Contents
- Introduction
- Aristotle Theory on Slavery
- Criticism of Aristotle’s Slavery Theory
- Conclusion
- Reference
Aristotle Theory on Slavery
The State of Nature
The state of nature
- Introduction
- Thomas Hobbes
- John Locke
- Jean-Jacques Rousseau
- Conclusion
Difference between Validity and Reliability
When the variables embedded in
hypotheses or otherwise relevant for a particular research are operationally
defined, their meanings may undergo changes. If these changes are significantly
large, tests of the relevant hypotheses may be rendered meaningless. It is,
therefore, important that the operationally defined measures, whether direct or
indirect, satisfy certain stipulated properties. Two most important properties against
which the success or failure of the measures are judged are validity and reliability.
Validity
A measure is valid if it measures
what is supposed to measure. In case of direct measure is valid if it measures
what it is supposed to measure. In case of direct measures, the validity is
self-evident, while it is only approximate in case of direct measures- indexes
and scales. In fact, there is no way to guarantee that an indirect measure is
valid for measuring a concept. However, the researchers have devised way to
deal with the issue.
To examine the validity of a measure,
at least three different types of validity based on different perspectives are
considered: face validity, predictive validity, and construct validity. Face validity
implies that the items chosen to measure a variable are logically related to
it. Suppose that the researcher is measuring the variable ‘religiosity’. In this
case, items such as “Did you get your children vaccinated during the last six
months?” or “Do you know the average family size in Guatemala?” do not seem, at
least apparently, to be related logically to religiosity. A scale or index
based on such items would not have face validity for measuring religiosity,
although the same items may give rise to measure with a very high face validity
for measuring some concept related to health status or population policy. The basic
problem with using face validity to judge whether a measure is valid is that it
is highly subjectively determined. It is possible that one researcher finds a
measure as possessing high face validity while another researcher may find the
same measure as possessing low face validity.
To examine whether a measure is valid
or not, predictive validity is the most useful. A measure is said to have
predictive validity if a high correlation can be demonstrated between the
behavior predicted by the measure, and the behavior subsequently exhibited. For
example, supposed that a researcher has developed a composite measure for the
variable ‘religiosity’ on the basis of ten items derived from a universe of items
though to reflect religiosity. Each individual in the sample has score on this
composite measure that locates his position in the religiosity continuum. An individual
with a high score is thought to have a high degree of religiosity. The religiosity
of the same individuals is observed subsequently in practice. If it is observed
that the individuals who scored high on the measure are also more religious in reality
than those who scored low, the measure has predictive validity. It is to be
noted that simply a high numerical association does not guarantee that the
measure is measuring the variable: it simply provides support to the contention
that is may be valid measure. Another problem is that the identification of a
subsequent behavior as the predicted behavior of a respondent is subjective
since many difference interpretations of the same behavior are possible, and
the choice of one as the predicted behavior is a matter of subjectivity.
The third basic method of validity
determination is the construct validity. In construct validity the researcher
specifies the kinds of relationships he expects on the basis of theoretical
considerations between the measure and other variables. He then correlates the
scores based on the measure with those variables and compares those observed
correlations with his expected relationships. A small difference between the
observed and expected relationship would boost the confidence in the validity
of the measure. For example, social status is expected to be positively
correlated with occupation and education and negatively correlated with the
number of children ever born. The correlations of the scores on social status
scale with these variables in the expected directions will provide evidence in
support of the validity of the measure of social status.
Reliability
If a measure, when applied repeatedly
to the same object under constant conditions, produces the same result each time,
it called reliable. The measure is unreliable if it produces different results.
For example, suppose that the researcher is interested to study the attitude
towards democracy of a number of newspapers. To measure this attitude, he can
follow a number of procedures. One of these procedures may be that he reads the
editorials of all the newspapers for a specific number of days, and on the
basis of his judgement, rank-orders the newspapers according to the degree of prodemocracy
attitude they possess. This strategy has
problems of reliability inherent in it. If several evaluators read the same
editorials, they may draw conclusions different from each other: a newspaper
that appears prodemocracy to one evaluator may not be so to the other. Thus,
this procedure --- reading newspapers editorials --- of measurement of attitude
towards democracy, when applied repeatedly may produce difference results. In other
words, the conclusions are heavily dependent on subjectivity, and the measurement
procedure is not reliable.
Another procedure may be to count the
number of times the words “democracy”, “freedom”, and “liberal”, for example,
appear in the newspaper editorials for a specified number of days. The assumption
is that the greater this number in a newspaper editorial, the more prodemocracy
the newspaper. This measurement procedure is more reliable since several
evaluators may count this number over and over and still will make the same
conclusions. It is to be noted that whether this number really measures the prodemocracy
attitude or not, is a question of validity discussed earlier.
There are a number of techniques for determining
the reliability of measure. These are: (1) test-retest, (2) parallel forms, and
(3) split-half.
In the test-retest technique individuals
are scored on the measure at a certain time. These are the test scores. The same
individuals are scored for the same measure at some later date, to obtain
retest scores. A high correlation (0.90 or more) between the two sets of scores
would imply that the measure is reliable, on the assumption that no intervening
variables interfered significantly with the retest scores --- an assumption
that hardly holds in practice. There are always some interest events that may
influence the retest scores.
Levels of Measurement in Social Research
Theories function tacitly as causal models for social phenomena. Frequently, theories are tested by statistical verifications of hypotheses derived from them (theories). The statistical procedures require that the data be quantified and reduced to numerical form. A wide variety of statistical techniques are available for analysis of data, depending in part, on the levels of measurement of the variables. The use of a particular statistical technique presupposes that the variables have been measured at certain levels. As we shall see later, the higher the level of measurement, the greater the variety of permissible mathematical operations that may be performed with the data, and the greater the level of sophistication of statistical techniques that can be used for data analysis.
A. Nominal Measures
The most basic measurement level is
the nominal measure in which the categories of a variable – the nominal
variable – differ only in name. the only relationship between the categories of
a nominal variable is that they are different from each other, and that no
category is necessarily higher or lower, greater or smaller, etc., than another
category. Knowledge of the criteria for placing individuals or objects into
categories which are exhaustive (include all categories) and mutually exclusive
(no case in more than one category), suffices for the attainment of this level
of measurement. Thus, the variable ‘sex’ with categories male and female which
are exhaustive (since these are the only two categories possible), and mutually
exclusive (since none can belong to both the categories at the same time), is a
nominal variable. This is because, the categories are just names only. We
cannot say that male is higher, better, and so forth, than female, or vice
versa. Similarly, the variable ‘religion’ with categories Islam, Christianity,
Hinduism, Buddhism, and other is a nominal variable.
It should be noted here that the researcher can arbitrarily assign numbers to the categories of a nominal variable, say, sex such as 1 for male and 2 for female. He will do equally well by assigning 5 for male and 2 for female. These numbers just label the categories, and no arithmetic operation can be performed on them. We cannot, for example, speak of average religion or average sex.
B. Ordinal Measures
A variable whose categories have an
ordered relationship is called an ordinal variable. In this measure, objects
can be ordered along a single continuum but the distance between the positions
are not meaningful. Thus, we can classify families according to social class
into the ordered categories: upper, upper middle, lower middle, and lower, or
classify individuals according to their religiosity into the ordered
categories: low religiosity, medium religiosity, and high religiosity. In
ordinal measures we have categories as well as order among the categories. In
this sense, ordinal measures are at a higher level than nominal measures that
have only categories. In ordinal measures we can not only categorize the
objects or individuals but also order them. As in nominal measures, the
researcher can also assign numerals to the categories of an ordinal variable.
For example, low religiosity, medium religiosity, and high religiosity may be
assigned numbers 1, 2, and 3 respectively. Here the numerals not only define
differences among the categories but also indicate ‘greater than’ or ‘less
than’ relationships. For example, an individual with 2 for the religiosity
variable may be thought to be more religious than an individual with 1 on the same
variable, and an individual with 3 may be thought to be more religious than an
individual with 2.
It should be noted that, as in nominal measures, arithmetic operations such as additions and subtractions also cannot be performed on numbers assigned to categories of ordinal variables. For example, for the religiosity variable we cannot add 1 and 2 and say that it is equal to 3. This will be equivalent to saying that low religiosity plus the medium religiosity equals high religiosity. But this is not the case, since we do not know the distance or interval between the categories. We do not know whether the distance between low religiosity and medium religiosity is the same as the distance between medium religiosity and high religiosity. It may so happen, for example, that individuals falling into the categories 1 and 2 do not differ much in terms of religiosity, while those falling into category 3 are very different from those falling into category 2. The ordinal measure tells us the greater than, more than, lower than etc. relationships, but not by how much greater, more, or lower.
C. Interval Measures
An interval variable is one in which the categories have an ordered relationship as well as the extract distances between categories are known. An interval variable has all the characteristics of both nominal and ordinal variables, and in addition, it has a constant unit of measurement. It provides equal intervals, however, from an arbitrary origin. An example of the interval measure is the measurement of temperature in centigrade scale. It is measured in terms of degrees, while no such measurement unit which remains constant from on situation to another exists to measure, for example, sex or religiosity. The difference between 60 and 61 degrees centigrade is the same as the difference between 30 and 31 degrees Centigrade. The distance between one category and the next is constant and is known. This means that the numbers representing categories in an interval measure can be added or subtracted in a meaningful way. But the ratio of two numbers is not meaningful. For example, we cannot say that 40 degrees is twice as hot as 20 degrees, neither can we say that a student who scored 90 in research methods test has twice the knowledge in research methods than a student who scored 45. This is because, both the temperature and the test score have arbitrary origins, since the zero degree, which is the freezing point of water, does not imply no temperature, and zero score in the test does not imply to complete lack of knowledge in research methods. There is some temperature at zero degree and at this temperature the water freezes. Similarly, a student who has scored zero in the test is unlikely to have had no knowledge of the attribute.
D. Ratio Measures
The ratio measurement is the highest
level of measurement. It has all the features of interval measure, and in
addition, an absolute or non-arbitrary zero implying that the zero value of a
ratio variable corresponds to the complete lack of the attribute. Thus zero
length implies no length at all, and zero children in a family implies that it
has no children. In this measure, multiplication and division are meaningful.
Thus, it is possible to state that a person 20 years old is twice as old as a
child 10 years old is twice as old as a child 10 years old. Some other examples
of ratio variables are height, time, distance and income.
An ordinal measure is a nominal
measure, and in addition, has the ordinarily property an interval measure is an
ordinal measure plus it has a unit of measurement; and the ratio measure has
all the properties of nominal, ordinal and interval measures plus it has an
absolute zero. This cumulative nature of these measures shows that a higher
level measure can be used as a lower level measure, but the converse is not
true. Thus, an interval variable, for example, can always be used as a nominal
or ordinal variable, but neither a nominal variable nor an ordinal variable can
be used as a nominal or an ordinal variable, but neither a nominal variable nor
an ordinal variable can be used as an interval.
Source: Methods and Techniques of Social Research by Abu Jafar Mohammad Sufian
Appropriate Preposition
|
S.L. |
Question |
Answer |
|
1 |
He refrained ----- taking any drastic action |
From |
|
2 |
You may go for a walk if you feel ----- it. |
Like |
|
3 |
Tania was a wonderful singer, but she’s ----- her
prime. |
Past |
|
4 |
Eight men were concerned ------ the plot. |
In |
|
5 |
He insisted ------- seeing her |
On |
|
6 |
Noureen will discuss the issue with Nasir ------
phone. |
Over |
|
7 |
Some writers sink ------ oblivion in course of time. |
Into |
|
8 |
Wordsworth introduced the readers ---- a new kind of
poetry. |
To |
|
9 |
He fantasized ------- winning the lottery. |
About |
|
10 |
Laziness is detrimental ------- success. |
For |
Advantages of Sampling in Social Research
The question naturally arises as to why the researcher needs to base his inference about the population on only a sample from it rather than on itself. In some researches the number of members in a population are so few that it is rater desirable that all members be included in the sample, in which case, the sample is the population itself. For example, a researcher aiming at improving the existing education system, needs to study the attitude of the people at the decision making level. In this case there may be total of only, say, 40 people at that level and it would be desirable to include all of them in the study. There are occasions when, even if the population is large, a complete coverage is a necessity. For example, suppose that a descriptive study is aimed at identifying the contraceptive needs of all the family planning clinics throughout the country. If the objectives of this need identification study is to satisfy these needs from the central family planning store, then taking a sample of clinics would not serve the purpose – a complete coverage of all the clinics would be required. However, there are many circumstances in which a sample survey would be preference to a complete coverage. The reasons, in brief, are discussed below.
Usually the money and
time available for a research project are adequate to study on a fraction of
the population. In a demographic research, for example, it much cheaper and
much less time consuming to collect data from, say, only 5000 couples than from
100000 couples. Obviously, the cost and the time involved are tremendously
reduced by sampling. Indeed, with a limited budget and limited time, a research
involving a large population can have afforded only through sampling. The time
factor is very important when the information is needed urgently. At the time
of an urgent need, collection of data from all members of a huge population
requiring a long time would defeat the purpose of the research. In such a
situation sampling is the only feasible solution to the problem. In some cases,
a complete coverage may take years, in which case, part if not whole, of the research
interest may be lost by the time the results are ready.
Another merit of sampling
is that a researcher can afford employing highly trained interviewers for
collecting information from the members of the sample while the enormous
interviewing project for a complete coverage would, by necessity, required a
large staff of interviewers, a many of them will, very likely, be of lower
quality. Understandably, the highly trained interviewers. Moreover, since the
volume of administrative and managerial work is reduced significantly for a
sample compared to a complete coverage, sampling permits a more careful
supervision of the field work, data quality check, subsequent processing of
data, and so on. As a result, the sample may produce even more accurate results
than would be expected on the basis of complete coverage.
As mentioned before, the
purpose of selecting a sample from a population is to draw inference about the
population characteristics on the basis of the analysis of sample data the key
to achieving this purpose is to select a representative sample. A perfectly representative
sample actually reflects all various that exist in the population. If the
population is homogeneous, a sample of only one member will perfectly represent
the population. Thus, if all households in a community have the same income, a
sample of only one household will perfectly represent the population of incomes
of the community households, an is enough to tell the income story of all
households in the community. But most of the populations a researcher usually
encounters are not homogeneous, and as a result, samples of many members need
to be drawn. A large sample is no guarantee of representativeness, however. As mentioned
before, a number of techniques have been developed to draw samples which,
although not perfectly representative, can be used, for practical purposes, as
sufficient bases for drawing inferences about the population.
Source: Methods and Techniques of Social Research by Abu Jafar Mohammad Sufian
Meaning of Sampling in Research
Sampling is the process of selecting a subset of individuals from a larger group of individuals, the selection being done with a view of drawing inferences about the larger group on the basis of information obtained from the subset. The larger group of individuals is known as population while the subset is known as sample. The idea of sampling is in fact very frequently used in our everyday life, although usually in crude form.
For
example: a physician tests only a few drops of blood of his
patient and then the test results are generalized for the entire volume of
blood in his body. The generalization is possible since every drop of blood is
the sample as every other drop in terms of all possible characteristics, that
is, all drops of blood are homogeneous to the extent that any drop perfectly
represents any other drop. Here the physician is selecting a perfectly
representative sample of a few drops of blood from the population of all drops
of blood conceivable in the patient’s body, and then based on the results of
the test of these sample drops, is drawing inference about the entire
population of blood drops.
Likewise,
a rice dealer testing a handful of rice from a sack to determine the quality of
the whole lot of rice in it, or a cook pressing a few boiled grains between
fingers to declare whether they are ready to be served, are all selecting
samples, like the physician, from extremely homogeneous populations. Because of
this homogeneity, any sample is enough to draw a valid conclusion about the
total population. When the population is not homogeneous, inference based on
just any portion from this population cannot be so easily taken as valid.
For
example, if a customer encounters high prices of commodities
in a few shops in a large shopping complex comprising a hundreds of shops and
then based on his experiences, infers that the prices of commodities in the
whole shopping complex are higher than usual, it would be difficult to take his
inference as granted. This is because, the shops in the complex are likely to
vary in prices of commodities unlike for example, the drops of blood in a body
which are all alike. A drop of blood in a body is the sample as any other drop,
but a shop may or may not be the sample as any other shop in terms of prices of
commodities. In other words, population of blood drops is homogeneous while the
shop population in terms of prices of commodities is not. For a homogeneous
population, whatever method is used to draw the sample, it represents the
population and hence inference based on it can be taken as valid. For a
heterogeneous, population, on the other hand, in order to choose a
representative sample that should essentially contain the same variation as in
the population so that the inference based on it can be taken as useful to
certain degree of confidence, the method of choice becomes important. A number
of methods based on varying circumstances have been developed to obtain
representative samples to enable the researcher to draw sound inferences about
the population on the basis of the sample results.
Source: Methods and Techniques of Social Research by Abu Jafar Mohammad Sufian
The Research Process
The operations involved in the process of social research are highly interrelated and continuously overlapping that it is difficult to make them correspond to a prescribed definite pattern procedure. such an attempt may result in failure to serve the purpose of introducing one to the broad range of intricacies of the research process. however, this does not imply a random choice of steps in social research. the following major steps, arranged in order, are common to almost all published researches:
- Formulation of Research Problem
- Overview of Relevant Literature
- Operationalization of Variables
- Population and Sample
- Methods of Data Collection
- Processing and analysis of Data
- Interpretation of Results and Conclusions
It is to be noted that each of the above
major steps is, in fact, a group of many operations each of which has its own
role to play in the research process. This section provides a brief
introduction to each of the above major steps of the research process.
A.
Formulation
of Research Problem
In conducting a research, the
researcher first chooses the general area of his interest from among the wide
array of general areas that exist in his parent discipline. However, with only
the general area in hand he does not know what specific information he needs to
collect since he does not have any specific question to answer. This is why he
needs to formulate a specific problem from within the chosen general area to
make the whole exercise a worthwhile scientific inquiry. The specific questions
with act as guidepost to keep him headed in the right direction.
B.
Overview
of Relevant Literature
Usually, the researcher’s own
intellectual orientation, inclination, training, and experiencing suggest the
general area of this interest. But the formulation of specific research problem
from within the general area almost inevitable requires a review of the
relevant literature. Such a review, as an act of meaningfully synthesizing
existing knowledge in the area, helps him to detect the gap in the existing
knowledge, an eventually to define his problem in terms of this gap. Broadly,
the review of literature enables him to formulate his problem in terms of the
specific aspects of the general area of his interest that have not so far been
researched.
A critical study of the works already
done in the general area not only provides him an exposure necessary to
determine the priority of what ought to be studied but also helps him to locate
his problem in some theoretical perspective and to link it up with whatever
knowledge exists in the area of inquiry. Such a review is needed to demonstrate
the relevance of his research to the larger body of knowledge. Ignoring this
aspect to research amounts to seeking answers to countless behavioral riddles
encountered in a haphazard manner everyday rather than to attempting to
discover the unity and uniformity that underline the human conduct. Through a
proper review of the relevant material he may develop the coherence between the
findings of this own study and those of other studies.
Having identified a specific problem
for his research, he attempts, if feasible, to develop hypothesis the
formulation of which depends partly on his awareness of the state of existing
knowledge in the area. A hypothesis is a tentative statement about something whose
validity is yet to be known. Usually, hypotheses are framed so as to contain
suggested explanation or solution of the problem in the form of propositions.
The purpose of research is then to support or refute the hypotheses. Generally,
in explanatory researches – those that seek to explain phenomena by involving
the examination of may different aspects of phenomena simultaneously –
hypotheses are formulated proposing the expected relationships among variables
used to explain the particular phenomenon of interest. In exploratory
researches – those undertaken to explore new interests, or in descriptive
researches that simply describe events, on the other hand, the findings
themselves may lead to formulation of hypotheses.
Once hypotheses have been formulated,
or objectives clearly defined, the researcher has set his task in a definite
direction. In other words, he now knows ‘what to do’. The question that follows
then is ‘how to do’. This entails a number of decisions. Probably the first
step involved in ‘how to do’ is operationalization and measurements of the
concerned variables and concepts. At the second and subsequent steps of ‘how to
do’, the research will be involved in defining the relevant population, drawing
a sample, and collection, analysis, and interpretation of data. A review of the
relevant literature enables him to identify the concepts and their meanings
used in various studies, and also to pick up the merits and avoid the pitfalls,
if any, of these studies. Such an experience is of great use in all steps of
his own research. Clearly, without a comprehensive exposure to the relevant
literature he would remain in darkness about these essential of a research. Put
differently, an effective review of the relevant literature is an essential
component of a research process.
C.
Operationalization
of Variables
It is very important that the variables used in a proposed research be clearly and concisely defined. Since concepts represent abstractions, the researcher has to devise some means of translating them into their empirical referents. In other words, the concepts and hence, the underlying variables have to be operationalized that methods to obtain empirical observations representing those concepts in the real world, can be developed.
D. Population and Sample
The researcher then decides his study population comprising all possible case of a particular phenomenon. It is rarely possible to study the entire population of interest, given the time and resource constraints. Sampling, then, becomes an essential part of the research. The researcher has to choose a certain segment of the population in such a way that it represents the population. When the elements of the population are far from homogeneity, as is often the case, it is very important that an appropriate method of drawing the sample is employed to ensure that a representative sample has been chosen. How large the sample should be, depends on a number of factors such as time, cost, precision required, etc.
E. Methods of Data Collection
Having chosen the sample, the
researcher proceeds to collect information from the sample elements on the
relevant variables and concepts. There are many different methods of collecting
data, depending on the nature and objectives of the study, availability of resources,
etc.
The most commonly used method of
collecting data is the interview method conducted by interviews in a face to face situation with the
respondents. Respondents are asked question, closed or open ended or of the
mixed types, and the responses are recorded by the interviewer on the spaces
provided on the interview schedule that contains the questions. Data can also
be collected by using self-administered questionnaires, in which case, the
respondents, in complete absence of any interviewer, answer the questions and
record the responses themselves. Other means of collecting information include
the use of secondary sources such as census, vital registration records, official
records, etc, which have already been gathered earlier, usually by some other
people for some other purpose. Amon other methods for gathering information are
experiment, observation, focus group discussion, and content analysis of
written materials. In experiment, the researcher usually manipulates a variable
and then observes and records the changes in people’s behavior due to such a
manipulation. In the observation method, he observes the social phenomena in
natural settings, and in focus group discussion the respondents are brought
together in groups, and the interviewer, while using a general discussion
guide, elicits detailed information through problems. In content analysis, data
are collected through specifying and counting social artifacts such as
newspapers, books, speeches, etc. for example, a question such as “Does the
newspaper X seem to have titled from the rightist to the leftist approach over
the recent years?” may probably be best answered by the method of content
analysis. None of the data collection methods is appropriate in all research situations.
One or more given methods may be more appropriate in one research situation in
another.
At the stage of deciding the method of data collection, the researcher usually gets involved in the construction of measuring instruments such as interview schedule, questionnaire, etc. The task of constructing the instrument requires a thorough understanding of the research problem as well as discussions with experienced and knowledgeable persons.
F. Processing and Analysis of Data
When the requisite data have been
gathered, the task of analyzing them is in order. The steps of collected data
in themselves do not mean anything unless answer to the research questions posed
at the beginning of the study are extracted out of them. The analysis phrase is
meant to serve this purpose.
The data are first processed, that is, edited to improve their quality, and then coded to convert them to the form of numerical codes representing attributes of varibles. Once coding is done, the data are ready to be fed into the computer for analysis. The analysis can also be carried out manually. The first step in the analysis is the determination of the numerical strength of different categories of response. Then the variables need to be described and summarized with the use of different measures of location, dispersion, correlation, etc. Most researches require that several variables be examined simultaneously, in which case, the approach is known as multivariate analysis. There are a number of techniques for conducting a multivariate analysis. The appropriateness of a technique in a specific situation depends on the nature of the research question asked, and the types of variables used in the analysis. As in the case of methods of collecting data, an analytical technique appropriate in one situation may not be appropriate or even application in another.
G. Interpretation of Results and Conclusions
The final task of the researcher is to interpret the results of the analysis and to draw conclusions. If the results of the analysis do not differ significantly from what can be explained on the basis of his hypotheses, the theory that gave rise to those hypotheses stands tenable. Otherwise, some modification of the theory may be suggested. In case he started without hypotheses, the generality of the findings may give rise to hypotheses to be tested in future research. The research while reporting his research should keep in mind who the audience is. The research report is expected to add to the existing knowledge, and to stimulate further research.
Source: Methods and Techniques of Social Research by Abu Jafar Mohammad Sufian
Types of Research
The purpose of research can be grouped into the following three broad types
- Exploration: It studies are mainly undertaken when the researcher has little or no knowledge about the situation under investigation, or when he is unaware of the specific aspects of a general problem. Such studies enable the researcher to formulate problems for more in-depth investigations, develop hypotheses, gain familiarity with potentially significant factors to be dealt subsequently in greater detail with more structural investigations, and so on.
- Description: Descriptive studies, on the other hand, are those that describe situations and events and are undertaken when much is known about the problem under investigation. A researcher may be interested in describing the different characteristics of a population or of a segment of it. A national census is a good example of a descriptive research in which an attempt is made to describe accurately a wide variety of characteristics such as income, education, sex composition, and so on of a national population. A cursory review of the social science literature reveals how replete it is with descriptive studies of all types of social events.
- Explanation: the third general types of studies are explanatory studies undertaken to explain events. Thus, if the research is reporting that families having greater number of child deaths also have greater number of children ever born than families with fewer or no child deaths, he is providing a description. But if he is reporting why families with more child deaths, he is providing a description. But if he is reporting why families with more child deaths have greater number of children ever born, he is performing an explanatory activity.
Plato’s Communism-PPT
Presentation Outline
- Objectives
- Introduction
- Communism of Property and Wives
- Criticism of Plato’s communism
- Conclusion
- References
Machiavelli’s Concept of Separation of Power and Ethics- PPT
Presentation Outline
- Introduction
- Machiavelli’s Concept of Separation of Power
- Machiavelli’s Concept of Separation of Ethics
- Criticism
- Conclusion
- References
Plato’s Ideal State -PPT
Presentation Outline
- Introduction
- Ideal State
- Objectives
- Features
- Criticism
- Conclusion
বাংলা ভাষা ও সাহিত্য জিজ্ঞাসা- ড. সৌমিত্র শেখর । (Bangla Vasha o shahitto Jigasha by Dr. Soumitra Shekhar)
প্রথম পর্ব (Part 1)
দ্বিতীয় পর্ব (Part 2)তৃতীয় পর্ব (Part 3)
Plato’s Justice Theory- Power Point Presentation
Presentation Outline
- Introduction
- Definition of Justice
- Plato’s Justice Theory
- Conclusion
Social Contract Theory by Thomas Hobbes, John Locke and Jean-Jacques Rousseau
1.3. Proponents
of the Social Contract Theory. 3
1.4. Social
Contract Theory by Thomas Hobbes. 3
1.5. Features
of Social Contract Theory. 4
1.6. Social
Contract Theory by John Locke. 4
1.7. Social
Contract Theory by Jean-Jacques Rousseau. 5
1.8. Comparison
among the Theorist 6
Gender Discrimination in Work Place: A Study on Bangladesh- Slide Presentation
Presentation Outline
- Introduction
- Definition
- Forms
of Gender discrimination at Workplace
- Gender
Discrimination in Various Sector of Bangladesh
- Causes
of Gender discrimination in the Workplace in Bangladesh
- Impact
of Gender Discrimination on Workplace
- Recommendation
to Overcome this Problem
- Conclusion
- References
Gender Discrimination in Workplace: A Study on Bangladesh- Conducted by Jayanta Bala
1.4. Forms of Gender Discrimination. 3
1.4.1. Employment Segregation by Gender. 4
1.4.4. Discrimination in Family Law.. 5
1.4.5. Discrimination at Work. 5
1.4.6. Discrimination in Education. 5
1.5. Different Kinds of Gender Discrimination. 6
1.5.1. Gender Discrimination in Shipyard. 6
1.5.2. Gender Discrimination in Shrimp Industry. 7
1.5.3. Gender Discrimination in Jute Mills. 7
1.6. Causes of Gender Discrimination. 7
1.7. The Effects of Gender Discrimination in
the Workplace. 7
1.7.3. Family Responsibilities. 8
1.8. The Ways of Preventing Gender
Discrimination. 9
1.8.1. Reversing Gender Discrimination. 9
1.8.2. International and Regional Laws. 9
1.8.3. Position under Nigerian Laws. 10
1.8.5. New Dimension Case Laws. 11
1.8.6. The Role of the Library. 11
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