Research Methodology and Methods – Miscellaneous Questions – Sociology – Solved PYQs – UGC NET

1. There are two statements, one is Assertion (A) and the other is Reason (R). Select the correct answer from the codes given below: (JUNE 2012)

Assertion (A): Qualitative methods are often criticized for failing to meet the same standards of reliability.

Reason (R): The procedures used to collect data in qualitative studies are precise and accurate.

Codes:

(A) Both (A) and (R) are true and (R) is the correct explanation of (A).
(B) Both (A) and (R) are true but (R) is not the correct explanation of (A).
(C) (A) is true but (R) is false.
(D) (A) is false but (R) is true.

Correct Answer: (C) (A) is true but (R) is false.

Qualitative research methods are frequently criticized for not achieving the same level of reliability associated with quantitative research. Reliability refers to the consistency and repeatability of research findings when the same procedures are applied under similar conditions. Qualitative research often relies on methods such as participant observation, in-depth interviews, ethnography, case studies, and life histories, where data collection and interpretation are closely linked to the researcher’s interaction with participants and the social context being studied. Because social situations and human experiences are dynamic and context-dependent, it is often difficult to replicate qualitative studies in exactly the same way, leading to concerns about reliability.

The Assertion (A) is true because qualitative methods are indeed often criticized for failing to meet conventional standards of reliability that are commonly applied in quantitative research. Critics argue that the subjective nature of qualitative inquiry may lead to variations in interpretation and findings across researchers and settings.

The Reason (R) is false because qualitative research procedures are generally not characterized by highly standardized, rigid, and precise measurement techniques. Instead, qualitative methods emphasize flexibility, contextual understanding, depth of inquiry, and the exploration of meanings and experiences. Researchers may adapt questions, observations, and interactions as the study progresses. While qualitative research can be systematic and rigorous, it does not rely on the same kind of precision and standardization that are typical of quantitative measurement procedures.

Qualitative researchers address concerns about reliability through techniques such as triangulation, member checking, peer review, audit trails, thick description, and reflexivity. These approaches enhance the trustworthiness and credibility of qualitative findings while recognizing the complexity of human behavior and social reality. Since the assertion is correct and the reason is incorrect, the appropriate answer is Option (C).

2. Calculation of number of deaths per thousand people falls under the measure of (JUNE 2012)

(A) Nominal scale
(B) Ordinal scale
(C) Ratio scale
(D) Interval scale

Correct Answer: (C) Ratio scale

The calculation of the number of deaths per thousand people, commonly known as the death rate or mortality rate, falls under the ratio scale of measurement. A ratio scale is the highest level of measurement in statistics and social research because it possesses all the characteristics of the other scales while also having a true or absolute zero point.

A ratio scale has four important properties: identification, ordering, equal intervals, and a true zero. The presence of a true zero means that the absence of the measured characteristic can be meaningfully represented. In the case of deaths, a value of zero indicates that no deaths have occurred. Since death rates are expressed numerically and can take a true zero value, they satisfy the requirements of ratio measurement.

The ratio scale allows researchers to perform all mathematical operations, including addition, subtraction, multiplication, and division. Meaningful comparisons can also be made using ratios. For example, a mortality rate of 20 deaths per thousand people is twice as high as a mortality rate of 10 deaths per thousand people. Such proportional comparisons are possible only with ratio-scale measurements.

Option (A) Nominal scale is incorrect because nominal measurement is used merely for classification into categories without any order or numerical significance. Examples include gender, religion, and blood groups. Option (B) Ordinal scale is incorrect because ordinal scales provide ranking or ordering but do not ensure equal intervals between ranks. Examples include class positions and satisfaction levels. Option (D) Interval scale is incorrect because interval scales have equal intervals but lack a true zero point. Temperature measured in Celsius or Fahrenheit is a common example, since zero does not indicate the complete absence of temperature.

In demographic and social statistics, measures such as birth rates, death rates, fertility rates, income, population size, age, weight, and height are generally measured on a ratio scale. This scale provides the greatest flexibility for statistical analysis and enables researchers to make precise quantitative comparisons across populations and social groups.

3. Attribute is related to (DEC 2012)

(A) Qualitative variable
(B) Quantitative variable
(C) Constant
(D) None of the above

Correct Answer: (A) Qualitative variable

In statistics and research methodology, an attribute refers to a characteristic or quality that cannot be measured numerically but can be classified into categories based on its presence, absence, or type. Attributes are associated with qualitative variables, which describe the nature, quality, or category of a phenomenon rather than its numerical magnitude.

A qualitative variable represents characteristics such as gender, religion, caste, marital status, nationality, literacy status, occupation category, and political affiliation. These variables do not express quantities in numerical terms; instead, they classify individuals or objects into distinct groups. For example, a person’s religion may be categorized as Hindu, Muslim, Christian, Sikh, or another category. Such classifications are attributes because they describe qualities rather than measurable amounts.

Option (A) is correct because attributes are inherently qualitative in nature. Statistical analysis of attributes often involves counting frequencies and examining distributions across categories rather than calculating arithmetic measures such as means or standard deviations.

Option (B) Quantitative variable is incorrect because quantitative variables are measurable in numerical terms. Examples include age, income, height, weight, population size, and examination scores. These variables are generally referred to as variables rather than attributes because they can vary in magnitude and can be subjected to mathematical operations.

Option (C) Constant is incorrect because a constant is a characteristic that remains unchanged throughout a study or analysis. An attribute may vary from one individual or group to another and is not necessarily constant. Option (D) None of the above is incorrect because the relationship between attributes and qualitative variables is well established in statistical terminology.

In statistical classification, data are often divided into attributes and variables. Attributes represent qualitative characteristics and are commonly analyzed through frequency distributions, percentages, proportions, and association measures. Variables represent quantitative characteristics and are analyzed using statistical techniques such as measures of central tendency, dispersion, correlation, and regression. Understanding the distinction between attributes and variables is important for selecting appropriate methods of data collection, classification, and analysis in social research.

4. Coefficient of correlation (r) indicates the extent of relationship between (DEC 2012)

(A) Two qualitative variables
(B) Two quantitative variables
(C) One qualitative variable and another quantitative variable
(D) All the above

Correct Answer: (B) Two quantitative variables

The coefficient of correlation (r) is a statistical measure used to determine the degree and direction of relationship between two quantitative variables. The most widely used correlation coefficient is the Pearson Product-Moment Correlation Coefficient, developed by Karl Pearson. This coefficient measures how changes in one numerical variable are associated with changes in another numerical variable.

The value of the correlation coefficient ranges from −1 to +1. A value of +1 indicates a perfect positive relationship, meaning that as one variable increases, the other also increases proportionately. A value of −1 indicates a perfect negative relationship, meaning that as one variable increases, the other decreases proportionately. A value of 0 suggests the absence of a linear relationship between the variables.

Examples of quantitative variables suitable for correlation analysis include age and income, height and weight, study hours and examination scores, or education level measured in years and occupational earnings. Since these variables are expressed numerically, the strength and direction of their relationship can be measured using the coefficient of correlation.

Option (A) Two qualitative variables is incorrect because qualitative variables are categorical in nature and are generally analyzed using measures of association rather than Pearson’s correlation coefficient. Option (C) One qualitative variable and another quantitative variable is also incorrect because the standard correlation coefficient is designed primarily for numerical variables measured on interval or ratio scales. Option (D) All the above is incorrect because the coefficient of correlation (r) specifically measures relationships between quantitative variables and is not universally applicable to all combinations of variable types.

In social science, economics, psychology, education, and demographic research, correlation analysis is widely used to examine relationships among measurable characteristics. It helps researchers identify patterns of association, test hypotheses, and understand the extent to which changes in one variable are related to changes in another. The coefficient of correlation remains one of the most important tools for studying relationships between quantitative variables in statistical research.