Quantitative Research MCQ Quiz in मल्याळम - Objective Question with Answer for Quantitative Research - സൗജന്യ PDF ഡൗൺലോഡ് ചെയ്യുക

Generalization is widely recognized as a quality criterion. It is an act of thinking that involves drawing broad inferences from specific observations.

Key Points

Assertion A: Generalisation of a finding is possible only in quantitative studies. 

• Generalization is an important aspect of the scientific method. In an ideal world, you would sample the entire population to test a theory.
• In an environment where evidence for enhancing practice is valued highly, both qualitative and quantitative researchers pay close attention to generalization in connection to knowledge claims.
• Many qualitative research leaders have come to appreciate the need of addressing generalization in order to guarantee that qualitative research findings are recognized as important sources of evidence for practice.

Hence, Assertion A is true.

Reason R: Quantitative studies make use of deductive reasoning.

• Quantitative research is the process of collecting and interpreting numerical data. 
• It can be used to look for patterns and averages, make predictions, test causal linkages, and extrapolate results to larger groups.
• Quantitative studies are associated with the deductive approach for theory development.

So, the Reason is true but does not explain the assertion.

Therefore, Both A and R are true but R is NOT the correct explanation of A.

Additional Information

• Quantitative research deals with standardized data gathering and generalizing conclusions.
• Quantitative approaches allow researchers to measure variables and test hypotheses in a systematic way.
•  Quantitative research methodologies are used for descriptive, correlational, or experimental research. 

Quantitative research:

• Quantitative research is the process of collecting and analyzing numerical data.
• It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations
• Quantitative data collection methods are much more structured than Qualitative data collection methods.
• Quantitative data collection methods include various forms of surveys – online surveys, paper surveys, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.

This method of research can be used for descriptive, correlational, or experimental research.

• In descriptive research, you simply seek an overall summary of your study variables.
• In correlational research, you investigate relationships between your study variables.
• In experimental research, you systematically examine whether there is a cause-and-effect relationship between variables.

Hence, 1, 3, and 4 are correct regarding quantitative research.

 Key Points

Study the application of theory

• Experimental researches are specifically designed to assess the efficacy or outcomes of applying certain theories or principles in practice.
• These experiments aim to manipulate one or more independent variables to observe their effect on one or more dependent variables, thereby allowing researchers to identify cause-and-effect relationships.
• Experimental designs often involve control groups and experimental groups to compare outcomes, ensuring that the effects observed can indeed be attributed to the manipulation of the independent variable(s), supporting or refuting theoretical frameworks with empirical evidence.

Establish association between variables (Incorrect for this context)

• This is more closely associated with correlational research, which aims to identify and analyze the relationships or associations between two or more variables without manipulating them.
• Correlational research can suggest associations but cannot establish causality.

Describe variables (Incorrect for this context)

• Descriptive research is aimed at describing characteristics of a population or phenomenon. It focuses on "what is" rather than "why it is," making it distinct from experimental research, which seeks to explore cause-and-effect relationships by manipulating variables.

Study trend analysis (Incorrect for this context)

• Trend analysis involves analyzing historical data to predict future occurrences and identify patterns over time.
• Although valuable in many contexts, this approach does not typically involve the manipulation of variables to test theories in the manner that experimental research does.

The correct answer is: (A), (C) and (D) only.

• (A) testing the significance of difference of two sample means
  • ​This is a primary use of the student's t-test.
• (C) testing the significance of correlation coefficient -
  • Although more directly tested via methods specifically designed for correlation coefficients, understanding the relationship between two variables can involve t-tests under certain conditions, especially in the context of Pearson's correlation coefficient in small samples.
• (D) testing the significance of a regression coefficient -
  • T-tests are used in regression analysis to determine if the coefficients in a linear regression model are significantly different from zero, indicating that there is a significant relationship between the independent and dependent variables.

The correct answer is option 3.

Key Points

• Active manipulation of an independent variable is the key defining characteristic of an experimental research.
• The characteristic of experimental research is where the researcher objectively observes a phenomena which is made to occur in a strictly controlled situation where one or more variables are systematically changed by manipulation.
• Active manipulation is not involved in any other types of research.
• Because of this experimental research provides the strongest evidence of all the research methods about the existence of cause and effect relationships.

Therefore, correct answer is experimental research

The experimental method

• The experimental method involves manipulating one variable to determine if changes in one variable cause changes in another variable. This method relies on controlled methods, random assignment, and the manipulation of variables to test a hypothesis.
• The experimental method is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and measures any change in other variables. 

Residual group 

• Residuals are estimates of experimental error obtained by subtracting the observed response from the predicted response. The predicted response is calculated from the chosen model after all the unknown model parameters have been estimated from the experimental data.
• Residuals help to determine if a curve (shape) is appropriate for the data. A residual is a difference between what is plotted in your scatter plot at a specific point, and what the regression equation predicts "should be plotted" at this specific point.
• A residual is a vertical distance between a data point and the regression line. Each data point has one residual. They are positive if they are above the regression line and negative if they are below the regression line. In other words, the residual is the error that isn't explained by the regression line.
• Residual analysis is used to assess the appropriateness of a linear regression model by defining residuals and examining the residual plot graphs.
• A residual plot is typically used to find problems with regression. Some data sets are not good candidates for regression, including Heteroscedastic data (points at widely varying distances from the line). Data that is non-linearly associated.

Single group experiment

• The single group design is a design in which a group of subjects are administered treatment and then measured (or observed). No attempt is made to randomly assign subjects to the groups, nor does the design provide for any additional groups as comparisons.
• We define a single group study as a study that consists of only a single group of subjects included in the study design, in which all subjects received a single intervention and the outcomes are assessed over time (i.e., not a cross-sectional study). These studies may be prospective or retrospective cohort studies.

Parallel group experiment

• A parallel study is a type of clinical study where two groups of treatments, A and B, are given so that one group receives only A while another group receives only B. Other names for this type of study include "between patient" and "non-crossover".
• A parallel-group design is an experimental study design in which each subject is randomized to one of two or more distinct treatment/intervention groups. Those who are assigned to the same treatment are referred to as a treatment group.

Rational group experiment

• A rational group can thus be defined as a group in which all elements are rational elements. For any element of infinite order, the element and its inverse must be conjugate.

     Therefore Option 2 is the correct answer.

The research method which focuses on establishing causal relationships with controls among variables - independent, moderator, and dependent, is called the Experimental method.

Key Points

Experimental Research:

• In this, the Researcher directly manipulate the independent variable in order to assess its effect on the dependent variable
• Researcher utilizes the principle of randomization in research on the basis of which they can infer that other things remaining equal the effect is a result of manipulation of cause
• It is easier to interpret the relationship between the independent and dependent variables as they can manipulate the independent variables and see their impact on the dependent variable
• Direct cause and effect relationship study is established in experimental research.

Therefore option 4 is the correct answer.

1. Ex post facto method: An ex post facto research design is a method in which groups with qualities that already exist are compared on some dependent variable. The assignment of subjects to different groups is based on whichever variable is of interest to the researchers.

2. A survey is a research method used for collecting data from a predefined group of respondents to gain information and insights into various topics of interest.

3. The case study method is a learning technique in which the student has faced a particular problem, the case. The case study facilitates the exploration of a real issue within a defined context, using a variety of data sources.

The correct answer is (B) and (D) Only.

Key Points 

To determine which p-values are significant at a 5% level of significance, we compare them to the significance level (α) of 0.05.

If the p-value is less than or equal to the significance level (p ≤ α), we reject the null hypothesis and consider the result statistically significant. Otherwise, if the p-value is greater than the significance level (p > α), we fail to reject the null hypothesis, and the result is not considered statistically significant.

Let's analyze each option:

(A) 0.5 - Not significant (p > α)
(B) 0.04 - Significant (p ≤ α)
(C) 0.95 - Not significant (p > α)
(D) 0.002 - Significant (p ≤ α)
(E) 0.995 - Not significant (p > α)

Based on the comparison with the significance level (α = 0.05), the significant p-values are (B) 0.04 and (D) 0.002.

Additional Information 

• In hypothesis testing, the p-value represents the probability of observing the data (or more extreme results) given that the null hypothesis is true.
• When conducting hypothesis tests for correlation coefficients, such as Pearson's correlation coefficient (r), a low p-value indicates strong evidence against the null hypothesis, suggesting that there is a statistically significant relationship between the variables being studied.
• In this context, obtaining a significant p-value (p ≤ α) at a 5% level of significance (α = 0.05) indicates that the correlation coefficient observed in the sample is unlikely to have occurred by random chance alone.
• Therefore, researchers can infer that there is a statistically significant correlation between the variables in the population from which the sample was drawn.
• Conversely, non-significant p-values (p > α) suggest that the observed correlation coefficient is not statistically significant at the chosen level of significance.
• In such cases, researchers fail to reject the null hypothesis, indicating that there is insufficient evidence to conclude a significant relationship between the variables based on the sample data.
• Understanding the significance of p-values in hypothesis testing is crucial for interpreting the results of statistical analyses and drawing valid conclusions about the relationships between variables in research studies.

The correct response is I and II.

Key Points

Socioeconomic status - Socioeconomic status can be turned into a quantitative measure by assigning values to different components of it such as income, education, and occupation.

• For instance, income could be measured in exact amounts, education could be measured in years or degrees obtained, and occupation could be measured by the occupational prestige score.
• However, socioeconomic status also reflects non-quantifiable aspects like social class, status, or privilege, which are more subjective. They often come with complex social, cultural, and contextual factors.
• Given these aspects, qualitative methods of investigation such as interviews or ethnographic observation might offer more nuanced insights.
• So, it's a variable that can possess both quantitative and qualitative characteristics.

Marital status - Marital status is considered a nominal or categorical variable. It describes different states like single, married, divorced, or widowed.

• These categories do not have a numerical relationship with each other and their order does not typically matter, so it is hard to capture them in a meaningful quantitative way.
• We may assign numbers to each status for data processing purposes, but these numbers don’t hold arithmetic values. Hence, Marital status is more of a qualitative variable.

Additional Information

Age - Age is a clear example of a quantitative variable. It can directly be expressed in numbers such as years, months, days, hours, etc.

• Age is also an interval-level variable as the differences between two ages can be meaningful.
• Since it is measurable in numerical terms, there's less room for subjective or qualitative interpretation (apart from societal or cultural implications of different ages).

Height - Height is also an excellent example of a quantitative variable. It can be precisely measured in numerical scales like inches, feet, centimeters, or meters.

• It is a ratio-level variable, as it has a true zero point and the differences and ratios are both meaningful. So, just like age, height is typically not considered a qualitative variable as it is directly measurable and it doesn't leave much room for interpretation.

Quantitative Research Paradigm:

• It attempts to quantify, collect and analyze numerical data and focus on the links among a smaller number of attributes across varied cases.
• It is less interested in individuals than in common features of a group of people.
• It is centered around the study of variables that captures common attributes that are quantified by counting, scaling, or by assigning values to categorical data
• It involves experiments, surveys, testing, and structured content analysis.
• It is a systematic, rigorous research process involving precise assessment tools producing reliable and replicable data that is generalized to other contexts.
• Hypothetico-deductive Studies: In this, a controlled variable is used to establish a systematic, clear, and logical cause and effect relationship between factors and effects. It involves defining a problem and then hypothesizing a solution and then conducting the research via a controlled variable to accept or reject the hypothesis.

Therefore, the characteristic features of the Quantitative Research paradigm are:

A. It is hypothetico-deductive

C. It lays stress on generalizations to the population characteristics

D. It emphasizes numeric data from a large number of people