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

Key Points

Assertion (Ass) states that quantitative variables are those variables about which essentially some quantity may be determined.

• This is a true statement because quantitative variables are those variables that can be measured numerically and are typically associated with numerical values. These variables are often used in statistics and research studies to describe, analyze, and draw conclusions about various phenomena.
• Examples of quantitative variables include height, weight, age, income, test scores, and ratings on a scale from 1 to 10. In each case, a numerical value can be assigned to represent the amount, size, or level of the measured variable.

Reason (R) states that any variable that can be ordered concerning magnitude is quantitative.

• This is also a true statement because variables that can be ordered in magnitude are typically associated with numerical values and can be measured quantitatively. For example, temperature, time, and distance can be ordered concerning magnitude and measured quantitatively.
• For example, the temperature can be ordered from lowest to highest and represented numerically using the Celsius or Fahrenheit scales. Similarly, time can be ordered from earliest to latest and can be represented numerically using units such as seconds, minutes, or hours. The distance can also be ordered concerning the magnitude and can be represented numerically using units such as meters or kilometres.

Therefore, both Assertion (Ass) and Reason (R) are true, and Reason (R) is the correct explanation of Assertion (Ass).

Reliability 

Reliability in statistical and psychometrics is the overall consistency of a measure. A measure is said to have a high reliability if it products similar results under consistent condition.

 Key Points Reliability Coefficient

• A reliability coefficient essentially measures consistency of scoring.
• A measure of the accuracy of a test or measuring instrument obtained by measuring the same individuals twice and computing the correlation of the sets of measures.
• The Reliability coefficient is represented by the term r, the correlation of a test with itself.
• Reliability coefficients are variance estimates, meaning that the coefficient denotes the amount of true score variance.
• On the basis of evaluating the reliability the reliability coefficients are as follows-

1. The Coefficient of stability is described as the extent to which a test varies as the result of factors associated with the particular time and occasion on which the test was administered.
2.  The Coefficient of Equivalence is described as the extent to which scores on a test can be generalized over different occasions. 
3. In statistics and research, Internal consistency is typically a measure based on the correlations between different items on the same test. It measures whether several items that propose to measure the same general construct produce similar scores.

Thus, option 3 is correct.

Additional Information Inter-class Correlation coefficient

Inter-rater reliability is expressed as inter-class correlation coefficient, it is the extent to which two or more raters (observers, coders, examiners) agree.

Hence, we can conclude that Coefficient of Contingencies is not the Reliability Coefficient. 

Key Points

• The dependent variable is the variable a researcher is interested in. The changes to the dependent variable are what the researcher is trying to measure with all their fancy techniques. In our example, your dependent variable is the person's ability to throw a ball. We're trying to measure the change in ball throwing as influenced by hunger.
• An independent variable is a variable believed to affect the dependent variable. This is the variable that you, the researcher, will manipulate to see if it makes the dependent variable change. In our example of hungry people throwing a ball, our independent variable is how long it's been since they've eaten.

Important Points

• A confounding variable is one you did not account for that can disguise another variable's effects. Confounding variables can invalidate your experiment results by making them biased or suggesting a relationship between variables exists when it does not.
• For example, if you are studying the relationship between exercise level (independent variable) and body mass index (dependent variable) but do not consider age's effect on these factors, it becomes a confounding variable that changes your results.
• If a variable cannot be controlled for, it becomes what is known as a confounding variable. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Thus Confounding is the  variable that is difficult to observe, define and control in educational research.

A variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item.

Key Points

• The dependent variable is the variable that is being measured or assessed in an experiment. Because that is what is being investigated, the participants' test scores would be the dependent variable in the study looking at how tutoring impacts test outcomes. "Dependent variable" signifies just that.
• A lot of variables have an impact on it. The (Independent Variable) causes the (Dependent Variable) to change, but it cannot cause the (Dependent Variable) to change (Independent Variable).
• A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable. In statistics, dependent variables are also called:
  • Response variables (they respond to a change in another variable)
  • Outcome variables (they represent the outcome you want to measure)
  • Left-hand-side variables (they appear on the left-hand side of a regression equation)
• The dependent variable is what you record after you’ve manipulated the independent variable. This measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Thus we can conclude that the dependent variable can be used to analyze the data.

An experiment has several types of variables, including a control variable (sometimes called a controlled variable).

• Variables are just values that can change. A good experiment only has two changing variables: the independent variable and the dependent variable. 

Key Points

• Dependent variables receive this name because, in an experiment, their values are studied under the supposition or demand that they depend, by some law or rule on the values of other variables.
• The independent variable,  are not seen as depending on any other variable in the scope of the experiment in question.
• A control variable is another factor in an experiment. It must be held constant. 
  • A variable may be thought to alter the dependent or independent variables, but may not actually be the focus of the experiment.
  • So that the variable will be kept constant or monitored to try to minimize its effect on the experiment. Such variables may be designated as either a "controlled variable", or "fixed variable".

​Hence, it can be concluded that in an experiment, making the value of other Variables zero or constant is called Control Variables.

There are different kinds of method in fashion for providing systematic, precise, and reliable information or data collection, and 'experimental method' is one of them.

• Experimental method refers to a method which is designed to study the interrelationship between an independent and a dependent variable under controlled conditions.

In the experimental method, the variable which is under the control of experiment is known as 'independent variable' as in this method:

• an independent variable is manipulated.
• all other variables except the independent variable are held constant.
• the manipulation of the independent variable on the dependent variable is observed.
• independent variable is controlled with the help of techniques like randomisation, matching, elimination, etc.

Hence, it could be concluded that in experimental method, the variable which is under the control of experiment is known as 'independent variable'.

Additional Information