Difference Between Reliability and Validity

7/28/2019 Difference Between Reliability and Validity

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Difference Between Reliability and

Validity Categorized under Miscellaneous | Difference Between Reliability and

Validity

Reliability vs validity

Reliability and validity seem to be synonymous, but they do not mean the

same thing. Theyare actually different things, different terms when they are

explained in a technical manner. These terms are often used on scholastic

outputs such as thesis studies, term papers, research papers, and the likes. Soto avoid confusion, here are the differences of the two.

Reliability is when your measurement is consistent. It means if you are using a

certain kind of instrument for a test and the results on the subjects you are

testing is the same for the first and second try, then it is considered reliable.

There are two ways in estimating whether a certain thing is reliable or not. The

first way is the test or retest and the other is the internal consistency. The testand retest is quite easy. You simply test an idea twice, test 1 and test 2. It must

be measured twice in different times, then compare the similarities of the

results of the two tests. Then, if the results of the two tests are the same, it

means that certain measurement is reliable.
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The next way in estimating reliability is internal consistency. This canbe done

by questioning. Make different sets of question that can measure the same

factor. Let this be answered by different people or different groups. And even

if different people answered these different questions, but still came out withthe right thought, then it must be reliable.

This is the definition of reliability. Now to differentiate it with validity, it is

best to define validity as well. In this way, the confusion between the two

terms may be fixed. With this it will be easier to distinguish the two from each

other.

If reliability is more on consistency, validity is more on how strong the

outcomes of the hypothesis are. It answers the question are we right? This

means if there is a social experiment on a class concerning discipline and then

after the experiment the class became more disciplined, then the strength of

the conclusion is very strong. This means the validity too is strong.

Validity is categorized into four types, the conclusion, internal validity,

construct validity, and external validity. The conclusion validity is focused

more on the relationship between the outcome and the program. Internalvalidity is more on asking what kind of relationship is there between the

outcome and the program. Construct validity analyzes how strong the

outcome is. External validity is focused more on the general concept of the

outcome.

These are some of the differences between reliability and validity.

SUMMARY:

1.

Reliability is more on the consistency of a measurement, while validity is

focused more on how strong the outcome of the program was.

2.
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Reliability is easier to determine, because validity has more analysis just to

know how valid a thing is.

3.

Reliability is determined by tests and internal consistency, while validity has

four types, which are the conclusion, internal validity, construct validity, and

external validity.

Read more: Difference Between Reliability and Validity | Difference Between |

Reliability vs

Validity http://www.differencebetween.net/miscellaneous/difference-between-reliability-and-validity/#ixzz2SgNio2bX

A ordinal variable, is one where the order matters but not the difference between values. For example,you might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7means more pain that a score of 5, and that is more than a score of 3. But the difference between the 7and the 5 may not be the same as that between 5 and 3. The values simply express an order. Anotherexample would be movie ratings, from to

A categorical variable, also called a nominal variable, is for mutual exclusive, but not ordered, categories.For example, your study might compare five different genotypes. You can code the five genotypes withnumbers if you want, but the order is arbitrary and any calculations (for example, computing an average)would be meaningless.

Discrete variables describe a finite set of conditions and take values from a finite, usually small, set ofstates. An example of a discrete variable is Success of the venture, defined in our tutorial on Bayesiannetworks. This variable can take two values: Success and Failure. Another example might be a variableHepatitis-B, assuming values True and False. Yet another is Financial gain assuming three values: $10K,$20K, and $50K.
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Continuous variables can assume an infinite number of values. An example of a continuous variable isBody temperature, assuming any value between 30 and 45 degrees Celsius. Another might be Financialgain, assuming any monetary value between zero and $50K.__________

1) Ordinal Variables

A discrete ordinal variable is a nominal variable, but its different states are ordered in a meaningfulsequence. Ordinal data has order, but the intervals between scale points may be uneven. Because oflack of equal distances, arithmetic operations are impossible, but logical operations can be performed onthe ordinal data.

2) Nominal Variables

Nominal variables allow for only qualitative classification. That is, they can be measured only in terms ofwhether the individual items belong to certain distinct categories, but we cannot quantify or even rank

order the categories: Nominal data has no order, and the assignment of numbers to categories is purelyarbitrary.

3) Discrete Variables

4) Continuous Ordinal VariablesThey occur when the measurements are continuous, but one is not certain whether they are on a linearscale, the only trustworthy information being the rank order of the observations. For example, if a scale istransformed by an exponential, logarithmic or any other nonlinear monotonic transformation, it loses itsinterval - scale property. Here, it would be expedient to replace the observations by their ranks.

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