CHAPTER 7 DESCRIPTIVE ANALYSIS -...

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CHAPTER 7

DESCRIPTIVE ANALYSIS

7.0 Chapter Overview

This chapter presents a descriptive analysis of the data obtained through data

collection instruments. The data were analyzed descriptively in terms of measures of

central tendency and measures of variability. A measure of central tendency includes

the mean, median and mode. A measure of variability includes standard deviation,

skewness and kurtosis. Descriptive analysis of data is necessary as it helps to

determine the normality of the distribution. The nature of the statistical technique to

be applied for inferential analysis of the data depends on the characteristics of the

data.

7.1 Introduction

Research consists of systematic observation and description of the characteristics or

properties of objects or events for the purpose of discovering relationships between

variables. The ultimate purpose is to develop generalizations that may be used to

explain phenomena and to predict future occurrences. To conduct research, principles

must be established so that the observation and description have a commonly

understood meaning. Measurement is the most precise and universally accepted

process of description, assigning quantitative values to the properties of objects and

events.(Best, 1981).

Planning and care in research design and data collection provides a substantial

guarantee of quality in research but the ultimate test lies in the analysis (Best J. W.,

1981). Data in the real world often comes with a large quantum and in a variety of

formats that any meaningful interpretation of data cannot be achieved straightway. In

order to achieve the objectives of the study, analysis of the data collected forms an

important and integral part. Analysis means categorizing, classifying and summarizing

data to obtain answers to the research questions. Classification also helps to reduce the

vast data into intelligible and interpretable forms (Youngman, 1979).

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In order to do statistical analysis, two types of data are recognized these are

1. Parametric data: Data of this type are measured data, and parametric statistical

tests assume that the data are normally or nearly normally distributed.

Parametric tests are applied to both interval and ratio scaled data.

2. Non Parametric data: data of this type are either counted or ranked non

parametric tests, sometimes known as distribution free tests, do not rest upon

the more stringent assumption of normally distributed populations

Two types of statistical application are used for generalization. These are descriptive

statistical analysis and inferential statistical analysis. The present chapter discusses the

descriptive data analysis used by the researcher for her study.

7.2 Descriptive Data Analysis

Descriptive analysis of data limits generalization to a particular group of individuals

observed. No conclusions extend beyond this group and any similarity to those outside

the group cannot be assumed. The data describe one group and that group only. Much

simple action research involves descriptive analysis and provides valuable information

about the nature of the particular group of individuals (Best & Kahn, 2003).

The descriptive analysis of data provides the following:

The first estimates and summaries, arranged in tables and graphs, to

meet the objectives.

Information about the variability or uncertainty in the data

Indications of unexpected patterns and observations that need to be

considered when doing formal analysis

Descriptive analysis is used to describe the basic features of the data in the study.

They provide simple summaries about the sample and the measures. Together with

simple graphical analysis, they form the basic virtual of any quantitative analysis of

data. With descriptive analysis, one simply describes what is or what the data shows.

Description of data is needed to determine the normality of the distribution,

description of the data is necessary as the nature of the techniques to be applied for

inferential analysis of the data depends on the characteristics of the data

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7.3 Procedure of Descriptive Analysis

Once the data are grouped, different statistical measures are used to analyze data and

draw conclusions. For the present study, the following statistical measures of

descriptive analysis were used to compute further statistical testing.

1. Measures of Central tendency.

2. Measures of Variability.

3. Measures of Divergence from Normality.

4. Measures of Probability.

Graphical methods have been adopted for translating numerical facts into more

concrete and understandable form.

7.3.1 Measures of central tendency

The central tendency of a distribution is an estimate of the “center” of a distribution

value. There are three major types of measures of central tendency

Mean

The Mean or average is probably the most commonly used methods of describing a

central tendency. The mean represents the center of gravity of distribution. Each score

in a distribution contributes to the determination of mean. It is also known as

arithmetic average. Mean is the average of all values in a distribution (Krishnaswamy

& Ranganathan, 2006).

To compute the mean, all the values are added and divided by the total number of

values. It is the ratio of summation of all scores to the total numbers of scores. Using

mean one can compare different groups. It also helps in computing further statistics.

Since this method involves handling of large numbers and entails tedious calculations,

the researcher used data analysis tools available in a simple Microsoft® office suite,

Excel 2007 to calculate the mean. The mode of function is Formulas/More

functions/Statistical/ Average.

The mean is calculated as:

AVERAGE (number1, number2…)

Where,

Average= mean (number1, number2…) = range of scores

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Mean can also be calculated using the formula:-

x =∑

Where, x = sample mean

∑ fx = sum of scores in a distribution

N = number of items

Median

The median is the positional average that divides a distribution into two equal parts so

that one half of items falls above it and the other half below it.

In other words, the midpoint of a distribution of values is called the median. It is the

point, below and above which 50% of the population lies. The Median is the score

found in the exact middle of the set of values. One way to compute the median is to

list all scores in numerical order, and then locate the scores in the center of the sample.

If there is an even number of numbers in the set, then the median calculates the

average of the two numbers in the middle.

Median = ⌈

⌉

Where,

l = lower limit of median class.

N = number of scores in a series.

fm = frequency of median class

c = length of class interval

F= no, of cases below the median.

The researcher used data analysis tools available in the simple Microsoft® office

suite, Excel 2007 to calculate the median. The mode of function is Formulas/More

functions/Statistical/ Median.

Mode

The mode is the most frequently occurring value in the set of scores. The mode is

indirectly calculated mean and median. It is a quick and appropriate measure of

central tendency.

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The mode can be calculated as the largest frequency in the distribution, using the

following formula:

Mode = 3 (median) – 2 (mean)


suite, Excel 2007 to calculate the mode. The mode of function is Formulas/More

functions/Statistical/ Mode.

7.3.2. Measures of variability

The measures of central tendency indicate the central value of the distribution.

However, the central value alone is not sufficient to fully describe the distribution.

(Kaul, 2007).

In addition to the measures of centrality, we require a measure of the spread of the

actual scores. The extent of such spread may vary from one distribution to another.

The extent of such variability is measured by the measures of variability.

Variability describes the way the classes are distributed and how they are changing in

relation to a variety. For example, Range and Standard Deviation. The technique

employed in the present study is Standard Deviation. The range is simply the highest

value minus the lowest value. The standard deviation is more accurate and detail

measure of dispersion.

Standard Deviation

The standard deviation shows the relation that set of scores has with the mean of the

sample. Standard deviation is expressed as the positive square root of the sum of the

squared deviations from the mean divided by the number of scores minus one. It is the

average difference between observed values and the mean. The standard deviation is

used when expressing dispersion in the same unit as the original measurement. It is

designated as (σ)

The standard deviation can be calculated using the following formula:

σ = i√Σfx2-c

2

N

Where, σ = Standard Deviation (S.D.)

i = length of class interval

Σ = sum of

x2= squares of the deviations of scores from the assumed mean

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f = frequency of class interval

c2 = square of correction

N = total number of scores


suite, Excel 2007 to calculate the S.D. The mode of function is Formulas/More

functions/Statistical/ STDEV.

7.3.3. Measure of Divergence from Normality

An important aspect of the “description” of a variable is the shape of its distribution,

which tells the frequency of values from different range of variables. A researcher is

interested in how well the distribution can be approximated from the normal

distribution. Simple description statistic can provide some information relevant to this

issue. The two measures used to determine the shape of distributions are skewness and

kurtosis.

Skewness: Many times it is seen that the mean, median and mode of the distribution

don’t fall at the same place, i.e. the scores may extend much farther in one direction

than the other. Such a distribution is called a skewed distribution.

Positively skewed distribution: The distribution is positively skewed when most of the

scores pile up at the low end (or left) of the distribution and spreads out more

gradually towards the high end of it. In a positively skewed distribution, the mean falls

on the right side of the median.

Negatively skewed distribution: The distribution is negatively skewed if the scores are

concentrated towards the upper value and it is positively skewed if they cluster

towards lower value. The mean of the distribution is higher than the median in

positive skewness whereas the median value is greater than the mean in negative

skewness.

Skewness = Mean - Mode

SD

For the present study skewness was calculated using Microsoft Excel 2007.The mode

of function is Formulas/More functions/Statistical/ SKEW.

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Kurtosis

The term “Kurtosis “refers to “peakedness ” or the flatness of a frequency distribution

as compared with the normal. A frequency distribution more peaked than the normal

is said to be Leptokurtic and a frequency distribution flatter than the normal is called

Platykurtic. A normal curve is also termed as Mesokurtic.

Positive kurtosis indicates a relatively peaked distribution leptokurtic and negative

kurtosis indicates a relatively flat distribution, which is platykurtic.


suite, Excel 2007 to calculate the Kurtosis. The mode of function is Formulas/More

functions/Statistical/ KURT.

7.3.4 Measures of Probability (fiduciary limits)

In order to estimate the population mean or the probable variability, it is necessary to

set up limits for a given degree of confidence which will embrace the mean or the

standard deviation since limits define the confidence interval.

Estimation of Population parameters :-( Fiduciary Limits)

The limits of the confidence intervals of parameters are called fiduciary limits. They

are calculated for both mean and standard deviation at 0.95 and 0.99 levels of

confidence.

The formula used for calculating standard error of mean and fiduciary limits is:-

S.EM. = σ

√N

At 0.95 level; mean +S. EM × 1.96

At 0.99 level; mean +S. EM × 2.58

The formula used for calculating standard error of S.D.:-

S.ED = 0 .71σ

√N

At 0.95 level; S.D.+ S. ED × 1.96

At 0.99 level; S.D.+ S. ED × 2.58

Where,

S. EM = standard error of mean

S. ED = standard error of standard deviation

σ= standard deviation

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N= total number of scores

M= Mean

7.4 Graphical Representation

Aid in analyzing numerical data may often be obtained from a graphic or pictorial

treatment of the frequency distribution. The advertisements have long used graphic

methods because these devices catch the eye and hold the attention when the most

careful array of statistical evidence fails to attract notice for this and other reasons the

research worker also utilizes the attention- getting power of visual presentation; and at

the same time, seeks to translate numerical facts often abstract and difficult to

interpret, into more concrete and understandable form. In the present study, the

researcher used graphical representation in the form of line diagrams and pie-charts.

7.5 Descriptive Statistical Analysis of data

The data were obtained for the variables involved in the study from Bachelor of

Education students of different B Ed colleges. The study was conducted in two

phases; hence this chapter deals with the description of the variables in the two phases

of the study.

Phase I: This section deals with the description of the following variables:

1. Information Literacy Skills of students from Arts Faculty

2. Information Literacy Skills of students from Science Faculty

3. Information Literacy Skills of students from Commerce Faculty

4. Information Literacy Skills of students with Graduate degree

5. Information Literacy Skills of students with Post Graduate degree

Phase II: This section deals with the description of the following variables:

1. Information Literacy Skills pre-test scores of control group

2. Information Literacy Skills post-test scores of control group

3. Information Literacy skills pre-test scores of experimental group

4. Information Literacy skills post-test scores of experimental group

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7.5.1: Descriptive Statistics of Information Literacy Skills of students from Arts

Faculty

Table 7.1 Descriptive Statistics of Information Literacy Skills among Student

Teachers from Arts Faculty

Faculty Total

sample

Mean Median Mode Standard

Deviation

Skewness Kurtosis

Arts 182 13.79 14 14.42 4 -0.1575 0.033

As evident from the Table 7.1 the value of mean, median, mode are 13.79, 14, and

14.42 respectively. The mode is higher than the mean and median. This indicates that

the distribution is negatively skewed indicating high scores. Further the difference

between mean, median mode is marginal indicating that the distribution is near

normal. Hence it can be calculated that the selected sample is a representative of the

population. The kurtosis of the sample is indicating that the distribution is leptokurtic

in nature indicating peaked distribution.

7.5.1.1 Estimation of population parameters

Table 7.2 SE and FL of Mean and Standard Deviation of the distribution of

information literacy skills among the students of arts faculty

Faculty Sample

size(N)

S.E of mean S.E of SD

S.EM = 0.296 S.ED = 0.210

Fiduciary limit at Fiduciary limit at

0.95 0.99 0.95 0.99

Arts 182 14.37

to 13.21

14.63 to

12.94

4.4116 to

3.584

4.5418 to 3.45

The standard error mean is 0.296.

The fiduciary limit is at 0.95 is 14.37 to 13.21which indicates that out of 100, 95

times the population mean will lie between the ranges 14.37 to 13.21

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The fiduciary limit of 0.99 is 14.63 to 12.94which indicates that out of 100, 99 times

the population mean will lie between the ranges 14.63 to 12.94

The standard error deviation is 0.210

The fiduciary limit of 4.4116 to 3.584 which indicates that out of 100, 95 times the

population standard deviation will lie between the ranges 4.4116 to 3.584


the population standard deviation will lay between 4.5418 to 3.45.

Table 7.3 Distribution of Original and Smoothed Frequencies of Information

Literacy skills of students from Arts faculty is presented graphically in figure 7.1

Class interval Original frequencies Smoothened frequencies

0-5 2 14.333

6- 10 37 66.333

11-15 82 135.666

16-20 50 135.666

20-25 11 61

203

Figure 7.1 Frequency Polygons of the Original and the Smoothened Frequencies

of Information literacy skills of students from Arts Faculty

0

20

40

60

80

100

120

140

160

0-5 6-10 11-15 16-20 21-25

Fre

qu

enci

es

Class Intervals

Frequency polygons of the original and the smoothened

frequencies of information literacy skills of students from

arts faculty

Original Frequencies Smoothened Frequencies

204

7.5.2: Descriptive Statistics of Information Literacy Skills of students from

Commerce Faculty

Table 7.4 Descriptive Statistics of Information literacy skills among student

teachers from Commerce Faculty

Faculty Total

sample


Deviation

Skewness Kurtosis

Commerce 107 14.29 14 13.42 3.92 -0.02 -0.677

As evident from the table 7.4 the value of mean, median, mode are 14.29, 14, and

13.42 respectively. This indicates that the distribution is negatively skewed. Further

the difference between mean, median mode is marginal indicating that the distribution

is near normal. Hence it can be calculated that the selected sample is a representative

of the population. The kurtosis of the sample is indicating that the distribution is

platykurtic in nature.


TABLE 7.5 SE and FL of mean and Standard Deviation of the distribution of

information literacy skills among the students of Commerce Faculty

Faculty Sample

size (N)


S.EM = 0.3868 S.ED = 0.2746


0.95 0.99 0.95 0.99

Commerce 107 14.758 to

13.2418

14.9979 to

13.0021

4.5382 to

3.4618

4.7084 to

3.2916

The standard error mean is 0.3868.

The fiduciary limit is at 0.95 is 14.758 to 13.2418 which indicates that out of 100, 95


The fiduciary limit of 0.99 is 14.9979 to 13.0021which indicates that out of 100, 99


205


The fiduciary limit of 4.5382 to 3.4618which indicates that out of 100, 95 times the




Table 7.6 Distribution of Original and Smoothened frequencies of Information

literacy skills of students from Commerce Faculty is presented graphically in

Figure 7.2


0-5 0 7

6-10 21 36

11-15 45 78

16-20 36 82.66667

21-25 5 41

206


of Information literacy skills of students from Commerce Faculty

0

10

20

30

40

50

60

70

80

90

0-5 6-10 11-15 16-20 21-25

Fre

qu

enci

es

Class Intervals



commerce faculty


207

7.5.3 Descriptive Statistics of Information Literacy Skills of students from

Science Faculty

Table 7.7 Descriptive statistics of Information literacy skills among Student

teachers from Science Faculty

Faculty Total

sample


deviation

Skewness Kurtosis

Science 97 15.44 16 16 4.033 0.06861 -0.23877

As evident from the table 7.7 the value of mean, median, mode are 15.44, 16, 16

respectively. As the scores are gradually increasing this indicates that the distribution

is positively skewed. Further the difference between mean, median mode is marginal

indicating that the distribution is near normal. Hence it can be calculated that the

selected sample is a representative of the population. The kurtosis of the sample is

indicating that the distribution is platykurtic in nature


Table 7.8 SE and FL limit of mean and Standard Deviation of the distribution of

Information literacy skills among the students of Science Faculty

Faculty Sample

size(N)


S.EM = 0.4095 S.ED = 0.29076*,


0.95 0.99 0.95 0.99

Science 97 16.24 to

14.637

16.49651 to

14.38349

6.283 to

1.783

4.7812 to

3.2848

The standard error mean is 0.4095



The fiduciary limit of 0.99 is 16.49651 to 14.38349which indicates that out of 100, 99


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the population standard deviation will lay between 4.7812 to 3.2848

Table 7.9 Distribution of Original and Smoothened Frequencies of Information

literacy skills of students from Science Faculty is presented graphically in

Figure 7.3


0-5 0 3.666

6- 10 11 22.333

11-15 34 58.666

16-20 41 78.666

20-25 11 52

26-30 0 11

209


of Information literacy skills of students from Science Faculty

0

10

20

30

40

50

60

70

80

90

0-5 6-10 11-15 16-20 21-25 26-30

Fre

qu

enci

es

Class intervals



science faculty


210

7.5.4: Standard Error and Fiduciary Limit of Information Literacy skills among

the students of different faculty


Information literacy skills among the Students of Different Faculty

Faculty Sample

size(N)


S.EM = 0.296*,0.4095**,

0.3868***

S.ED =

0.210*,0.29076**,

0.2746***


0.95 0.99 0.95 0.99

Arts* 182 14.37 to

13.21

14.63 to

12.94

4.4116 to

3.584

4.541 to 3.45

Science** 97 16.24 to

14.637

16.49651 to

14.383

6.283 to

1.783

4.781 to

3.284

Commerce*** 107 14.758 to

13.2418

14.9979 to

13.002

4.538to

3.461

4.708 to

3.291

211

7.5.5 : Descriptive Statistics of Information Literacy Skills of students with

graduate degree

Table 7.11 Descriptive statistics of Information literacy skills among Student

teachers with Graduate Degree

Faculty Total

sample


deviation

Skewness Kurtosis

Graduate 237 14.131 14 13 4.0677 0.0985 -0.0698

As evident from the table 7.11 the value of mean, median, mode are 14.131, 14, 13

respectively. This indicates that the distribution is positively skewed. Further the

difference between mean, median mode is marginal indicating that the distribution is

near normal. Hence it can be calculated that the selected sample is a representative of

the population. The kurtosis of the sample is indicating that the distribution is




Information literacy skills among the students teachers with Graduate Degree

Faculty Sample

size(N)


S.EM = 0.2642 S.ED = 0.1876


0.95 0.99 0.95 0.99

Graduate 237 14.64 to

13.62

14.80 to 13.46 4.43 to 3.70 4.55 to 3.58


The fiduciary limit is at 0.95 is 14.64 to 13.62 which indicates that out of 100, 95 times


212

The fiduciary limit of 0.99 is 14.80 to 13.46 which indicates that out of 100, 99 times



The fiduciary limit of 4.43 to 3.70 which indicates that out of 100, 95 times the


The fiduciary limit of 0.99 is 44.55 to 3.58 which indicates that out of 100, 99 times the

population standard deviation will lay between 4.55 to 3.58.


literacy skills of student teachers with Graduate degree is presented graphically

in figure 7.4

CLASS INTERVAL ORIGINAL

FREQUENCIES

SMOOTHENED

FREQUENCIES

0-5 2 16.333

6- 10 43 78.333

11-15 100 169

16-20 78 182.666

20-25 14 30.666

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of Information literacy skills of students with Graduate degree

0

20

40

60

80

100

120

140

160

180

200

0-5 6-10 11-15 16-20 20-25

Fre

qu

enci

es

Class Intervals


frequencies of information literacy skills of students with

graduate degree

ORIGINAL FREQUENCIES SMOOTHENED FREQUENCIES

214

7.5.6 : Descriptive Statistics of Information Literacy Skills of students with

graduate degree

Table 7.14 Descriptive statistics of Information literacy skills among student

teachers with Post Graduate degree

Faculty Total

sample


Deviation

Skewness Kurtosis

Post

Graduate

149 14.69 15 15.62 4.25 -0.00813 -0.44271

As evident from the table 7.14 the value of mean, median, mode are 14.69, 15, 15.62

respectively. This indicates that the distribution is slightly negatively skewed. Further

the difference between mean, median mode is marginal indicating that the distribution

is near normal. Hence it can be calculated that the selected sample is a representative

of the population. The kurtosis of the sample is indicating that the distribution is



Table 7.15 SE and FL Mean and Standard Deviation of the distribution of

Information literacy skills among the students’ teachers with Post Graduate

Degree

Faculty Sample

size(N)


S.EM = 0.34822 S.ED = 0.2472


0.95 0.99 0.95 0.99

Post Graduate 149 15.37 to 14 15.58 to

13.792

4.73 to 3.76 4.88 to

3.612


The fiduciary limit is at 0.95 is 15.37 to 14which indicates that out of 100, 95 times the

population mean will lie between the ranges 15.37 to 14

215






The fiduciary limit of 0.99 is 4.88 to 3.612which indicates that out of 100, 99 times the

population standard deviation will lay between 4.88 to 3.612


literacy skills of students with Post Graduate Degree is presented graphically in

Figure 7.4


0-5 0 8.666

6-10 26 46.333

11-15 61 103.333

16-20 49 114.333

21-25 13 62

26-30 0 13

216


of Information literacy skills of students with Post Graduate Degree

0

20

40

60

80

100

120

140

0-5 6-10 11-15 16-20 21-25 26-30

Fre

qu

enci

es

Class Intervals


frequencies of information literacy skills of students with post

graduate degree


217

7.5.7.: Standard Error and Fiduciary Limit of Information Literacy skills among

the students of different faculty


Information literacy skills among the Students of different Faculty

Faculty Sample

size(N)


S.EM =

0.2642*,0.34822**,

S.ED =

0.1876*,0.2472**,


0.95 0.99 0.95 0.99

Graduate* 237 14.64 to

13.62

14.80 to

13.46

4.43 to

3.70

4.55 to 3.58

Post graduate** 149 15.37 to

14

15.58 to

13.792

4.73 to

3.76

4.88 to 3.612

218

Phase II: This section deals with the descriptive analysis of the following dependent

variables:

1. Information Literacy Skills pre-test scores of control group

2. Information Literacy Skills post-test scores of control group

3. Information Literacy skills pre-test scores of experimental group

4. Information Literacy skills post-test scores of experimental group

7.5.8.: Descriptive Statistics of Information Literacy Skills pre-test scores of

control group

Table 7.18 Descriptive statistics of Information literacy skills Pre-test scores of

Control Group

Group Total

sample


deviation

Skewness Kurtosis

Pre test

Control

46 13.239 12.5 12 2.368 0.5096 -0.88

As evident from the table 7.18 value of mean, median, mode are 13.239, 12.5, 12

respectively. The mean is higher than mode and median. This indicates that the

distribution is positively skewed. Further the difference between mean, median mode

is marginal indicating that the distribution is near normal. Hence it can be calculated

that the selected sample is a representative of the population. The kurtosis of the

sample is indicating that the distribution is platykurtic in nature.

219



Information literacy skills Pre-test scores among the students teachers in Control

Group

Group Sample size

(N)


S.EM = 0.34822 S.ED = 0.2472


0.95 0.99 0.95 0.99

Pre-test

scores of

Control

46 13.923

TO

12.554

14.1396

TO

12.338

2.8538

TO

1.882

3.0075

TO

1.728



times the population mean will lie between the ranges 13.923 to 12.55.

The fiduciary limit of 0.99 is 14.1396 to 12.338 which indicate that out of 100, 99



The fiduciary limit of 0.95 is 2.8538 to 1.8822 which indicates that out of 100, 95

times the population standard deviation will lie between the ranges 2.8538 to 1.8822.


times the population standard deviation will lay between 3.0075 to 1.7285.

220


literacy skills among the Student Teachers in Control Group is presented

graphically in Figure 7.6


0-5 0 1.666

6-10 5 15

11-15 30 38.666

16-20 11 41

21-25 0 11

221


of Information literacy Pre-test Scores among the student teachers in Control

Group

0

5

10

15

20

25

30

35

40

45

0-5 6-10 11-15 16-20 21-25

Fre

qu

enci

es

Class intervals


frequencies of information literacy pre-test scores among the

student teachers in control group


222

7.5.9 Descriptive Statistics of Information Literacy Skills post-test scores of

control group

Table 7.21 Descriptive statistics of Information literacy skills Post-test scores of

Control Group

Group Total

sample


deviation

Skewness Kurtosis

Post test

Control

46 12.978 12 12 2.185 0.603 -0.44

As evident from the table 7.12 value of mean, median, mode are 12.97, 12, 12

respectively. The mean is higher than mode and median. This indicates that the

distribution is positively skewed. Further the difference between mean, median mode

is marginal indicating that the distribution is near normal. Hence it can be calculated

that the selected sample is a representative of the population. The kurtosis of the

sample is indicating that the distribution is platykurtic in nature.

223



Information literacy skills Post-test scores among the student teachers in Control

Group

Group Sample

size (N)


S.EM = 0.3222 S.ED = 0.2288


0.95 0.99 0.95 0.99

POST TEST

SCORES OF

CONTROL

46 13.601

TO

12.339

13.801

TO

12.338

2.634

TO

1.737

2.776

TO

1.595








times the population standard deviation will lie between the ranges 2.6341 to 1.7373.



224


Literacy skills among the student teachers in Control Group is presented



0-5 0 2

6-10 6 17

11-15 33 41.33333

16-20 7 40

21-25 0 7

225


of Information literacy skills Post-test scores among the students teachers in

Control Group

0

10

20

30

40

50

60

70

80

0 5 6 10 11 15 16-20 21- 25

Fre

qu

enci

es

Class intervals


frequencies of information literacy skills post test scores

among the students teachers in control group


226

7.5.10 Descriptive Statistics of Information Literacy Skills pre-test scores of

Experimental group

Table 7.24 Descriptive statistics of Information literacy skills Pre-test scores of

Experimental Group

Group Total

sample

Mean Median Mode Standard Skewness Kurtosis

Pre test

Experimental

65 13.661 14 19.67 3.894 -0.044 -0.7590

As evident from the table 7.24 value of mean, median, mode are 13.661, 14, and 19.67

respectively. This indicates that the distribution is negatively skewed. Hence it can be

calculated that the selected sample is a representative of the population. The kurtosis

of the sample is indicating that the distribution is platykurtic in nature.



Information literacy skills Pre-test scores among the students teachers in

Experimental Group

Group Sample

size (N)


S.EM = 0.4830 S.ED = 0.34284


0.95 0.99 0.95 0.99

Pre test scores of

Experimental

65 14.6077

TO

12.714

14.907

TO

12.414

4.5659

TO

3.222

4.778

TO

3.01




227

The fiduciary limit of 0.99 is 14.907 to 12.414 which indicate that out of 100, 99 times

the population mean will lie between the ranges 14.907 to 12.414.



times the population standard deviation will lie between the ranges 4.5659 to 3.2221




literacy skills among the student teachers in Experimental group is presented



0-5 0 5.666

6-10 17 25.333

11-15 25 48.666

16-20 20 46

21-25 3 23

228


of Information literacy skills Pre-test scores among the students teachers in

Experimental Group

0

10

20

30

40

50

60

0-5 6-10 11-15 16-20 21-25

Fre

qu

enci

es

Class Intervals

Frequency polygons of the original and the smoothened frequencies

of Information literacy skills pre test scores among the students

teachers in experimental group

ORIGINALFREQUENCIES SMOOTHENED FREQUENCIES

229

7.5.10. Descriptive Statistics of Information Literacy Skills post-test scores of

Experimental group

Table 7.27 Descriptive Statistics of Information literacy skills Post-test scores of

Experimental Group

Group Total

sample

Mean Median Mode SD Skewness Kurtosis

Post test

Experimental

65 17.538 18 18.924 4.534 -0.002 1.082

As evident from the table 7.27 value of mean, median, mode are 17.5384, 18, 18.924

respectively. This indicates that the distribution is negatively skewed. Further the

difference between mean, median mode is marginal indicating that the distribution is

near normal. Hence it can be calculated that the selected sample is a representative of

the population. The kurtosis of the sample is indicating that the distribution is

leptokurtic in nature.



Information literacy skills Post-test scores among the student teachers in

Experimental Group

Group Sample

size(N)


S.EM = 0.562 S.ED = 0.3992


0.95 0.99 0.95 0.99

Post test scores of

Experimental

65 18.6356

To

16.4404

18.9828

To

16.0932

5.3166

TO

3.7514

5.5639

TO

3.505

230








times the population standard deviation will lie between the ranges 5.3166 to 3.7514

The fiduciary limit of 0.99 is 5.5639 to 3.505 which indicates that out of 100; 99 times



literacy skills among the student teachers in Experimental Group is presented

graphically in figure 7.9


1-10 6 20.333

11-20 43 54

21-30 15 58.333

31-40 1 16

231


of Information literacy skills Post-test scores among the students teachers in

Experimental Group

7.6 Summary

The results of the descriptive analysis have been tabulated and graphically presented.

The chapter has discussed the descriptive statistics to support the distribution of every

variable in response to the objectives of the study. This is also essential to further test

the hypothesis through inferential statistical techniques. The following chapter deals

with the testing of hypothesis.

0

10

20

30

40

50

60

70

1-10 11-20 21-30 31-40

Fre

qu

ence

is

Class Intervals


frequencies of information literacy skills post test scores among

the students teachers in experimental group


CHAPTER 7 DESCRIPTIVE ANALYSIS -...

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