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CHAPTER III
METHODOLOGY
OVERVIEW
This chapter deals with the methodology of the present study. Here
the investigator has presented the method used for the study, population
and area, sample, description of tools, scoring procedure for data
collection and statistical techniques employed.
METHOD USED FOR THE STUDY
According to Best and Khan (1995), “Research is a systematic
activity that is directed towards discovery and the development of
organized body of knowledge”. In any given investigation it may not be
only desirable but also necessary to use one or more general types of
research methods in combination. The decision about the method or
methods to be employed however always depends upon the nature of the
problem and the kind of data necessary for its solution.
Since the problem selected for the present study is concerned with
“survey type” the researcher has adopted the “survey method” which
suggests the gathering of evidence relating to current conditions.
Survey Research is a method for collecting and analyzing data,
obtained from a large number of respondents representing a specific
population, collected through highly structured questionnaire or interview.
The survey is often the only means through which opinions, attitudes,
suggestions and other such data can be obtained. The survey method
gathers information and facts from a large number of cases. It is
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concerned with conditions or relationships that exist, practices that
prevails beliefs, points of view or attitudes that are held, processes that are
going on, effects that are being felt or trends that are developing.
Survey methods provide three types of information:
(i) of what exists by studying and analyzing the important aspects
of the present situation.
(ii) Of what we want by clarifying goals and objectives possibly
through a study of the conditions existing elsewhere or what
experts otherwise consider to be desirable and undesirable.
(iii) Of how to get these through discovering the possible means of
achieving the goals on the basis of the experiences of others or
opinion of the experts (Sidhu, 2002).
On the whole, survey method involves a clearly defined problem
and definite objectives; it requires imaginative planning, careful analysis
and interpretation of the data gathered and logical and skillful reporting of
the findings (John W. Best, 1992). Using this method, the investigator has
gathered information to measure the parental influence, emotional
intelligence and academic achievement of the higher secondary students.
AREA OF THE STUDY
The area of the study consists of two southern revenue districts of Tamil Nadu
namely, Tirunelveli and Thoothukudi. The map is attached herewith Map 3.1.
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POPULATION FOR THE STUDY
The population for the study includes all the higher secondary
students in government, aided and matriculation higher secondary schools
of the above mentioned southern districts. According to the government
norms, there are three educational districts centered at Tirunelveli,
Cheranmahadevi and Tenkasi in Tirunelveli revenue district and two
educational districts centered at Thoothukudi and Kovilpatti educational
districts in Thoothukudi revenue district. They are shown in the Map 3.2.
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193
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SAMPLE FOR THE STUDY
A sample is the representative part of the population. The
investigator had used stratified random sampling for selecting the sample.
The sample consists of 1080 students of higher secondary level. The
students are selected from 27 higher secondary schools, who have been
randomly selected from Tirunelveli, Cheranmahadevi, Tenkasi,
Thoothukudi and Kovilpatti educational districts. The stratification has
been done on the basis of sex, group of study, nature of schools, location
of schools, type of schools and community. The details regarding schools
covered and the number of students from each school is given in the
appendix F.
Table 3.1 Educational District wise distribution of the sample
S.No
.
Name of the educational
districts
School
s
Sample Percenta
ge
1 Tirunelveli 6 240 22.22
2 Cheranmahadevi 6 240 22.22
3 Tenkasi 6 240 22.22
4 Thoothukudi 6 240 22.22
5 Kovilpatti 3 120 11.11
Total 27 1080
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The following is the break up of the sample on the basis of sex, group of study,
medium of studey nature of schools, location of schools, type of schools and
community.
Table 3.2 Variable wise distribution of the sample
Variable Category N %
Sex Male 586 54.26
Female 494 45.74
Locality Rural 521 48.24
Urban 559 51.76
Medium of instruction Tamil 719 66.57
English 361 33.43
Group of study Science 411 38.6
Arts 368 34.07
Vocational 301 27.87
Type of school
Govt. 340 31.48
Aided 400 37.04
Matriculatio
n
340 31.48
Nature of school
Boys 127 11.76
Girls 231 21.39
Co-
Education
722 66.85
Community
SC/ST 218 20.19
MBC 201 18.61
BC 605 56.02
OC 56 5.19
It is inferred from the above table that the sample consists of
54.26% of boys and 45.74 % of girls, 66.57 % of the students are studying
in Tamil medium and 33.43% are studying in English medium, 48.24
percent of the students are residing at rural area and 51.76 percent of the
students are residing at city, 38.6 percent of the students are from science
group, 34.07 percent of them are arts group and 27.87 percent are
vocational group, 31.48 percent of the students are from government
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schools, 37.04 percent are from are aided and 31.48 percent are
matriculation schools, 11.76 percent of the students are from boys 21.39
percent are from girls and 66.85 percent are from co-education schools,
and 20.19 percent of the students are SC/ST, 18.61 percent are MBC,
56.02 percent are BC and 5.19 percent are OC.
TOOLS USED IN THE PRESENT STUDY
The investigator has constructed and validated the parental
influence scale with the help of his guide Sadananthan for his
investigation. The second tool was an adopted inventory of Thomas
Alexander. The investigator also prepared self identification proforma.
1. Samy and Satha Parental Influence Scale (SSPIS) (2009)
Parental Influence Scale
2. Adapted the Emotional Intelligence Inventory developed by
Thomas Alexander (2007)
DESCRIPTION OF THE TOOLS
1. PARENTAL INFLUENCE SCALE
The parental influence scale was designed by Lourdusamy and
Sadananthan and it was named as Samy & Satha Parental Influence Scale
(SSPIS). The scale attempts to find out the level of parental influence on
the students in four areas and also the over all parental influence.
Preparation of the draft tool
The investigator prepared the scale based on Likert type. The Likert Scale is a
popular format of questionnaire that is used in educational research. It was invented by
Rensis Likert, an educator and psychologist. The Likert Scale is an ordered, one-
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dimensional scale from which respondents choose one option that best aligns with
their view. There are typically between four and seven options. Five is very common.
All options usually have labels, although sometimes only a few are offered and the
others are implied. A common form is an assertion, with which the person may agree
or disagree to varying degrees. In scoring, numbers are usually assigned to each option
(such as 1 to 5).The format of a typical five-level Likert item is:
1. Strongly disagree, 2. Disagree, 3. Neither agree nor disagree,
4. Agree, 5. Strongly agree
The investigator read standard books and journals on family and
parental influence on academic achievement of children. Also he
consulted parents, experts and decided the different dimensions of
parental influence. The investigator showed it to the experts. On the
opinion of the experts and the order they arranged, the following
dimensions of parental influence scale were identified for the study. They
are namely, parenting, parent and school relationship, parent and academic
involvement and parent and multi skill development. Then for each
dimension twenty five statements were prepared and each statement has to
be answered in a fivepoint rating scale with the options namely highly
agree, agree, undecided, disagree and highly disagree.
Selection of Items
The tool was administered to 50 higher secondary students drawn
from Christhu Raja higher secondary school, St John’s higher secondary
school, Angelo matriculation school, St. Ignatius higher secondary school
and Kallanai - Corporation girls higher secondary school, Tirunelveli. For
refinement of the tool, item validity was calculated. The Parental
influence scale has four dimensions. Each dimension consists of 25 items.
By using product moment correlation, item-total correlation was found.
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The items which were having correlation value above 0.4 were retained
and other items were discarded. The item-total correlation is shown in the
following table 3.3.
Table 3.3
Item-total correlation value of SSPIS
Item No ‘r’ value Item No ‘r’ value Item No ‘r’ value Item No ‘r’ value
1* 0.405 21* 0.592 41* 0.658 61* 0.513
2* 0.425 22 0.393 42 0.291 62 0.312
3* 0.478 23* 0.825 43 0.353 63* 0.769
4* 0.455 24 0.311 44* 0.687 64* 0.658
5* 0.536 25* 0.543 45* 0.586 65 0.291
6 0.298 26* 0.644 46* 0.427 66 0.253
7 0.325 27 0.363 47 0.356 67 0.387
8* 0.489 28* 0.424 48* 0.654 68* 0.586
9* 0.476 29* 0.466 49 0.289 69* 0.527
10* 0.458 30 0.387 50* 0.458 70* 0.456
11* 0.467 31 0.344 51* 0.862 71* 0.654
12* 0.547 32* 0.687 52* 0.534 72* 0.789
13* 0.459 33* 0.518 53* 0.711
14* 0.543 34* 0.579 54* 0.844
15 0.297 35* 0.773 55* 0.464
16* 0.524 36* 0.802 56* 0.518
17 0.298 37* 0.559 57* 0.579
18 0.387 38* 0.513 58* 0.773
19* 0.874 39 0.312 59* 0.802
20* 0.896 40* 0.769 60 0.359
N.B. * marked items are valid items.
The valid items were retained. Finally the tool consisted of only 52
items.
Item Validity
For further improvement and refinement of the scale, the scale
consisting of
72 items, (the draft tool II) was administered again to 50 students of
higher secondary students in Christhu Raja higher secondary school, St
John’s higher secondary school, Angelo matriculation school, St. Ignatius
higher secondary school and Kallanai - Corporation girls’ higher
secondary school, Tirunelveli. The responses were scored. The total score
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for each student was calculated. The tool was arranged in decending order
of scores. The first 27% of the cases were considered as upper group and
the lowest 27% of the cases were considered as lower group. That is 14
students were in the upper group and 14 students were in the lower group.
The mean and standard deviation for the upper and the lower groups were
computed. The ‘t’ test (small sample) was used for calculating ‘t’ value
for each item. For 27 df at 5% level of significance the table value of ‘t’ is
2.05. The items which were having less than 2.05 were discorded and the
items which were having greater than 2.05 were retained. Thus the tool
has 52 items. The ‘t’ value of the item was given in the table 3.4.
Table 3.4 ‘t’ value for the items in the tool SSPIS
Item No ‘t’ value Item No ‘t’ value Item No ‘t’ value Item No ‘t’ value
1* 4.465 16* 3.496 31* 2.689 46* 2.772
2* 4.425 17* 3.828 32* 2.583 47* 2.655
3* 3.478 18* 3.545 33* 3.525 48* 3.584
4* 2.455 19* 2.648 34* 2.650 49* 2.525
5* 3.536 20* 3.523 35* 3.456 50* 3.458
6* 3.499 21* 3.469 36* 2.868 51* 2.652
7* 2.476 22* 3.689 37* 3.436 52* 2.787
8* 3.458 23* 3.417 38* 2.713
9* 2.467 24* 2.577 39* 2.846
10* 3.447 25* 2.776 40* 2.536
11* 2.461 26* 2.804 41* 3.520
12* 3.444 27* 3.560 42* 2.576
13* 3.522 28* 3.411 43* 2.771
14* 3.880 29* 2.770 44* 2.806
15* 2.895 30* 2.657 45* 3.514
N.B. * marked items are valid items. The valid items were retained.
Finally the tool consisted of only 52 items.
Establishing Validity
Validity refers to the appropriateness of the interpretation of the
results of a test or evaluation instrument for a given group of individuals
and not to the instrument itself (Norman E Gronlund and Robert L. Linos
1990). It is known that every test is constructed within a purpose, i.e to
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provide measures of a defined variable. Then it is said that the test is
valid. The investigator established the content validity.
Content Validity
The draft tool I (SSPIS) which consisted of 100 items, was given to
five experts in St. Xavier’s College of Education (Autonomous),
Palayamkottai for their comments and suggestions. Some irrelevant items
were deleted and some items were reformulated and refined on the basis
of the suggestions given by them. The tool was also given to the guide for
his comments. Some modifications were made in the statements on the
basis of his comments. Thus the draft tool II (SSPIS) has 72 statements.
Thus the content validity of the tool was established.
Establishing Reliability
The investigator used test – retest method to establish reliability of
the final tool that consists of 52 items. For establishment of reliability the
investigator collected the required data from a total of 50 students of
higher secondary students of Christhu Raja higher secondary school, St
John’s higher secondary school, Angelo matriculation school, St. Ignatius
higher secondary school and Kallanai - Corporation girls’ higher
secondary school, Tirunelveli. After an interval of 15 days the same tool
was administered to the same set of students. The reliability coefficient
was found as 0.80 which is highly reliable. Thus, the reliability of the tool
was established. It is given in the following table:
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Table 3.5 Dimension – wise reliability coefficient - SSPIS
No Dimension Reliability
coefficient
1 Parenting 0.79
2 Parent and school relationship 0.80
3 Parent and academic involvement 0.81
4 Parent and multi skill development 0.78
5 Parental Influence 0.80
Table 3.6 Description of Final Tool of SSPIS
No Dimensions Item numbers
1 Parenting 1 – 13
2 Parent and school relationship 14 – 26
3 Parent and academic
involvement
27 – 39
4 Parent and multi skill
development
40 – 52
The highest possible score is 260 and lowest score is 52.
Table 3.7 Scoring Key
Responses Scores
Highly Agree 5
Agree 4
Undecided 3
Disagree 2
Highly disagree 1
2. EMOTIONAL INTELLIGENCE INVENTORY
The emotional intelligence inventory was designed by Thomas
Alexander (2007). It was adopted by the investigator. The inventory
consists of 80 items. The inventory attempts to find out the emotional
intelligence of the students in four areas and also the over all emotional
intelligence.
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Table 3.8 Item distribution – emotional intelligence
Dimensions Item numbers
Self-awareness 1 – 17
Self-management 18 – 40
Social awareness 41 – 52
Relationship management 53 – 80
The above table shows that 80 items are equally distributed on the
four areas namely self-awareness, self-management, social awareness, and
relationship management. The author has already established the
reliability and validity of the tool. The author established the concurrent
validity of the tool. The concurrent validity is 0.81.
Table 3.9 Scoring key for emotional intelligence
Responses Scores
Highly Agree 5
Agree 4
Undecided 3
Disagree 2
Highly disagree 1
DATA COLLECTION
Data were collected with the help of the research tools outlined in
this chapter. The investigator has met the heads of the schools, got
permission and administered the tools to 1080 higher secondary students
belonging to these schools, forming the sample. The tools were
administered to the students with the help of the respective class teachers.
These tools were administered to the higher secondary students by the
investigator in one sitting. The investigator administered the tools only in
Tirunelveli Thoothukudi revenue districts. He administered the tools all
over these districts from August 2009 to July 2010. The students have
been given the duration of the time limits for answering the items of the
tools as given in table 3.10.
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Table 3.10 Duration of the administration of the tools
Tools Duration
Parental influence 20 mins
Emotional intelligence 35 mins
Total duration of the tools 55 mins
The total time taken for all the tools is fifty five minutes.
SCORING OF THE RESPONSES
The responses of the students on all the tools were scored according
to the weightage assigned by the tool constructors. The incomplete or
partially answered items were left out. The scores obtained were kept as a
raw score. Academic achievement score was the marks obtained by XI
standard students in the Govt. public examination 2009–2010 for various
subjects. The marks were collected from the school records. The
percentage of the total marks was taken as the score for the academic
achievement.
TABULATION OF THE RESPONSES ON THE DATA SHEETS
The scores obtained on each of the tool and academic achievement
of every respondent were entered in printed data sheet. The data were fed
into the computer and stored in the compact disk. The collected data were
analysed with the help of the SPSS statistical package.
STATISTICAL TECHNIQUES USED
The major statistical techniques used were
1. Mean and Standard Deviation
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2. Fixing levels and scoring out the cases with the help of T-scores
3. Critical ratio test (t-test)
4. Analysis of variance (ANOVA)
5. Chi-square test
6. Pearson’s product moment correlation
7. Regression and
8. Factor Analysis
The above techniques have been elaborated in the following pages.
PERCENTAGE ANALYSIS
It is used for computing the level of parental influence and
emotional intelligence of the respondents.
After calculating Mean and Standard Deviation, the raw scores
were converted in to T- scores with the help of the formula.
Where T = T- score
X= Raw score
M= Mean
S= Standard Deviation
S
M][X1050T
−+=
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Table 3.11 The levels are fixed as follows
Fixation Below 40 40 to 60 Above 60
Scores Mean-1SD< Mean Mean+1SD>
Levels Low Moderate High
THE ‘t’ TEST
The ‘t’- test is used to find out the significant difference between
the means of different variables for different subgroups.
Where M1= the mean for the first group
M2= the mean of the second group
S1= the standard deviation for the first group
S2= the standard deviation for the second group
N1= the size of the first group
N2= the size of the second group
Here the investigator has fixed 5% level of significance to test the
hypothesis.
ANOVA
Analysis of variance has been used to find out the significant
difference among SC/ST, MBC, BC and OC higher secondary students,
the difference among boys, girls and co-education school higher
secondary students and the difference among aided, government and
matriculation school higher secondary students, in their parental influence
and emotional intelligence.
)/NS/N(S
MMt
22
212
1
21
+
−=
206
SCHEFFE INTERVAL
Scheffe is the most conservative of all post hoc tests. It has more
power when making complex comparisions. It is appropriate to use
Scheffe test only when making post hoc complex comparisons. (eg more
than K-1). If the F ratio is significant then Scheffe test is used to find
which group is more significant. It is calculated by
I Scheffe = [(K-1)*(Fα)*(MSE)*(1/n1+1/n2)]
1/2
Where K - No of groups
Fα – Critical value of F at 5% level (table value)
MSE = MSW - Mean scores within groups
n1,n2 – No of samples within / between groups
CHI-SQUARE ANALYSIS
The Chi-square analysis is employed to test the association between
the variables father’s education, mother’s education, father’s occupation,
mother’s occupation and rank in the family of the higher secondary
students and their parental influence and emotional intelligence.
Where O= the observed frequency
E = the expected frequency
E
E)Σ[(O 22 −
=χ
Variance between the groups F
Variance within the groups=
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For testing the hypothesis, the investigator has the 5% level of
significance. For finding the degrees of freedom the investigator has used
the following formula.
df= (r-1) (c-1)
Where r= number of rows, c= number of columns
CORRELATION ANALYSIS
Pearson’s product moment correlation is used for determining the
reliability, validity, coefficients and also for ascertaining the relationship
among the dependent variables. This is employed to find out the
relationship between parental influence and emotional intelligence.
Where
r = Product moment correlation coefficient
x,y = Variables studied
N = Number of paired observations.
The investigator has fixed 5% level of significance to test the
hypothesis.
Pearson r is used for estimating the extent of relation exiting among
the different variables taken in pairs for all the different groups. Garrett
(1969) presents the following classification for interpreting the various
values of r, which is adopted for the study.
( )))(/())((
))((r
2222yyNxxN
yxxyN
Σ−ΣΣ−Σ
ΣΣ−Σ=
208
r from 0.00 to + 0.20denotes indifferent or negligible correlation.
r from + 0.20 to + 0.40denotes low correlation.
r from + 0.20 to + 0.70denotes substantial correlation.
r from + 0.70 to + 1 denotes high to very high correlation.
The correlation is interpreted only after the statistical significance
of coefficient of correlation is considered from the tables. In the present
study it is seen the if “r” exceeds 0.161 it is significant at 5%level, if “r”
below 0.161 the correlation is not statistically significant (Garrett 1969)
MULTIPLE CORRELATIONS
Multiple correlations are used for estimating the inter-correlations
among independent variables as well as to the correlations with the
dependent variable. The co-efficient of multiple correlation indicates the
strength of relationship between one variable (independent variable) and
two or more others (dependent variables) taken together. This is employed
to find out the influence of parental influence and emotional intelligence
on academic achievement.
FACTOR ANALYSIS
Harman (1960) defines the procedure of factor analysis as follows:
The principal concern of factor analysis is the resolution of a set of
variables linear by in terms of a smaller number of categories or “factors”.
This resolution can be accomplished by the analysis of the correlation
among the variables. A satisfactory solution will yield factors, which
convey all the essential information of the original set of variables. Thus,
the chief aim is to attain scientific parsimony or economy of description”.
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Guilford (1956) outlines the different steps in factor analytic study in the
following term:
a. Select an appropriate domain for investigation.
b. Develop a hypothesis concerning the factor.
c. Select or construct suitable tests.
d. Select a suitable population.
e. Obtain a sample of adequate axes.
f. Extract factors with commonalities in the diagonal cells or the
correlation matrix.
g. Rotate the reference axes.
h. Interpret the rotated factors.
The present investigation made use of principal-axes method, as it
is one of the satisfactory procedures of factor analysis. Fruchter (1954)
explains the superiority of this method in the following terms.
The principal-axes method of factoring the correlation matrix is of
interest of several reasons. Each factor extracts the maximum amount of
variance, (i.e., the sum of squares of factor loadings is maximized on each
factor) and gives the smallest possible residuals. The correlation matrix is
condensed into the smallest number of orthogonal factors by this method.
The method also has an advantage of giving mathematically unique (least
square) solution for a given table of correlations. Harman (1960) points
out that this method needs larger number of computations. But this
difficulty is overcome with the help of high-speed computers.
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TEST OF SIGNICICANCE OF EXTRTACTED FACTORS
The test of significance is applied to the obtained factors and only
those, which are significant, are retained for final interpretation.
INTERPRETATION OF FACTORS: PRINCIPLES AND
CRITERIA
a. Locate the group of variables on which the factor has the highest
loadings.
b. Locate the group of variables on which the factor has the lowest
loadings.
c. Examine the possibility of different factors becoming independent.
d. Treat factor loading whose absolute values are greater than 0.30as
significant and neglect others as not significant
The degree of presence of each variable is a factor determined as follows:
a. Factor loading above 0.900 - extremely high presence of the
variable.
b. Factor loading above 0.700 to 0.900 - very high presence of the
variable.
c. Factor loading above 0.550 to 0.700 - considerable presence of the
variable.
d. Factor loading above 0.450 to 0.550 - variable somewhat present.
e. Factor loading above 0.300 to 0.450 - variable present but low.
f. Factor loading below 0.300 - variable not present.
Analysis and discussion of the results follow in the next chapter.
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FIXING THE LEVEL FOR TESTING HYPOTHESIS
The investigator fixed 5% level of significance for testing the
hypothesis. If the calculated value is greater than the table value, the null
hypothesis is rejected. If the calculated value is less than the table value,
the null hypothesis is accepted.