demographic profile. 2. There is no significant relationship between respondents’ demographic profile and their perceived effects of biometric system. Theoretical Framework

This study was based on the Innovation Diffusion Theory by Everett Rogers. Roger’s work asserts that 4 main elements influence the spread of a new idea: the innovation, communication channels, time, and a social system. These elements work in conjunction with one another: diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. Rogers adds that central to this theory is process. Individuals experience 5 stages of accepting a new innovation: knowledge, persuasion, decision, implementation, and confirmation.

If the innovation is adopted, it spreads via various communication channels. During communication, the idea is rarely evaluated from a scientific standpoint; rather, subjective perceptions of the innovation influence diffusion. The process occurs over time. Finally, social systems determine diffusion, norms on diffusion, roles of opinion leaders and change agents, types of innovation decisions, and innovation consequences. An individual might reject an innovation at any time during or after the adoption process. The rate of adoption is defined as the relative speed in which members of a social system adopt an innovation.

Rate is usually measured by the length of time required for a certain percentage of the members of a social system to adopt an innovation (Rogers 1962, p. 134). The rates of adoption for innovations are determined by an individual’s adopter category. In general, individuals who first adopt an innovation require a shorter adoption period (adoption process) when compared to late adopters. Within the rate of adoption, there is a point at which an innovation reaches critical mass. This is a point in time within the adoption curve that the number of individual adopters ensures that continued adoption of the innovation is self-sustaining.

Illustrating how an innovation reaches critical mass, Rogers outlines several strategies in order to help an innovation reach this stage. Strategies to propel diffusion include: when an innovation adopted by a highly respected individual within a social network, creating an instinctive desire for a specific innovation. Also, injecting an innovation into a group of individuals who would readily use said technology, and provide positive reactions and benefits for early adopters of an innovation. Bearing that theory in mind, it is already the age of technology, the world of innovations.

Man creates something could make his life easier and comfortable. He does something better for his development in life. Teachers are compelled to adopt the innovations of technology whether they are in favor or not. The innovation they faced is not to change for something not good but it is for their development to become better individuals and professional. Conceptual Framework Figure 1 shows the dependent and independent variables in the study. The independent variable includes the age, civil status, position, and sex of the respondents. The dependent variable is the perceived effects of Biometric System to teachers.

It is assumed that the perception of teachers to Biometric system is dependent on the age, civil status, position, and sex of the respondents. Significance of the Study This research study will be significant for students, teachers, school administrators, and future researchers. Teachers. This research study would help to enlighten them about the good effects of biometric system and how helpful this system to their profession. Students. This research study will help students in a way that their teachers would be efficient in having their classes. Enables to teach them properly and obtain better performance.

School Administrators. to help them realize how good it is having an attendance biometric system and contribute something good to the university. Future Researchers. The result of this study will help the researcher knows what to do with the conducted studies on the similar concern. Scope and Limitation of the Study This study investigated the evaluation of the teachers of the College of Business Administration at Capiz State University – Main Campus, Roxas City with the position as regular and emergency teachers on their perceived effects of biometric system during the first semester of academic year 2013 – 2014.

The demographic factors used in the study were limited to the respondents’ age, sex, civil status, and position. The research instrument use to gather the needed data was a pen-and-paper survey questionnaire. The relationships between the following variables were explored. First, the perceived effects of biometric system to teachers. Second, the perceived effects of biometric system to teachers when grouped according to age, sex, position and civil status. Definition of terms The following terms are conceptually and operationally defined as they apply to this study.

Biometrics – The term ‘biometrics’ refers to a measurable characteristic that is unique to an individual such as fingerprints, facial structure, the iris or a person’s voice. This is the subject of the study. The significant thing talks about. Age is the length of time that one has existed. In this study, it refers to the number of years the respondents have lived at the time the research is conducted. Teacher is one who teaches or instructs; one whose business or occupation is to instruct others; an instructor; a tutor.

Emergency teacher is a substitute teacher is a person who teaches a school class when the regular teacher is unavailable. In this study, it refers to the position of our respondents. Regular Teacher hired by the school to teach the whole academic year and so on. It refers to our respondents. CHAPTER II REVIEW OF RELATED LITERATURE The content of this chapter is based on review of related literature and to provide historical information pertaining to the different concepts in the study.

There are many biometrics in use today and a range of biometrics that are still in the early stages of development. Biometrics can, therefore, be divided into two categories: those that are currently in use across a range of environments and those still in limited use or under development, or still in the research realm. Here we present literature survey for some of the biometrics of the two categories. Biometrics Currently in Use across a Range of Environments Fingerprint Fingerprint is the pattern of ridges and valleys on the tip of a finger and is used for personal verification of people.

Fingerprint based recognition method because of its relatively outstanding features of universality, permanence, uniqueness, accuracy and low cost has made it most popular and a reliable technique and is currently the leading biometric technology [Jain et al. 2004]. There is archaeological evidence that Assyrians and Chinese ancient civilizations have used fingerprints as a form of identification since 7000 to 6000 BC [Maltoni et al. 2003]. Henry Fauld in 1880 laid the scientific foundation of the modern fingerprint recognition by introducing minutiae feature for fingerprint matching [Maltoni et al.

2003]. Current fingerprint recognition techniques can be broadly classified as Minutiae-based, Ridge feature-based, Correlation-based [Jain and Prabhkar, 2001] and Gradient based [Aggarwal et al. 2008]. Face Face recognition for its easy use and non intrusion has made it one of the popular biometric [Chellappa, 1995]. A summary of the existing techniques for human face recognition can be found in [Chellappa et al. 1995; Zhao et al. 2003]. Further, a survey of existing face recognition technologies and challenges is given [Abate et al. 2007]. A number of algorithms have been proposed for face recognition.

Such algorithms can be divided into two categories: geometric feature-based and appearance-based. Appearance-based methods include: Eigenfaces [Turk and Pentland, 1991], Fisherfaces [Belhumeur et al. 1997], Independent Component Analysis (ICA) [Bartlett et al. 2002], Kernel Principal Component Analysis (KPCA) [Scholkopf et al. 1999, Kim et al. 2002], Kernel Fisher Discriminant Analysis (KFDA) [Liu 2004, Yang 2002], General Discriminant Analysis (GDA) [Baudat and Anouar, 2000], Neural Networks [Lawrence et al. 1998], and Support Vector Machine (SVM) [Phillips, 1999; Jonsson et al. 2002].

An inherent drawback of appearance-based methods is that the recognition of a face under a particular lighting and pose can be performed reliably when the face has been previously seen under similar circumstances. Further, in appearance-based methods the captured features are global features of the face images and facial occlusion is often difficult to handle in these approaches. Geometric feature-based methods are robust against variations in illumination and viewpoints but very sensitive to feature extraction process. The geometry feature-based methods analyze explicit local facial features, and their geometric relationships.

The geometry feature-based methods include: Active Shape Mode [Cootes et al. 1995; Yuille, 1991], Elastic Bunch Graph matching [Wiskott et al. 1997] and Local Feature Analysis (LFA) [Penev and Atick 1996]. Iris The iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. A survey on the current iris recognition technologies is available in [Bowyer et al. 2008]. [Flom and Ara, 1987] first proposed the concept of automated iris recognition. It was John Daugman who implemented a working automated iris recognition system [Daugman, 1993; Daugman, 2003].

Though Daugman’s system is the most successful and most well known, many other systems have also been developed. An automatic segmentation algorithm based on the circular Hough transform is employed by [Wildes, 1997]. [Boles and Boashash, 1998] extracted iris features using a 1-D wavelet transform. [Sanchez-Avila and Sanchez-Reillo, 2002], further developed the iris representation method proposed by Boles et al. [Lim et al. 2001] extracted the iris feature using 2-D Haar wavelet transform and [Park et al. 2003] utilized directional filter banks to extract the normalized directional energy as a feature.

[Kumar et al. 2003] employed correlation filters. Recently Ma et al. proposed two iris recognition methods, one using multi-channel Gabor filters [Ma et al. 2002] and the other using circular symmetric filters [Ma et al. 2002]. Hand geometry Hand geometry refers to the geometric structure of the hand that is composed of the lengths of fingers, the widths of fingers, and the width of a palm, etc. The advantages of a hand geometry system are that it is a relatively simple method that can use low resolution images and provides high efficiency with great users’ acceptance [Gofarelli et al. 1997, Jain et al.

1999]. A brief survey of reported systems for hand-geometry verification can be found in [Golfarelli et al. 1997;Jain et al. 1999; Sanchez-Reillo et al. 2000; Pavesic et al. ]. An elaborate survey on hand geometry verification is given in [Dutan, 2009]. Geometrical features of the hand constitute the bulk of the hand features adopted in most of the hand recognition systems. Palmprint Palmprint is the region between the wrist and fingers. Palmprint features like ridges, singular points, minutia points, principal lines, wrinkles and texture can used for personal verification [Shu and Zhang, 1998].

There are two types of palmprint verification systems: high resolution and low resolution. High resolution system employs high resolution images, while low resolution system employs low resolution images. In high resolution images, ridges, singular points and minutia points are used as features. In low resolution images, it is principal lines, wrinkles and texture that are used as features. Palmprint verification techniques can be mainly divided into four categories: (1) line based [Zhang and Zhang, 2004;Han et al. 2003;Lin et al. 2005;Wu et al. 2004;Wu et al.

2006;Liu and Zhang, 2005;Liu et al. 2007]; (2) texture based [Zhan et al. 2003, Kong et al. 2006]; (3) orientation based [Kong and Zhang, 2006, Kong et al. 2006]; and (4) appearance based [Wu et al. 2005; Connie et al. 2005;Lu et al. 2003;Wu et al. 2003; Ribaric and Fratric 2005;HU et al. 2007;Yang et al. 2007]. Speaker /voice Speaker/voice verification combines physiological and behavioral factors to produce speech patterns that can be captured by speech processing technology. Inherent properties of the speaker like fundamental frequency, nasal tone, cadence, inflection etc.

are used for speech authentication. Speaker recognition systems are classified as text-dependent (fixed-text) and text- independent (free-text). The text- dependent systems generally perform better than text-independent systems because of the foreknowledge of what is said can be exploited to align speech signals into more discriminant classes. The text-dependent systems, however, require a user to repronounce some specified utterances, usually containing the same text as the training data. A survey of text-dependent verification techniques is given in [H’ebert, 2008].

Signature Handwritten signature is one of the first accepted civilian and forensic biometric verification technique in our society [Abuhaiba, 2007]. Human verification is normally very accurate in identifying genuine signatures. Signature verification systems use the distinctive behavioural features of a signature (such as speed, pressure and stroke order) to verify the identity of the user, as opposed to a simple physical crosscheck of one signature and another. Biometrics in Limited Use or Underdevelopment, or Still in the Research Realm Earshape

It is known that the shape of the ear and the structure of the cartilegenous tissue of the pinna are distinctive. Although a newcomer in the biometrics field, ears have long been used as a means of human identification in the forensic field. A small literature on ear biometrics is given in [Pun and Moon, 2004; Yan and Bowyer, 2005]. A recent survey on ear biometrics has been provided by [Hurley et al. 2008]. Although ear recognition is a relatively new topic, researchers have already come up with various approaches which drastically differ from each other in terms of acquisition, raw data interpretation and feature extraction. Knuckle crease

The image pattern formation from the finger-knuckle bending is highly unique and makes this surface a distinctive biometric identifier [Woodard and Flynn, 2005]. [Woodard and Flynn, 2005] were the first to exploit the use of finger knuckle surface in biometric systems. However, their work did not provide a practical solution in establishing an efficient system using the outer fingers. Brain/EEG Using elctroencephalogram (EEG) as a biometric is a new approach. Poulos et al, 1999 have proposed to model the EEG signal using autoregressive (AR) models and then using Kohonen’s Vector Quantizer (VQ) for the classification. Heart sound/ECG

The use the electrocardiogram (ECG) as a biometric has been found to give relatively high result for human recognition [Biel et al. 2001; Israel et al. 2005]. The differences between behavioural and physical biometrics The above biometric technologies fall in two categories: behavioural biometrics and physical biometrics. In general, behavioural biometrics can be defined as the non-biological or non- physiological features (or unique identifiers) as captured by a biometric system. As behavioural biometrics also covers any mannerisms or behaviour displayed by an individual, this category includes signature as well as keystroke recognition.

Physical biometrics may be defined as the biological and physiological features (or unique identifiers) as captured by a biometric system. This category includes fingerprint recognition, hand geometry recognition, facial recognition, iris and retinal recognition, and voice recognition. Chapter III METHODOLOGY This chapter presents the research design, respondents of the study, research data gathering procedures, and statistical tools used. Research Design The descriptive research design was used in this study.

According to David (2002), this particular research design describes the present existing conditions. It involves the collection of data in order to test the hypotheses or to answer questions concerning the current status of the respondents under study. This type of research design is used in order to provide descriptive information to the perceived effects of biometric system to teachers of the College of Business Administration at Capiz State University (Main Campus). Locale of the Study This study was conducted at Capiz State University – Main Campus, Roxas City, Capiz. Respondents of the Study

The respondents of the study were the 20 out of 47 teachers in the College of Business Administration at Capiz State University, Main Campus, and first semester of Academic Year 2013-2014, only those regular and emergency teachers. Table 1 displays the demographic profile of the respondents according to selected variables such as age, position, sex, and civil status. Statistics showed that most of them or 35 percent had ages between 30 years and below. 14 or 70 percent of the respondents coursed in this study had regular positions. The female respondents were predominant because the constituted 55 percent and 65 percent were married.

Table 1. Demographic profile of the respondents. Age Frequency Percent Valid Percent Cumulative Percent Valid 30 years old & below 7 35. 0 35. 0 35. 0 31-40 years old 6 30. 0 30. 0 65. 0 41-50 years old 5 25. 0 25. 0 90. 0 51 years old & above 2 10. 0 10. 0 100. 0 Total 20 100. 0 100. 0 Position Frequency Percent Valid Percent Cumulative Percent Valid Regular 14 70. 0 70. 0 70. 0 Emergency 6 30. 0 30. 0 100. 0 Total 20 100. 0 100. 0 Sex Frequency Percent Valid Percent Cumulative Percent Valid Male 9 45. 0 45. 0 45. 0 Female 11 55. 0 55. 0 100. 0 Total 20 100. 0 100. 0 Civil Status Frequency Percent

Valid Percent Cumulative Percent Valid Single 7 35. 0 35. 0 35. 0 Married 13 65. 0 65. 0 100. 0 Total 20 100. 0 100. 0 Sample Size The target population for this study was the regular and emergency teachers who uses a biometric system in the College of Business Administration at Capiz State University, Main Campus during the first semester of academic year 2013-2014, though purposive sampling method. Sampling Technique The sampling technique used in the study is purposive sampling technique. The researchers select a group of people who are available for the study to represent the desired number of respondents.

Research Instrument The instrument used in this study was a survey questionnaire consisting of two parts. Part one of the questionnaire includes the personal profile of the respondents such as name, age, sex, civil status and position. Part two is the checklist. This includes questions as to the perceived effects of biometric system to teachers. The questionnaire was subjected to face and content validity to our research coordinator. The reliability of the questionnaire was measured. The common measure of reliability is the Cronbach’s alpha and the usual criterion is a Cronbach’s alpha coefficient of .

70 (Harris & Ogbonna, 2001). A Cronbach’s alpha coefficient of . 70 and above indicates a high of internal consistency among the data collected. (Harris & Ogbonna; Hsu et al. , 2003). The Spearman Rank Correlation Coefficient was used to determine the reliability coefficient of one half of the questionnaire: (Downie, 1984) where: is the reliability coefficient of one-half of the questionnaire To get the reliability coefficient of the whole questionnaire, the Spearman Brown Prophecy formula was used: (Garrett, 1971) where: According to Milton Smith, a reliability coefficient of 0.

80 or more but not more than 1. 0 is necessary for the whole questionnaire to be reliable. The computed reliability coefficient of the whole questionnaire was 0. 93. Thus using Smith’s guideline, the questionnaire was deemed reliable and hence utilized in this study. The pilot test was done at the College of Industrial Technology of Capiz State University – Main Campus. Ten (10) teachers were selected to participate in the pilot test. A reliability coefficient of . 98 was found from the pilot test. It indicated the instrument possessed internal consistency in measuring the variables of interest.

Data Gathering Procedure After establishing the validity and reliability of the instrument, the said questionnaire was reproduced according to the number of respondents. The researchers personally distributed and administered the research instrument to ensure 100 percent rate retrieval. Each of the respondents was provided a copy of the questionnaire and those who cannot fully understand the questions were personally assisted. After retrieval, the classification, tabulation, analysis and interpretation of data were done systematically. Data Analysis Procedure

The data gathered were processes using the Statistical Package for Social Sciences (SPSS). Items in the questionnaire on perceived effects of biometric system to teachers were scored as follows. ScoreScoring Response Verbal IntervalCategory Interpretation 32. 34 – 3. 00 Agree Highly Favorable 2 1. 67 – 2. 33 Uncertain Favorable 11. 00 – 1. 66 Disagree Less Favorable Mean and frequency count was used to analyze the evaluation of the perceived effects of biometric system to taechers. Chi-square was used to determine the relationship between the respondents’ perceived effects when grouped according to their demographic profile.

Chapter IV Presentation, Analysis and Interpretation of Data The data gathered were presented, analyzed, and interpreted in this chapter. The figures in each table were preceded by a textual discussion. Perceived Effects of Biometric System to the Respondents Table 2 discloses that the grand mean of the perceived effects of biometric system to the respondents was 2. 08. The grand mean implies that the perceived effects of biometric system to the respondents was favorable. Furthermore, the means of the respondents on the perceived effects of biometric system ranged from 1. 45 to 2. 60. the highest mean of 2.

60 with a verbal interpretation of highly favorable was on statement, “great help in monitoring attendance”. It was followed by a mean of 2. 50 with verbal interpretation of highly favorable on statement, ‘keep me away from being late”. The lowest mean of 1. 45 with a verbal interpretation of less favorable was on statement, “insult my efficiency as a teacher”. Table 2. Perceived effects of biometric system to the respondents. Statement Mean Verbal Interpretation better than the manual attendance logging. 2. 40 Highly Favorable more convenient than the other Attendance System. 2. 25 Favorable ruins my daily routine.

1. 85 Favorable just a waste of time and money of the university. 1. 50 Less Favorable great help in monitoring attendance. 2. 60 Highly Favorable as the cause of delay in starting my class. 1. 60 Less Favorable keep me in attending my classes regularly. 2. 45 Highly Favorable violates my privacy. 1. 50 Less Favorable keep me away from being late. 2. 50 Highly Favorable it ensures school attendance. 2. 45 Highly Favorable could be a risk to my health due to radiation. 1. 90 Favorable makes me spend more time with my class. 2. 30 Favorable does not really help at all in checking the attendance of teacher.

1. 60 Less Favorable improved security standards of the school. 2. 40 Highly Favorable fast and easy attendance logging. 2. 30 Favorable simply annoys me due to catching up the time for logging in. 2. 15 Favorable as a stress contributor. 1. 95 Favorable makes me an efficient and effective teacher. 2. 20 Favorable insults my efficiency as a teacher. 1. 45 Less Favorable reduces fraud in logging attendance. 2. 25 Favorable Perceived Effects of Biometrics(Grand Mean) 2. 08 Favorable Perceived Effects of Biometric System to the Respondents When Grouped According to Demographic Profile

Table 3 shows the perceived effects of biometric system to the respondents when grouped according to selected variables such as age, position, sex, and civil status. For variables age: the means of the respondents according to age on the perceived effects of biometric system ranged from 1. 91 to 2. 18, all of which had verbal interpretation of favorable. The highest mean of 2. 18 was on 31-40 years old respondents. The lowest mean of 1. 91 was on 41-50 years old respondents. For position: the means of the respondents according to position on the perceived effects of biometric system ranged from 2.

05 to 2. 11, all of which had verbal interpretation of favorable. The highest mean of 2. 11 was on emergency position and the lowest mean of 2. 05 was on regular position. For sex: the means of the respondents according to sex on the perceived effects of biometric system ranged from 1. 98 to 2. 18, all of which had verbal interpretation of favorable. The highest mean of 2. 18 was on the male respondents and the lowest mean of 1. 98 was on the female respondents. For civil status: the means of the respondents according to civil status on the perceived effects of biometric system ranged from 2. 03 to 2.

13, all of which had verbal interpretation of favorable. The highest mean of 2. 13 was on single respondents while the lowest mean of 2. 03 was on the married respondents. Table 3. Perceived effects of biometric system to the respondents when grouped according to demographic profile. Variables Mean Verbal Interpretation Age: 30 years old & below 2. 11 Favorable 31-40 years old 2. 18 Favorable 41-50 years old 1. 91 Favorable 51 years old & above 2. 10 Favorable Grand Mean 2. 08 Favorable Position: Regular 2. 05 Favorable Emergency 2. 11 Favorable Grand Mean 2. 08 Favorable Sex: Male 2. 18 Favorable Female

1. 98 Favorable Grand Mean 2. 08 Favorable Civil Status: Single 2. 13 Favorable Married 2. 03 Favorable Grand Mean 2. 08 Favorable Differences in the Perceived Effects of Biometric System to the Respondents When Grouped According to Age Table 4 reveals that there was no significant differences in the perceived effects of biometric system to the respondents when grouped according to age. This was so, because the F-value of 1. 491 had a P-value of 0. 255 which was greater than 0. 05 alpha. This result implies that the respondents had the same perceived effects of biometric system regardless of their age.

Table 4. Difference in the perceived effects of biometric system to the respondents when grouped according to age. Source of Variation Sum of Squares df Mean Square F-value P-value Remarks Between Groups 0. 215 3 0. 072 Within Groups 0. 767 16 0. 048 1. 491 0. 255 n. s. Total 0. 982 19 Differences in the Perceived Effects of Biometric System to the Respondents When Grouped According to Position Table 5 discloses that there was a mean differences in the perceived effects of biometric system to the respondents when grouped according to position of 0. 06 in favor of the emergency respondents.

This mean differences was not significant because the P-value of the t-value of 0. 897 was on 0. 382 which has greater than 0. 05 alpha. The result implies that the regular respondents had the same perceived effects of biometric system with the emergency respondents. Table 5. Differences in the perceived effects of biometric system to the respondents when grouped according to position. Position N Mean Mean Difference F-value P-value Remarks Regular 14 2. 05 Emergency 6 2. 11 Differences in the Perceived Effects of Biometric System to the Respondents When Grouped According to Sex

Table 6 displays that the mean differences in the perceived effects of biometric system to the respondents when grouped according to sex was 0. 20 in favor of the male respondents. This mean differences was not significant because the t-value of 1. 847 had a P-value of 0. 081 which was greater than the 0. 05 alpha. The finding implies that the male respondents had the same perceived effects of biometric system with their female respondents. Table 6. Differences in the perceived effects of biometric system to the respondents when grouped according to sex. Sex N Mean Mean Difference F-value P-value Remarks Male 9 2. 18 Female

11 1. 98 Differences in the Perceived Effects of Biometric System to the Respondents When Grouped According to Civil Status The table 7 reflects that the mean differences in the perceived effects of biometric system to the respondents when grouped according to civil status was 0. 10 in favor of the single respondents. This mean differences was not significant because the P-value of the t-value of 1. 464 was 0. 160 which was greater than the 0. 05 alpha. This result implies that the single respondents had the same perceived effects of biometric system with the married respondents. Table 7. Differences in the perceived effects

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