A research problem encompasses a definite expression on a particular area of concern that points to the need for a structured scientific inquiry to address the issue. Regarding the rule of a scientific methodology, not all existing problems are research problems since they cannot all be studied (Frankfort-Nachmias, & Nachmias, 2015).
Variable; concepts represent an empirical phenomenon, and they convey a research problem. The moving of the research from the conceptual to empirical level allows the conversion of concepts to variables. A variable refers to a feature that does not have a fixed pattern (Frankfort-Nachmias, & Nachmias, 2015). Depending on the situation, a variable takes different values. For instance, with cases such as individuals, groups, and institutions, a variable can take two or more values. During the construction and testing of hypotheses, variables are used instead of concepts; therefore, the researcher should make a definite conclusion on the variables to be used.
A relation in scientific research entails the way two or more variable are connected (Frankfort-Nachmias, & Nachmias, 2015). For instance, a relationship between variable x and y means that they share something in common. Moreover, if we say that income and education are related, it means that both variables are parallel and a change in one will result in a systematic change on the other.
Hypothesis refers to the tentative answer to a scientific question (Frankfort-Nachmias, & Nachmias, 2015). Besides, hypothesis expresses the relationship between independent and dependent variables based on the research question. The credibility of research hypotheses comes after a researcher has conducted an empirical test against the null hypothesis.
Examples include the following. In a workplace, discrimination is portrayed in the situation when obese individuals paid less than non-obese individuals. To investigation the situation, the researcher should operationalize general questions into a number of hypotheses. A good example to represent this situation is a recent study by Katherine Mason regarding income inequalities between obese and non-obese people (Frankfort-Nachmias, & Nachmias, 2015). A null hypothesis states that both the obese and non-obese people have no difference in the income.
When formulating a research problem, social scientist must start by identifying the unit of analysis. The other name of this concept is the level of analysis. Therefore, a unit of analysis can be defined as a major entity that the researcher will study. The scientist must define the unit of analysis at the beginning of a research project (Frankfort-Nachmias, & Nachmias, 2015). It is important to identify the unit before collecting and analyzing the data. For instance, with political regimes or terrorist networks as units of analysis, the researcher must understand the social process of interest involved such as leaking of sensitive information and transfer of weapons. Similarly, researchers use the word survival to demonstrate behaviors of individuals, groups, organizations and countries, although they portray different unit of analysis. With the concept of survival, the researcher will provide distinct operational definitions to each unit. In the case of individuals, survival could mean physical existence in spite of difficult circumstance. Conversely, with organizations, survival may mean that the organization continues to exist legally despite experiencing losses.
The elements involved include the following. First, population entails the recipient of an intervention. Second, Intervention refers to the planned action that the population expects to receive (Frankfort-Nachmias, & Nachmias, 2015). Third, outcomes entail the measurement of action or service to determine whether or not the desired results were realized. Fourth, comparison refers to an alternative action that could be offered to the population. The researcher may decide to compare two or more individuals or groups.
In a situation when you examine attributes as a group and then change to individuals, there is a likelihood of experiencing distortion either in their observation or when interpreting the outcome. Due to this, it is inappropriate to provide a general interpretation of the complexity of the units. For instance, you cannot directly interpret by starting with a sophisticated unit of analysis and move to the simple one or vice versa. Therefore, the inaccurate information are what result in an ecological fallacy.
With individualistic fallacy, researchers use gathered evidence about individuals to draw inferences on groups, societies or nations (Frankfort-Nachmias, & Nachmias, 2015). For instance, a researcher could be making an individualistic fallacy when he/she computes a percentage of people in a nation who agree with a particular view on democracy and use the figure to determine the democracy of the political system of the country.
A difference exists between the dependent, independent and control variables, and includes the following. An independent variable is a variable that influences a phenomenon (Frankfort-Nachmias, & Nachmias, 2015). The variation of the independent variable does not depend on the other variable. The dependent variable refers to the presumed outcome as a result of a variation or manipulation in the independent variable (Frankfort-Nachmias, & Nachmias, 2015). With control variable, its role is determine the spurious relationship between the independent and dependent variables. On the other hand, a discrete variable is one whose unit is minimum size while a continuous variable can take any value since it does not have a minimum size.
The availability of parents in a clinic increases the anxiety level in children ages 2-4 years who receive treatment.
The availability of parents in a clinic decreases the anxiety level in children ages 2-4 years who receive treatment
Availability of parents in a clinic has no effect on the anxiety level of children ages 2-4 years who receive treatment.
The independent variable is the availability of parents in a clinic while the dependent variable is anxiety level of children.
A spurious relationship is one that the researcher uses variables to explain other than using those in the hypothesis (Frankfort-Nachmias, & Nachmias, 2015). On the same note, a relation is nonspurious when a researcher controls or prevents all other variables from influencing the study and produce an empirical outcome showing the relationship between variables.
Covariation encompasses union between two variables so that there is a sequential correspondence between disagreeing of one and the other. A good example of a covariation is income and education. For instance, in our society people who have studied receive more income. In the case of social scientists, setting up a relation entails determining whether there is a covariation between values of one variable and the other or more variables (Frankfort-Nachmias, & Nachmias, 2015). Similarly, it includes measuring the values of those variables.
In social science research, the direction of an association shows relations between variables. Variables can show either a positive relation or a negative relation (Frankfort-Nachmias, & Nachmias, 2015). With positive relations, we mean that an increase in one variable results in an increase in the other variable. For instance, education and income show a positive relation since when you spend more years in school studying, you will have increased income in the future. Having an interest in politics and political participation portray a positive relation. Individuals who have shown interest in politics tend to engage in more political activities. With negative relation, a researcher means that an increase in one variable decreases the other variable (Frankfort-Nachmias, & Nachmias, 2015). For instance, associations between rates of home mortgage loans and number of new loans is that an increase in interest rate leads to a decrease in the number of new loans.
A hypothesis can be defined as a tentative prediction of the relationship between independent and dependent variables (Frankfort-Nachmias, & Nachmias, 2015). The credibility of research hypotheses comes after a researcher has conducted an empirical test against the null hypothesis.
A good hypothesis shows clarity. A hypothesis must not contain ambiguous information (Frankfort-Nachmias, & Nachmias, 2015). When a researcher wants to test hypothesis empirically, all the variables involved must be defined.
A good hypothesis should be specific. A researcher should show accountability to ensure that the hypothesis is specific to the research question. The relations and conditions among the variables should be pointed out.
A good hypothesis should be testable. A researcher should be able to use the available methods to test the hypothesis. For instance, it is hard to test the hypothesis that subject a is 10 grams heavier than subject b without a scale.
A good hypothesis should be value free. The biases and values of a researcher have no room in scientific research (Frankfort-Nachmias, & Nachmias, 2015). The scientific hypothesis should be based on empirical data with no preferences.
Frankfort-Nachmias, C., & Nachmias, D. (2015). Research methods in the social sciences (8th ed.). New York, NY: Worth Publishers, a Macmillan Education Company.
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