1. LEADERSHIP STYLE ASSESSMENT TOOL
Atul Kumar Mishra 11PGDMHR14
Nicky Kumari 11PGDMHR33
Nirankar Singh Royal 11PGDMHR36
Swimmy Alasaka 11PGDMHR55
SUBMITTED TO DR. SNIGDHA RAI

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TABLE OF CONTENTS
LITERATURE REVIEW .......................................................................................................................................4
TESTS CONDUCTED SO FAR:.......................................................................................................................5
UNIQUE SELLING PROPOSITION OF OUR TEST: .........................................................................................6
CONCEPTUAL FRAMEWORK...........................................................................................................................7
USE OF TOOL ..................................................................................................................................................8
METHODOLOGY.............................................................................................................................................8
SAMPLE ..............................................................................................................................................................8
SIZE OF SAMPLE ........................................................................................................................................8
SAMPLE DESCRIPTION.............................................................................................................................8
PROCEDURE OF DATA COLLECTION ............................................................................................................9
DESCRIPTION OF TOOL.....................................................................................................................................9
ITEMS .................................................................................................................................................................9
RATING FORMAT USED..............................................................................................................................10
DATA ANALYSIS ..............................................................................................................................................10
ITEM ANALYSIS ...........................................................................................................................................10
RELIABILITY ANALYSIS ............................................................................................................................12
SPSS OUTPUT FOR CRONBACH'S ALPHA:..............................................................................................13
ITEM-TOTAL STATISTICS ......................................................................................................................13
FACTOR ANALYSIS ....................................................................................................................................18
REPORTING OF FINAL TEST/TOOL...............................................................................................................22
SAMPLE REPORT OF A RESPONDENT‟S SCORE....................................................................................23
LIMITATIONS ....................................................................................................................................................24
KEY LEARNINGS ..............................................................................................................................................24
ANNEXURE ........................................................................................................................................................25
REFERENCES.....................................................................................................................................................26

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INTRODUCTION
Corporate world has changed a lot in last few decades. Leaders of today need to be
continuously improving and well aware of their style. If you're looking for advancement in
your personal or professional life, there's hardly a way to accomplish this without possessing
leadership skills. The best executive is the one who has sense enough to pick good men to do
what he wants done, and self-restraint to keep from meddling with them while they do it.
Leadership skills are the vehicle through which you employ resources to realize a particular
outcome. So, even though you may have a vision or goal, if you don't have the skills to
realize it, the vision becomes useless.
Leadership skills require constant refinement. The key point is that leadership is not restricted
to the hierarchical structure of an organization (i.e. a company) but takes place also during a
sales call, a customer service response, a family decision or a meeting with some friends.
Consequently, leadership occurs in many forms and at all times. This is a fact that does not
depend on "having formal subordinates".

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LITERATURE REVIEW
The term Leadership and its definition have evolved over the time. There are as many
definitions of leadership as the number of people who have tried to define leadership. While
working on a leadership development assignment with Hay/McBer consulting firm, David
Goleman‟s team drew a sample of 3871 executives from a database of 20,000 executives. The
research found six leadership styles, each making use of different components of Emotional
Intelligence in different combinations.
The chronological root of Leadership theories in a timeline are as follows:
Late industrial revolution during 1870‟s
Great Man Theory believed that „Leaders had to be great people.‟
Personality Theories during 1900-30‟s and then came Trait theories.
These theories believed that certain qualities and traits make some people more suited
to leadership positions.
Behavioural theories during 1940‟s
These theories believed that any baby can be groomed and later take a leadership
position.
Contingency theories during 1950‟s
These theories believed that there should be a balance between environment,
leadership style and employee qualities.
Situational and participative theories during 1970‟s
Situational theory believed that the best course of action should be taken based on the
situation.
Participative theory believed that the best leadership style is one that takes input of
others into account.
Management theories during 1980‟s

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Management theories believed that clear chain of command, rewards & punishment
and Obeying Leader is primary.
Relationship theories during 1990‟s
Relationship theories believed that transformational leaders are passionate about the
work and are able to energize others.
TESTS CONDUCTED SO FAR:
To identify leadership style, Fiedler believes a key factor in leadership success is the
individual‟s basic leadership style. Fiedler created Least Preferred Coworker (LPC)
questionnaire for this purpose; to measure whether a person is task or relationship-oriented.
A stream of research has focused on differentiating transformational leaders from
transactional leaders. While transactional theories like Fiedler‟s model and path-goal theory
concentrates on guiding or motivating their followers in the direction of established goals by
clarifying role and task requirements, transformational leadership inspires followers to
transcend their own self-interests foe the good of organization. Major work in this direction
has been done at Ohio state university. Multifactor Leadership questionnaires (MLQ) are
widely accepted as appropriate tool to measure transformational, transactional and laissez-
faire leadership styles (Bass and Avolio, 1997).
Prior to this, during 1946-1956, Stogdill and Coons identified two leadership styles
dimensions namely Structure – Task orientation; and Consideration – People Orientation.
(Stogdill and Coons, 1957). Subsequently to questionnaires were developed to measure these
dimensions – Leader Behaviour Description Questionnaire (LBDQ) and Leader Opinion
Questionnaire (LOQ) (Stogdill and Coons, 1957).
Paul Hersey and Kenneth H. Blanchard developed a questionnaire having 12 items based on
Leader Adaptability and Style Inventory (LASI), an instrument developed at the center of
Leadership studies, Ohio University to assess the situational leadership. Hersey and
Blanchard (1974).
Similarly, Tansactional leadership questionnaire (TLQ) was developed to measure
transactional leadership styles (Alimo-Metcalfe and Alban-Metcalfe, 2001).
Organizational leadership assessment (OLA) instrument was used in 1999 to measure the
servant leadership by James Alan Laub of Florida Atlantic University. First the
chareacteristics of servant leadership were determined using Delphi technique and then these

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characteristics were used to construct the OLA. In this construct, nearly 80 items were
included and the test was conducted on 828 people from 41 organisations. (Laub,1999)
Similarly, servant leadership assessment instrument measures the seven concepts of
servant leadership theory founded by Patterson (2003). It consist of 42 items. The reliability
coefficient of the SLAI was found to be ranging from .89 to .92 (Dennis, 2004).
Considerable work has been done since mid-90s on emotional intelligence. Mayer-Salovey-
caruso Emotional Intelligence test (MSCEIT) has been developed to measure Mayer and
Salovey‟s model of EI. The test is modeled on ability based IQ test. The test contained 141
questions.
To assess Goleman‟s Emotional intelligence competencies, Emotional Competency
Inventory (ECI), 1999 and Emotional and Social Competency Inventory (ESCI), 2007
were created.
While work has been done to assess emotional intelligence in individual, little has been done
to assess the leadership style based on Goleman‟s Emotional Intelligence. In 2000, Goleman
introduced six leadership styles based on his theory of leadership styles.
There are limited number of tests developed/conducted based on Daniel Goleman‟s
emotional intelligence leadership style.
The tests that are previously conducted focus more on emotional intelligence rather than
finding the leadership styles based on emotional intelligence. The target respondents are
working professionals who do not get sufficient time to mould their leadership style
according to the situation. The tests available online include either too many questions or too
few questions to assess one‟s leadership style.
UNIQUE SELLING PROPOSITION OF OUR TEST:
The test is designed to suit the working professionals as well as MBA graduates, for
they are managers in the making and have sufficient time to mould their leadership
styles.
The process of data collection would be easier for the respondents who are MBA
graduates.
Questions are designed in a way as to avoid social desirability bias from respondents.

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CONCEPTUAL FRAMEWORK
As per David Goleman’s Emotional Leadership:
David Goleman proposed that there are six types of leadership style and every individual has
all the leadership styles but in varying degree. Each individual has one or more dominant
styles and the person modifies his/her leadership styles to suit the situation. The leadership
styles originally talked about by him is shown diagrammatically below:
David Goleman‟s Emotional Leadership styles Grid

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PRACTICAL UTILITY
USE OF TOOL
We have developed this tool to assess the leadership type of an individual. The main target
audience will be B-school students who are leaders in the making. Other people can also use
it. The leadership style is based on David Goleman‟s theory of emotional intelligence. This
basically states that leadership style of an individual can be altered a little for improvement.
But for this they first need to know what their current leadership style is. This tool will also
help them in choosing the right job opportunity where they can excel.
METHODOLOGY
SAMPLE
SIZE OF SAMPLE – sample size is 126.
SAMPLE DESCRIPTION - It consisted of B-school students. As we are developing the test
to identify the leadership skill of individual, we are focusing on leaders in making. The
graduates from different MBA colleges would be leading business tomorrow and it is very
important for them and also for the business that they know their way of leading people so
they can choose their task accordingly and develop on things which they are lacking and
polish their skills more.
SAMPLING TECHNIQUE - We used convenient random sampling. In this technique we collect
data from friends, family, colleagues etc. We choose this sampling technique because we not
had any particular sampling requirement. We needed data from current B school students.
Individual
• Person becomes aware of one‟s
leadership style.
• Person can change his/her
dominant style of leadership as
per the organization‟s demand.
• A person gets to know which
style shall work best for
him/her and at what time.
Organization
• Knowing the leadership style of
managers would help an
organization to ensure right fit
of people as per the roles.
• It shall help Organization have
a robust leadership pipeline
ready for future.

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PROCEDURE OF DATA COLLECTION
We floated an online questionnaire to collect data. We used different social mediums to
contact students. As we were trying to collect data from different B-schools across India, this
method was the most convenient one. Once we got data from them, we converted it in excel
sheet. We collected some demographic data also, so we needed to code it. We used this
format to code
Parameter Code
Male M
Female F
Age <20 1
Age 20-25 2
Age > 25 3
Work Experience < 1 1
Work Experience 1-3 2
Work Experience >3 3
Once we got the data converted into excel sheet, we loaded it in SPSS. For that we created
variable and labels accordingly.
The data collection process was not administered. We just asked our friends and colleagues to
fill it and distribute it further among their friends.
DESCRIPTION OF TOOL
ITEMS – Initially we started with six factors as proposed by Goleman in his theory of
emotional intelligence. There were six leadership style, two negative and four positive
factors. For each factor we made six items, but for one we made five. Other than these
factors, we also made one anchor question just to confirm our response.

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The leadership styles are the six factors.
Question Leadership Style
I frequently take control of things Commanding
I identify patterns in seemingly unconnected events Visionary
I give importance to people over task Affiliative
I seek opinion from others before taking a decision Democratic
I set examples and expect people to do work in the same way Pacesetting
I frequently provide alternative solutions for a problem to others Coaching
There were some items which were negatively coded.
RATING FORMAT USED
We used a four point Likert scale to rate the questions. The general practice is to use 5 point
or 7 point scale with an option of neutral, but we wanted our respondent to select at least one
option. So we chose this method to forced rating.
Option Rate
Strongly Agree 4
Agree 3
Disagree 2
Strongly Disagree 1
DATA ANALYSIS
ITEM ANALYSIS
It is detail analysis of items to determine whether a question is performing as per the intended
objective. It is of critical importance when interpreting item analysis to realize that there are
numerous reasons why an item may fail to meet the minimum threshold for consideration as
acceptable. Some of the reasons for an item failing to meet acceptable psychometric
thresholds are:

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 Flaws in the question:
o Translation problem
o Ambiguous wording
o Spelling or grammar errors that cause confusion
o Incorrect information in the question
 Flaws in the instruction of the material that the question covered
When examining a question in light of poor item analysis results, it is often one of the above
reasons that are responsible. To analyze each of the 35 items in our scale, we have analyzed
two parameters:
1. Mean should not be less than 2 or greater than 4
2. Standard Deviation should not be greater than 1
Using SPSS, we calculated the mean and standard deviation of each of the item and have not
taken items which do not satisfy the above mentioned criteria.
Below is the output of SPSS after doing item analysis on it.

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The items which do not satisfy the criteria have been circled in red.
After Item Analysis, items 22 and 31 are removed.
RELIABILITY ANALYSIS
Reliability: The measure of consistency of test. It should always measure the thing which it is
suppose to.
In statistical terms, reliability is based on the idea that individual items (or sets of items)
should produce results consistent with the overall questionnaire.
Cronbach's alpha is the most common measure of internal consistency ("reliability"). It is
most commonly used with multiple Likert questions in a survey/questionnaire that form a
scale. Our 35 items had 4-point forced rating scale. To find out the reliability, we have used
Cronbach's alpha.

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SPSS OUTPUT FOR CRONBACH'S ALPHA:
SPSS produces many different tables. The first important table is the Reliability Statistics
table that provides the actual value for Cronbach's alpha, as shown below:
Cronbach's alpha is 0.569, which indicates a moderate level of internal consistency for our
scale with this specific sample.
In the below table, we can observe that the final mean and deviation of all the items are 2.874
and .183, which are good.
Summary Item Statistics
Mean Minimum Maximum Range Maximum/
Minimum
Variance N of
Items
Item
Means
2.874 1.875 3.476 1.619 1.872 .183 35
ITEM-TOTAL STATISTICS
SPSS output shows the result of basic reliability analysis for the fear of computing subscale.
The values in the column labelled “Corrected Item-Total Correlation” are the correlations
between each item and the total score from the questionnaire. In a reliable scale all items
should correlate with the total. So, we are looking for items that don't correlate with the
overall score from the scale: if any of these values are less than about .2. This means that the
particular item does not correlate very well with the scale overall. Items with low correlations
have been dropped.
Reliability Statis tics
.569 .621 35
Cronbach's
Alpha
Cronbach's
Alpha Based
on
Standardized
Items N of Items

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The values in the column labelled “Alpha if Item is Deleted” are the values of the overall
alphas if that item isn't included in the calculation. As such, they reflect the change in
Cronbach's alpha that would be seen if a particular item were deleted. The overall value is
0.569, and so all values in this column should be around that same value. We have looked for
values of alpha greater than the overall alpha because if the deletion of an item increases
Cronbach's alpha then this means that the deletion of that item improves reliability.
The Item-Total Statistics table presents the “Cronbach's Alpha if Item Deleted” in the final
column, as shown below:
Item-Total Statistics
Scale Mean
if Item
Deleted
Scale
Variance if
Item
Deleted
Corrected
Item-Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if
Item
Deleted
A1 97.9683 35.119 .049 . .573
A2 97.1667 34.860 .154 . .561
A3 97.4365 33.832 .286 . .549
A4 97.9683 35.391 .015 . .578
A5 97.9444 35.445 .012 . .578
A6 97.2778 33.386 .364 . .542
A7 97.3175 32.778 .401 . .535
A8 97.9365 33.468 .234 . .551
A9 97.2698 34.263 .228 . .554
A10 98.3730 36.444 -.092 . .586
A11 97.7857 34.810 .065 . .572
A12 97.4127 33.444 .335 . .544
A13 98.5476 35.386 .012 . .578
A14 97.9365 35.260 .049 . .572
A15 97.5635 34.472 .187 . .558
A16 98.0952 35.031 .040 . .576
A17 97.4921 32.700 .429 . .533
A18 97.2778 34.362 .188 . .558
A19 97.1270 33.296 .357 . .541

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A20 97.3175 34.042 .246 . .552
A21 97.8810 34.410 .161 . .560
A22 98.7460 35.567 -.001 . .579
A23 97.4762 33.851 .290 . .549
A24 97.4365 33.544 .404 . .542
A25 97.8175 34.854 .117 . .565
A26 97.9524 35.950 -.054 . .589
A27 98.1270 34.080 .215 . .555
A28 97.5238 34.363 .201 . .557
A29 97.6746 34.509 .160 . .560
A30 97.2460 34.811 .093 . .568
A31 98.6349 38.362 -.311 . .610
A32 97.7222 33.722 .295 . .548
A33 97.3968 33.313 .339 . .542
A34 98.1429 35.691 -.016 . .581
A35 97.5159 34.140 .199 . .556
Items which have “Corrected Item-Total Correlation” of less than .2 have been marked in red.
We have not taken the items which have “Corrected Item-Total Correlation” of less than 2.
Items removed are: 1,2,4,5,10,11,13,14,15,16,18,21,22,25,26,29,30 and 34
After removing these items, we have done reliability analysis again. Below is the output:
As we can see Cronbach‟s alpha has increased from .569 to .645.
Reliability Statistics
.654 .669 17
Cronbach's
Alpha
Cronbach's
Alpha Based
on
Standardized
Items N of Items

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Summary Item Statistics
Mean
Minimu
m
Maximu
m Range
Maximu
m /
Minimum Variance
N of
Items
Item
Means
3.049 1.968 3.476 1.508 1.766 .138 17
Mean is still in range and variance has decreased, which is good.
Item-Total Statistics
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted
A3 48.6667 15.184 .338 .296 .629
A6 48.5079 15.020 .387 .379 .623
A7 48.5476 14.874 .362 .451 .625
A8 49.1667 15.068 .238 .211 .643
A9 48.5000 15.644 .241 .208 .641
A12 48.6429 15.447 .269 .240 .638
A17 48.7222 14.570 .447 .290 .614
A19 48.3571 15.255 .311 .245 .632
A20 48.5476 15.162 .331 .284 .630
A23 48.7063 15.601 .251 .280 .640
A27 49.3571 15.815 .162 .193 .652
A31 49.8651 18.102 -.253 .286 .708
A32 48.9524 15.358 .289 .323 .635
A33 48.6270 14.988 .353 .398 .626
A35 48.7460 15.631 .190 .225 .649
A28 48.7540 15.675 .220 .202 .644
A24 48.6667 15.440 .353 .499 .630
Few of the items still have “Corrected Item-Total Correlation” of less than 0.2. We are
removing them now as the increase in Cronbach's alpha.

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Items removed are: 27, 31 and 35
After removing these items, we have done reliability analysis again. Below is the output:
As we can see Cronbach‟s alpha has increased from .654 to .706
Mean
Minimu
m
Maximu
m Range
Maximu
m /
Minimum Variance
N of
Items
Item
Means
3.165 2.667 3.476 .810 1.304 .040 14
Mean is still in range and variance has decreased drastically, which is good.
Item -Total Statistics
41.1429 13.595 .324 .272 .690
40.9841 13.344 .397 .374 .681
41.0238 13.191 .374 .419 .683
41.6429 13.399 .241 .194 .704
40.9762 14.071 .217 .192 .702
41.1190 13.770 .272 .227 .696
41.1984 12.832 .478 .282 .669
40.8333 13.596 .313 .223 .691
41.0238 13.415 .354 .278 .686
41.1825 13.798 .283 .262 .694
41.4286 13.543 .327 .290 .689
41.1032 13.245 .378 .351 .682
41.2222 13.838 .214 .165 .704
41.1429 13.867 .330 .426 .690
A3
A6
A7
A8
A9
A12
A17
A19
A20
A23
A32
A33
A35
A24
Scale Mean if
Item Deleted
Scale
Variance if
Item Deleted
Corrected
Item-Total
Correlation
Squared
Multiple
Correlation
Cronbach's
Alpha if Item
Deleted

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We could see that now no items have “Corrected Item-Total Correlation” of less than 0.2.
And the Cronbach‟s alpha has also increases and shows strong degree of reliability, therefore
we are not removing anymore items now.
FACTOR ANALYSIS
Factor analysis is frequently used to develop questionnaire as we need to ensure that the
questions asked relate to the construct that we intend to measure.
Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of
correlations within a set of observed variables. Factor analysis is often used in data reduction
to identify a small number of factors that explain most of the variance observed in a much
larger number of manifest variables. Factor analysis can also be used to generate hypotheses
regarding causal mechanisms or to screen variables for subsequent analysis (for example, to
identify co linearity prior to performing a linear regression analysis).
Output 1: After doing factor analysis on our data, the first output we got was KMO and
Bartlett‟s Test.
The KMO statistic varies between 0 and 1. A value of 0 indicates that the sum of partial
correlations is large relative to the sum of correlations, indicating diffusion in the pattern of
correlations (hence, factor analysis is likely to be inappropriate). A value close to 1 indicates
that pattern of correlations are relatively compact and so factor analysis should yield distinct
and reliable factors. Kaiser (1974) recommends accepting values greater than 0.5 as
acceptable (values below this should lead to either collect more data or rethink which variable
to include). Furthermore, values between 0.5 and 0.7 are mediocre, values between 0.7 and
0.8 are good, values between 0.8 and 0.9 are great and values above 0.9 are superb. For our
data the value is 0.634, which falls into the Mediocre but still its higher than the accepted
level. So we are confident that factor analysis is appropriate for these data.

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Bartlett's measure tests the null hypothesis that the original correlation matrix is an identity
matrix. For factor analysis to work we need some relationships between variables and if the
R-matrix were an identity matrix then all correlation coefficients would be zero. Therefore,
we want this test to be significant (i.e. have a significance value of less than 0.05). A
significant test tells us that the R-matrix is not an identity matrix; therefore, there are some
relationships between the variables we hope to include in the analysis. For our data, Bartlett's
test is highly significant (.000), and therefore factor analysis is appropriate.
Output 2:
Com m unalitie s
1.000 .821
1.000 .551
1.000 .662
1.000 .749
1.000 .861
1.000 .654
1.000 .700
1.000 .585
1.000 .683
1.000 .728
1.000 .797
1.000 .718
1.000 .667
1.000 .578
1.000 .607
1.000 .666
1.000 .657
1.000 .622
1.000 .750
1.000 .707
1.000 .703
1.000 .545
1.000 .794
1.000 .767
1.000 .555
1.000 .568
1.000 .677
1.000 .583
1.000 .644
1.000 .732
1.000 .612
1.000 .791
1.000 .544
1.000 .717
1.000 .674
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
A12
A13
A14
A15
A16
A17
A18
A19
A20
A21
A22
A23
A24
A25
A26
A27
A28
A29
A30
A31
A32
A33
A34
A35
Initial Ex traction
Ex traction Method: Princ ipal Component Analy sis.

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The second output shows the table of communalities before and after extraction. Principal
component analysis works on the initial assumption that all variance is common; therefore;
before extraction the communalities are all 1. The communalities in the column labelled
extraction reflect the common variance in the data structure. After extraction some of the
factors are discarded and so some information is lost. The amount of variance in each
variable that can be explained by the retained factors is represented by the communalities
after extraction.
In our data, we can observe that the communality after extraction is greater than 0.5 in all the
items.
Output 3: Total variance explained
SPSS output 3 lists the eigenvalues associated with each linear component (factor) before
extraction, after extraction and after rotation. Before extraction, SPSS identified 14 linear
components within the data set. There should be as many eigenvectors as there are variables
and so there will be as many factors as variables. The eigenvalues associated with each factor
represent the variance explained by that particular linear component and SPSS also displays
the eigenvalue in terms of the percentage of variance explained (factor 1 explains % of total
variance). First few factors explain relatively large amounts of variance (especially factor 1)
whereas subsequent factors explain only small amounts of variance. SPSS then extracts all
factors with eigenvalues greater than 1, which leaves us with 5 factors. The eigenvalues

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associated with these factors are again displayed (and the percentage of variance explained)
in the columns labelled "Extraction Sums of Squared Loadings". The values in this part of the
table are the same as the values before extraction, except that the values for the discarded
factors are ignored. In the final part of the table labelled "Rotation Sums of Squared
Loadings), the eigenvalues of the factors after rotation are displayed. Rotation has the effect
of optimizing the factor structure and one consequence for the data is that the relative
importance of the seven factors is equalized. Before rotation Factor 1 accounted for
considerably more variance than the remaining four (21.6% compared to 10.56, 10.49, 9.52
and 8.26), however after extraction it accounts for only 12.67% of variance (compared to
12.10, 11.95, 11.90 and 11.80 respectively).
Output 4: Rotated component matrix
Now we will use rotated component matrix using 0.49 as cut-off point for factor loading for
naming the factors. In this way we get 5 factors which come under different leadership styles.
Based on factor loadings, item are grouped and factors are labeled as:
Factor 1 - Commanding
Factor 2 - Visionary
Factor 3 - Pacesetting
Factor 4 - Affiliative
Factor 5 - Coaching
Rotate d Co m p on e nt M atr ixa
.766
.762
.753
.670
.743
.598
.498
.539
.640
.572
.896
.644
.701
.670
A 3
A 6
A 7
A 8
A 9
A 12
A 17
A 19
A 20
A 23
A 24
A 32
A 33
A 35
1 2 3 4 5
Component
Ex traction Method: Princ ipal Component A nalys is .
Rotation Method: V arimax w ith Kaiser Normaliz ation.
Rotation converged in 9 iterations.a.

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REPORTING OF FINAL TEST/TOOL
Below are the questions which we got finally after applying item analysis, reliability analysis
and factor analysis under each construct.
Factor 1 - Commanding
I can face situations even when odds are against me.
No matter how difficult the situation is, I believe I can deal with it.
Factor 2 - Visionary
I seek and look for clear direction and plan of action before starting any task.
Before moving ahead I make sure that there is a consensus in the team.
Factor 3 - Pacesetting
I set examples before others by performance standards.
I am charged-up even when the circumstances are odd.
I give instant feedback to members while working in a team.
Factor 4 - Affiliative
I believe people give their best when they are involved in decision making process.
I appreciate member‟s effort while working in a team.
I relate to other‟s concern while interacting with them.
I do not like to impose my decision on others.
Factor 5 - Coaching
I encourage colleagues to accept task that challenges them to come out of their comfort zone.
I foresee the long-term impact of tasks at hand.
I see to it that the assignment is done well in time.

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SAMPLE REPORT OF A RESPONDENT‟S SCORE
Respondent’s detail-
Name-Sonal Arora
Gender- Female
Institute-IMI
Work experience- (1-3) yrs
A7 A24 Total Mean
Commanding 2 3 5 3.33
A9 A6 Total Mean
Visionary 4 4 8 4
A3 A8 A32 Total Mean
Pacesetting 3 8 2 13 4.33
A19 A20 A23 A35 Total Mean
Affiliative 4 3 4 4 15 3.75
A12 A17 A33 Total Mean
Coaching 3 3 2 8 2.67
Here we clearly see that the respondent has Affiliative style as her dominant style and
Pacesetting as her second dominant style where the total as well as the mean is on the higher
side. As per her response to anchor questions her dominant style is commanding and
Affiliative is the second dominant style.

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LIMITATIONS
 Online data collection affected the sincerity of the respondent.
 Number of responses was less than required. As a standard guideline, the number of
responses should be 4-5 times the number of items. In our case it was 126 which are
14 less than four times of item.
 Test completion time was not limited, as the response collection was online there was
no proper administration of test.
KEY LEARNINGS
 Importance of psychometric testing in HR domain, how to use it, what to do and more
importantly what not to do
 Item construction – what all factors should be kept in mind while constructing the
items
 Getting hands on experience and importance of item analysis and reliability in data
analysis
 Detailed understanding of factor analysis
 Importance of –
 Sample selection – how much important it is to select the right sample. In case
it is not done properly it can adversely affect the final tool
 Test administration – it is the most critical part while collecting data for the
tool. If the test environment is not proper or it is not administered properly, we
won‟t get the desirable result
 Rating scale – initially we faced some problem because we selected wrong
scale but after we corrected it our result got improved. So selecting proper
scale is also important

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ANNEXURE
QUESTIONNAIRE
Factor 1 – Commanding/Coercieve
I can face situations even when odds are against me.
No matter how difficult the situation is, I believe I can deal with it.
Factor 2 - visionary
I seek and look for clear direction and plan of action before starting any task.
Before moving ahead I make sure that there is a consensus in the team.
Factor 3 Pacesetting
I set examples before others by performance standards.
I am charged-up even when the circumstances are odd.
I give instant feedback to members while working in a team.
Factor 4 Affiliative
I believe people give their best when they are involved in decision making process.
I appreciate member‟s effort while working in a team.
I relate to other‟s concern while interacting with them.
I do not like to impose my decision on others.
Factor 5 Coaching
I encourage colleagues to accept task that challenges them to come out of their comfort zone.
I foresee the long-term impact of tasks at hand.
I see to it that the assignment is done well in time.

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