1. To Study the Perception RegardingTo Study the Perception Regarding
Good Leader in the CommunityGood Leader in the Community
Presented by,
Leader’s group
2. Good Leader Characteristics
by Karlene Sugarman, M.A.
"Leadership is like gravity. You know it's there, you know it exists, but how do you define it?"
Former San Francisco 49er Tight End, Dr. Jamie Williams
Great leaders come in many forms. In one sense solid leadership is a subjective thing,
in another there are certain characteristics that are, by consensus, typical of quality leadership.
Leaders can either be task-oriented or person-oriented.
1) Task-oriented leaders are most interested in training, instructing behavior, performance and winning
2) Person-oriented leaders are more interested in the interpersonal relationships on the team.
“Great leaders in sports are both task- and people-oriented, but lean more towards being task-oriented.”
Leaders must possess the qualities they are trying to incorporate into their team. For example, if you
want members to be confident, have self-control, be disciplined, etc., then you must first possess all
these traits. One of the most powerful things you can do is lead by example. Vince Lombardi says,
"Leaders are made, they are not born; and they are made just like anything else has every been made
in this country - by hard work" (Dowling, 1970, p. 179).
Outstanding leaders make decisions based on facts, and apply common sense and simplicity to
complex tasks. You must select the right strategy for the right situation, even when the pressure is
overwhelming.
Successful leaders are not only highly driven and intrinsically motivated but also foster that
same enthusiasm in their associates.
Great leaders take calculated risks and are innovative and confident in their decisions to do so. They
realize that being timid will not get them where they want to go. This confidence and assertiveness
will usually trickle down to the team members. The quality and effectiveness of a great leader will
often show itself by way of the team's effort as a whole.
3. Introduction:Introduction:
Factor analysis is a method of data reduction. It does this by seeking underlying
unobservable variables that are reflected in the observed variables.
There are many different methods that can be used to conduct a factor analysis
such as principal axis factor, maximum likelihood, generalized least squares,
unweighted least squares.
Given the number of factor analytic techniques and options, it is not surprising
that different analysts could reach very different results analyzing the same data
set. However, all analysts are looking for simple structure.
Simple structure is pattern of results such that each variable loads highly onto
one and only one factor.
Factor analysis is a technique that requires a large sample size.
Factor analysis is based on the correlation matrix of the variables involved, and
correlations usually need a large sample size before they stabilize.
4. Assumption of Factor AnalysisAssumption of Factor Analysis
Factor analysis is designed for interval data, although it can
also be used for ordinal data (e.g. scores assigned to Likert
scales).
The variables used in factor analysis should be linearly
related to each other.
This can be checked by looking at scatterplots of pairs of
variables.
Obviously the variables must also be at least moderately
correlated to each other, otherwise the number of factors
will be almost the same as the number of original variables,
which means that carrying out a factor analysis would be
pointless.
5. Research PurposeResearch Purpose
“The Purpose of this study are to explore the Good leader Characteristics in
community in terms of vision, ethics, reality and courage, time punctuality,
Good character & personality, responsible nature etc. “
“To compare these Characteristics, to determine which of these Characteristics
relate to Good leader effectiveness in community .”
“To construct a hypothesized model of Good leader Characteristics
in the perspective of community.”
8. KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
.854
Bartlett's Test of
Sphericity
Approx. Chi-Square 702.978
df 136
Sig. .000
Interpretation :-
It is a measure of sampling adequacy. It should be >=0.5.
If it is not >=0.5 then we can not run factor analysis
because sample size is not sufficient for running factor analysis.
Conclusion:-
From the above table we can say that value of KMO test is 0.854.
It means our sample size is sufficient for running factor analysis which is
near to 0.5 and p-value of Bartlett’s test is .000
That means it is significant and we reject our H0
it means at least some of
variable highly correlated.
Testing of Hypothesis:-
H0
: There is correlation between different factors under consideration is
identify matrix.
H1
: There is no-correlation between different factors under consideration is
identify matrix.
9. Communalities
Interpretation:
From the above table we can compare the extraction form std. variables with
over all extraction from original variables.
Here initial column shows the standardize value of variables .
It means 0 & std. deviation is 1.
And extraction shows the over all variability extracted by all the factors for
each variable.
From the above table we can say that variable calculator extract more
variability by all the factors compare to all other variables.
Its very near to std. value.
11. Interpretation:-
Here above table total column shows us eigenvalue.
Eigenvalue should be >1 because our standard variability is 1 and by comparing
that value with eigenvalue first 4 factors extract more variability ,
And other factors extract less variability. so we can choose only 4 factors.
% of variance column shows variability extracted in % by each factor.
Cumulative % column shows cumulative % of extracted variability.
Conclusion:-
The leftmost section of this table shows the variance explained by
the initial solution.
Only four factors in the initial solution have eigenvalues greater than 1.
Together, they account for almost 61% of the variability in the original variables.
The second section of this table shows the variance explained by
the extracted factors before rotation.
The cumulative variability explained by these four factors in the extracted
solution is about 61%.
The rightmost section of this table shows the variance explained by the extracted
factors after rotation.
The rotated factor model makes some small adjustments to factors 1,2,3 but
factor 4 is left virtually unchanged.
Look for changes between the unrotated and rotated factor matrices to see
how the rotation affects the interpretation of the first, second & third factors.
13. Rotated Component Matrix(a):-
# The rotated component matrix determine what the component represent.
1) The first component is most highly correlated with good vision.
2) The second component is most highly correlated with good communication skill
3) The third component is most highly correlated with good character
as good personality.
4) The four component is most highly correlated with responsible nature.
# So we can focus on good vision, good communication skill,
good character as well as good personality, responsible nature.
Conclusion:-
“We can say that above mention four characteristics must have in good leader”
14. Naming the Factors: (Surrogated Variables)
1) We obtain the factor loading in below table.
2) Once, we fill that the variables can be groups mathematically and logically
under various factors, we group them.
3) Then we give the logical names to those groups of variables.
4) The group of variables and their factors which are named logically
as displayed in table.
15. Factors Name of factors (Surrogated Variables) Loadings
Factors 1 Positive characteristics
Q_13_4 He/She must have responsible nature. 0.851416
Q_13_1 He/she must have good vision. 0.811688
Q_13_2 He/She must have good communication skill. 0.808207
Q_13_3 He/She must be having good character as well as good personality. 0.662443
Factors 2 spontaneous decision characteristics
Q_13_8 He/She must spend time with the subordinates whenever problem is occurring. 0.732691
Q_13_14 He/She must have fearless attitude towards political pressure. 0.718728
Q_13_17 He/She must belive in reality not in dreams. 0.638698
Factors 3 negative characteristics
Q_13_13 He/She must not be punctual. 0.757541
Q_13_5 He/She must not be honest. 0.747584
Q_13_7 He/She must not have monitoring skill. 0.709122
Factors 4 spiritual characteristics
Q_13_6 should come "Who can I enlist to help" and "What can do?" 0.729919
Q_13_11 He/She must not think about them 0.540485
Q_13_16 He /She must have aggressive nature 0.420693
Table_1 - Factors and variables
16. The variables which are now combined and brought under respective groups
can be used for further studies.
The named factors will be now as surrogated variables.
Surrogated variables are nothing but a common and alternative name for
all variables combined under one group.
The surrogated variables now become the representative of entire group of
variable that fall under it.
We got four factors and so four surrogated variables.
It is important to note that these factors are uncorrelated with each other
as compared to original correlated variable.
Factors
Name of factors (Surrogated
Variables)
Factors 1 Positive characteristics
Factors 2 spontaneous decision characteristics
Factors 3 negative characteristics
Factors 4 spiritual characteristics
Table-2 Surrogated Variables
17. Final conclusion of analysis:-
Initially we show that 17 variables under study were highly correlated and so
we took the decision to group them through factor analysis.
The results of factors analysis are quite fruitful and we have derived four factors
and hence four surrogated variables.
1) In table The factors deals with ‘Positive characteristics .
In other words variable that can share common information related to
number of respondent,
2) second factor deals with number of spontaneous decision characteristics.
3) third factor deals with some of the negative characteristics .
4) four factor deals with some of the spiritual characteristics.
