RESEARCH OBJECTIVES

Meaning

·     Research Objectives are the specific components of the research problem, that you’ll be working to answer or complete, in order to answer the overall research problem. - Churchill, 2001

·     The objectives refers to the questions to be answered through the study. They indicate what we are trying to get from the study or the expected results / outcome of the study.

Criteria

   Research Objectives should be clear and achievable, as they directly assist in answering the research problem.

n     The objectives may be specified in the form of either statements or questions.

n     Generally, they are written as statements, using the word “to”. (For example, ‘to discover …’, ‘to determine …’, ‘to establish …’, etc. )

HYPOTHESIS

INTRODUCTION

            Formulation of hypothesis or propositions is an important step in the formulation of the research problem. It is known as principal instrument in research. In fact in certain situations the very purpose of research is to test the hypothesis. Although hypotheses are not essential for research they, certainly provide clarity, specificity and focus to the problem. They tell the researcher what specific information to collect, and thereby provide greater focus.

MEANING

            Hypothesis is a tentative proposition. It is a hunch. It is an assumption or an intelligent guess. It is formulated for empirical testing. It is a tentative answer to a research question. It is tentative because it is yet to be empirically tested.  You test these by collecting information that will enable you to conclude if your hypothesis was right. According to Lundberg,” hypothesis is a tentative generalisation, the validity of which remains to be tested.”  According to Goode and Hatt, it is” a proposition which can be put to a test to determine its validity.” From the above definitions three things become clear. First of all hypothesis is a tentative propositions. Secondly its validity is unknown. Thirdly in most cases it specifies a relationship between two or more variables.

CHARACTERISTICS

• A hypothesis must be simple, specific and conceptually clear. It must try to test only one relationship at a time. For example the average age of the male students in this class is higher than that of the female. This hypothesis is simple, clear, specific and easy to test. It tells you what you are attempting to compare (average age of this class), which population groups are being compared (female and male students), what you want to establish (higher average age of the male students).
• A hypothesis must be capable of verification: If the hypothesis is not verifiable the hypothesis has no meaning. In other words methods and techniques must be available for data collection and analysis.
• Hypothesis should be related to body of knowledge: The hypothesis should emerge from the existing body of knowledge. Above all it should add to it. Naturally a hypothesis is to be formulated after a through reading of the subject.
• Hypothesis must be operationalisable: It must be expressed in terms that are measurable. If it is not measurable it cannot be tested. In such a situation no conclusion can be drawn.

TYPES OF HYPOTHESIS

            There are different kinds of classification of hypotheses. Now on the basis of its function it can be classified as Descriptive hypothesis and Relational hypothesis. Another approach is to classify them as Working hypothesis, null hypothesis and statistical hypothesis. Third approach is to divide them on the basis of the level of abstraction. Accordingly on the basis of simple description one can have commonsense hypothesis. Secondly on the basis of logical derivation one can have complex hypothesis. Finally on the basis of abstraction one can have analytical hypothesis.

Descriptive Hypotheses and Relational Hypotheses: Descriptive hypotheses describe the characteristics of a variable (such as size, form, distribution). The variable may be an object, person, organisation, situation or event. For example the rate of unemployment among art graduates is higher than that of commerce graduates. Public enterprises are more open for centralised planning. Relational hypotheses describe a relationship between two variables. The relationship suggested may be positive or negative correlation or causal relationship. For example families with higher incomes spend more on recreation. Participative management promotes motivation among executives.

Working Hypotheses: As one begins to study a problem hypotheses are formulated. In the beginning the hypotheses may not be very specific. Such hypotheses are known as working hypotheses. These are subject to correction as one begins to go further into the research.

Null Hypotheses: These are hypothetical statements denying what are explicitly indicated in working hypotheses. These are formulated for testing statistical significance. As the test would nullify the null hypotheses they are called null hypotheses.

Statistical Hypotheses: These are statements about a statistical population. These are derived from a sample. These are quantitative in nature and they are measurable. For example group A is older than group B.

Commonsense Hypotheses: These are hypotheses that are very common to even ordinary lay men. They highlight empirical uniformities. Such empirical uniformities are perceived in our societies and in the behaviour pattern of certain groups of people. For example soldiers from upper-class are less adjusted in the army than lower class men. Generally commonsense hypotheses are not scientific in nature. Because they are mixture of clichés and moral judgements. Thus they have to be made scientific.

Complex Hypotheses: They aim at testing the logically derived relationship between empirical uniformities. In our social setting one can come across number of empirical uniformities. When such uniformities are made into ideal types they become complex hypotheses. For example the marginalised groups are generally silent. Such an empirical uniformity is termed as culture of silence. Now this hypothesis may be an execration of the truth. As the statements are far removed from the fact they become complex.

 Analytical Hypotheses: These hypotheses are concerned with the relationship of analytical variables. This requires the formulation of a relationship between changes in one property and changes in another. This kind of hypotheses is the outcome of highest level of abstraction.

FUNCTIONS OF HYPOTHESES

 In social sciences hypotheses renders several important functions. We shall discuss some of the important functions.

• It gives a definite point for the investigation. It provides the direction of the study. It provides the much needed focus to the research.
• A hypothesis specifies the source of data, which shall be studied, and in what context they shall be studied.
• It decides the data needs. It defines which facts are relevant and which are not.
• A hypothesis suggests which type of research is likely to be most appropriate.
• It decides the most appropriate technique of analysis.
• Hypotheses contribute to the development of theory. It links theory and investigation.

Variable

n    A concept which can take on different quantitative or qualitative values

Ex: weight, height, income, etc

n    Variable is a ‘symbol to which values or numerals are assigned’.

Variable – Construct /Quality / Property to be studied

Types of Variable

n    Independent Variable

n    Dependent Variable

n    Extraneous Variable

n    Continuous Variable

n    Discrete Variable

n    Ordinal Variable

n    Nominal variable

Independent Variable

n    The variable that is antecedent to the dependent variable is a ‘independent variable’

n     The variable whose change results in the change in another variable is called an independent variable.

n    An independent variable is the one that influences the dependant variable in either a positive or negative way

Dependent Variable

n    The variable which depends upon or is a consequence of another variable is a ‘dependent variable’

n    The variable that changes in relationship to changes in another variable(s) is called dependant variable.

Defining Independent  & Dependent Variable

n     Independent V                   Dependent V

Presumed Cause                                   Presumed Effect

Stimulus                                                Response

Predicted From…                                Predicted To…

Antecedent                                           Consequence

Manipulated                             Measured Outcome

Input                                                    Output

Treatment                                             Outcome

Extraneous Variable

n    Independent variable that are not related to the purpose of the study but may affect the dependent variable are termed as an ‘extraneous variable’

  IV                                                                  DV

                                   

                                    EV

Continuous Variable

n     Phenomena which can take on quantitatively different values even in decimal points are called as a ‘continuous variable’

Ex: Age

n     height in centimetres (2.5 cm or 2.546 cm or 2.543216 cm)

n     temperature in degrees Celsius (37.20C or 37.199990C etc.)

Discrete Variable

n    If the values can only be expressed in integer values, they are non-continuous variable or discrete variable

Ex: number of children

n    number of visits to a clinic (0, 1, 2, 3, 4, etc).

n    number of friends (0, 1, 2, 3, 4, 5, etc.)

Ordinal variables

n    These are grouped variables that are ordered or ranked in increasing or decreasing order.

For example:
High income (above $300 per month);
Middle income ($100-$300 per month); Low income (less than $100 per month).

Other examples are:

n    Agreement with a statement: fully agree, partially agree, fully disagree

n    Disability: no disability, partial disability, serious or total disability

n    Seriousness of a disease: severe, moderate, mild

Nominal variables

n    The groups in these variables do not have an order or ranking in them.

For example: 

Sex: male, female

Main food crops: maize, millet, rice, etc.

Religion: Christian, Muslim, Hindu, etc.

Factors rephrased as variables