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Korvikt [17]
3 years ago
7

While analyzing the data from a personality test he had administered, Dr. Smith finds that individuals that score higher on ques

tions assessing their talkativeness also had higher scores on items related to enjoying social gatherings. He ultimately predicted that these characteristics are related to the personality trait of extraversion. To find the correlation between these characteristics, Dr. Smith MOST clearly used a technique known as _______________.
Social Studies
1 answer:
Katena32 [7]3 years ago
3 0

Answer:

Factor analysis.

Explanation:

Personality can be defined as a unique blend of various characteristics or traits such as mental, physical, emotional and social with respect to an individual. Thus, these characteristics, qualities or traits influences the way a person acts, thinks, feel and behave in relation with their environment and others.

This ultimately implies that, a person's personality is unique to him or her and as such differentiates them entirely from another person based on thought, emotional and behavioral patterns.

Some of the factors that influence the personality of a person includes the following;

I. Hereditary.

II. Environment.

III. Culture.

IV. Family background.

In this scenario, Dr. Smith discovered that individuals with higher score on questions assessing their level of talkativeness also had higher scores on items that are related to enjoying social gatherings. Subsequently, in his prediction he stated that these characteristics are related to the personality trait of extraversion.

Hence, to determine the correlation between these characteristics, Dr. Smith most clearly used a technique known as factor analysis.

In Statistics, factor analysis can be defined as a statistical technique that is typically used to reduce or shrink a large number of observed, correlated variables with respect to some unobserved variables (factors) that are usually lower in size, so as to have a data set that is easier to manage, understand and work with. Thus, it is used to extract hidden patterns known as maximum common variance from a data set and grouping them as a common score.

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