lecture 7 rmt
TRANSCRIPT
-
7/31/2019 Lecture 7 RMT
1/56
BBA 04
Bahria University
-
7/31/2019 Lecture 7 RMT
2/56
Hypotheses Development Definition of Hypotheses: Is a logical relationship
between two or more variables expressed in the form
of a testable statement.
2
-
7/31/2019 Lecture 7 RMT
3/56
Statement of Hypotheses: Formats If-Then Statements
Can be used to test whether there are differences
between two groups. It takes two forms: Employees who are more healthy will take sick
leave less frequently.
Ifemployees are more healthy, then they will takesick leave less frequently.
3
-
7/31/2019 Lecture 7 RMT
4/56
Directional and Nondirectional Hypotheses
Directional hypotheses: the direction of the
relationship between the variables (positive/negative)
is indicated.
The greater the stress experienced in the job, the lowerthe job satisfaction of employees.
Or
Women are more motivated than men are.
4
-
7/31/2019 Lecture 7 RMT
5/56
Nondirectional hypotheses Non directional hypotheses: there are no indication of
the direction of the relationships between variables.
There is a relationship between age and Job
satisfaction.
Or There is a difference between the work ethic values of
American and Arabian employees.
5
-
7/31/2019 Lecture 7 RMT
6/56
Null and Alternate Hypotheses The null hypotheses is a proposition that states a
definitive, exact relationship between two variables.
It states that the population correlation between two
variables is equal to zero (or some definite number).
In general, the null statement is expressed as no
(significant) difference between two groups.
6
-
7/31/2019 Lecture 7 RMT
7/56
The Alternate Hypotheses The alternate hypotheses is the opposite of the null
hypotheses, is a statement expressing a relationship
between two variables or indicating differences
between groups.
7
-
7/31/2019 Lecture 7 RMT
8/56
Examples The null hypotheses:
Women are more motivated than men are. Then,
H0: M = w
Or H0: M - w = 0
Where H0 represents the null hypotheses,
M is the mean motivational level of the men,
w is the mean motivational level of women.
8
-
7/31/2019 Lecture 7 RMT
9/56
The alternate hypotheses for the above example:
HA: M < w
Which is the same asHA: M > w
Where HArepresents the alternate hypotheses.
9
-
7/31/2019 Lecture 7 RMT
10/56
Examples for the nondirectional
relationship There is a difference between the work ethic of
American and Arabian employees.
The null hypotheses would be:
Ho: AM = AR
Or
Ho: AM - AR= 0
Where AM is the mean work ethic value ofAmericans and ARis the mean work ethic value ofArabs.
10
-
7/31/2019 Lecture 7 RMT
11/56
Examples for the nondirectional
relationship The alternate hypotheses for the above example
would statistically be set as:
HA: AM
ARwhere HArepresents the alternate hypotheses.
11
-
7/31/2019 Lecture 7 RMT
12/56
Examples for the nondirectional
relationship For the example: The greater the stress experienced in the
job, the lower the job satisfaction of employees. The null hypotheses would be:
Ho: There is no relationship between stress experiencedon the job and the job satisfaction of employees.This would be statistically expressed by:
Ho: P = 0where P represents the correlation between
stress and job satisfaction, which in this case is equal to nocorrelation
12
-
7/31/2019 Lecture 7 RMT
13/56
Examples for the nondirectional
relationship The alternate hypotheses for the above null, can be
stated as:
HA: P
-
7/31/2019 Lecture 7 RMT
14/56
Examples for the nondirectional
relationship For the example: There is a relationship between age and
job satisfaction.
For this nondirectional statement, the null hypotheses
would be statistically expressed as:H0: p=0
The alternate hypotheses would be expressed as:
H0: P 0
14
-
7/31/2019 Lecture 7 RMT
15/56
After formulating the null and alternate hypotheses,the appropriatestatistical tests (t tests, F tests) canbe applied, which would indicate whether or notsupport has been found for these hypotheses.
15
-
7/31/2019 Lecture 7 RMT
16/56
Exercise
A production manager is concerned about the low output levels of
his employees. The articles that were read of job performance
mentioned four variables as important to job performance:
skill required for the job,
rewards,
motivation,
and satisfaction.
In several articles it was also indicated that only if the rewards were
(attractive) did motivation, satisfaction, and job performance increase,
not otherwise.
16
-
7/31/2019 Lecture 7 RMT
17/56
Exercise Given the above situation, do the following:
1. Define the problem.
2. Evolve a theoretical framework.3. Develop at least six hypotheses.
17
-
7/31/2019 Lecture 7 RMT
18/56
Exercise (cont.) Problem Statement
How can the job performance (output) of the
employees be increased through enriched jobs andrewards?
18
-
7/31/2019 Lecture 7 RMT
19/56
Schematic Diagram for the Theoretical
Framework
SOLUTION TO EXERCISE 5.13Copyright2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
19
-
7/31/2019 Lecture 7 RMT
20/56
Hypotheses HA1: If the job is enriched and utilizes all the skills
possessed by the employee, then employee satisfactionwill be high.
HA2: If the job is enriched and utilizes all the skillspossessed by the employee, then employee motivationwill be high.
HA3: There will be a positive correlation between
satisfaction and motivation.
20
-
7/31/2019 Lecture 7 RMT
21/56
Hypotheses HA4: Greater rewards will influence motivation and
satisfaction only for those employees who find the
rewards attractive, not for the others. HA5: Satisfaction and motivation will positively
influence performance.
HA6: The more enriched the job and the greater the
skills utilized by the job, the higher the level ofemployee performance.
21
-
7/31/2019 Lecture 7 RMT
22/56
-
7/31/2019 Lecture 7 RMT
23/56
Exercises on Theoretical Framework (Cont.)Theoretical Framework
Since the administrators main concern is about the
strike, teachers strike is the dependent variable. Pay
and the physical environment of the classroom are the
two independent variables, which influence the strikesituation.
23
-
7/31/2019 Lecture 7 RMT
24/56
Exercises on Theoretical Framework (Cont.) The greater the pay demands made by the teachers, the
greater the possibility of a strike, since the school
administration refuse the idea of higher wages.
The more uncomfortable the classroom physical
environment, the more difficult it will be for teachers todo an effective job in the classroom, and hence the
greater the possibility of teachers going on strike.
24
-
7/31/2019 Lecture 7 RMT
25/56
Exercises on Theoretical Framework (Cont.)However, this relationship between the independent
variables and the dependent variable will be true only for
those teachers who are not dedicated to teaching. Thetruly dedicated teachers would be more concerned about
doing a good job despite the hardships faced by them,
and hence the pay demands and the classroomenvironment will not be factors influencing their
decision to join the strike.
25
-
7/31/2019 Lecture 7 RMT
26/56
Schematic Diagram 5A
THEORETICAL FRAMEWORK ANSWERS TO EXERCISES (PAGES 113-120 OF MANUAL) 5ACopyright2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
26
-
7/31/2019 Lecture 7 RMT
27/56
Hypothesis: H01: Dedication to teaching will not alter the
relationship between the independent variables of payand classroom environment and the dependent
variable of teachers decision to go on strike.
HA1: Only for those teachers who are not truly
dedicated to teaching, will pay considerations andclassroom environment be factors that wouldinfluence their decision to go on strike.
27
-
7/31/2019 Lecture 7 RMT
28/56
-
7/31/2019 Lecture 7 RMT
29/56
Secondary data Reanalyzing the already collected data for some other
purpose
Raw vs. Compiled data
-
7/31/2019 Lecture 7 RMT
30/56
Types of secondary data Documentary data are often used in research projects
that also use primary data collection methods. However,
you can also use them on their own or with other
sources of secondary data
Survey based data refers to data collected using a survey
strategy, usually by questionnaires, that have been
already analysed for their original purpose
-
7/31/2019 Lecture 7 RMT
31/56
Multiple-source secondary data can be based entirely on
documentary or on survey or can be amalgam of the two.
The key factors is that different data sets have been
combined to form another data set prior to your accessing
the data.
Types of secondary data
-
7/31/2019 Lecture 7 RMT
32/56
Types of secondary data
Source: Saunders et al. (2006)Figure 8.1 Types of secondary data
-
7/31/2019 Lecture 7 RMT
33/56
Locating secondary data
Establishing that the required secondary data isavailable
Locating the precise data required
-
7/31/2019 Lecture 7 RMT
34/56
Availability of secondary data
References in publications (books, journal articles)
Within organisations (unpublished sources)
Tertiary literature (indexes and catalogues inarchives or online)
-
7/31/2019 Lecture 7 RMT
35/56
Availability of secondary data
References in published guides
Data held by organisations
Data on the Internet
-
7/31/2019 Lecture 7 RMT
36/56
Evaluating secondary data
Fewer resource requirements
Unobtrusive
Longitudinal studies may be feasible
Provision of comparative and contextual data
Unforeseen discoveries may occur
Generally permanent and available
-
7/31/2019 Lecture 7 RMT
37/56
Evaluating secondary data
Purpose of data collection may not match theresearch needs
Access may be difficult or costly
Aggregations and definitions may be unsuitable
No real control over data quality
Initial purpose may affect data presentation
-
7/31/2019 Lecture 7 RMT
38/56
Evaluating secondary data
Enable the research question(s) to be answered
Enable research objectives to be met
Have greater benefits than their associated costs
Allow access for research
-
7/31/2019 Lecture 7 RMT
39/56
Evaluating secondary data
Source: Saunders et al. (2009)
Figure 8.2 Evaluating potential secondary data sources
-
7/31/2019 Lecture 7 RMT
40/56
Sources of Secondary Data Federal Bureau of Statistics
World Bank
IMF
State Bank
Ministry of Commerce
Karachi Stock Exchange
Business Recorder
International sources
-
7/31/2019 Lecture 7 RMT
41/56
Federal Bureau of Statistics
Pakistan demographic survey
Labour force survey
Business registerWeekly sensitive price indices
Foreign trade statistics
Monthly price indices (CPI, WPI, SPI)
National accounts
Gross national product
http://t15.pdf/http://cpi_annexure_july_2011.pdf/http://table3.pdf/http://newtable2-1.pdf/http://newtable2-1.pdf/http://table3.pdf/http://cpi_annexure_july_2011.pdf/http://t15.pdf/http://t15.pdf/http://t15.pdf/ -
7/31/2019 Lecture 7 RMT
42/56
Federal Bureau of Statistics Census of manufacturing industries (Industry)
Employment and employment cost (all employees)
Employment and employment cost (production workers)
Employment and employment cost (non-production workers)
Fixed assets
Industrial cost
Non-industrial cost
Value of production
Trade margin
Census value added Contribution to GDP
Indirect taxes
Stocks statistics
Value of fuel and electricity consumed
http://3.0_a.pdf/http://3.0_a.pdf/ -
7/31/2019 Lecture 7 RMT
43/56
World Bank Online data catalogs
World bank finances
World development indicators Global development finances
World development report
Social economic databases
Education and gender statistics
-
7/31/2019 Lecture 7 RMT
44/56
-
7/31/2019 Lecture 7 RMT
45/56
IMFWorld Economic Outlook
Data are available from 1980 to the present, andprojections are given for the next two years
National accounts
Inflation
Unemployment rates
Balance of payments Fiscal indicators
Trade for countries and country groups
Commodity prices
-
7/31/2019 Lecture 7 RMT
46/56
IMF eLibrary
-
7/31/2019 Lecture 7 RMT
47/56
IMF - International Financial
Statistics
-
7/31/2019 Lecture 7 RMT
48/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
49/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
50/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
51/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
52/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
53/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
54/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
55/56
Karachi Stock Exchange
-
7/31/2019 Lecture 7 RMT
56/56
International SourcesWERS Work Place Employment Relations Survey
Projects:
Causality Of Demand For Money In Selected AsianEconomies: Short Term and Long Term Analysis
TOL Projects Trust, OCB and Leadership
PTCL: Making one time customer a life time partner
through competitive customer service