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Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Related Experiment Video

Updated: Mar 23, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

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Predicting dropout using student- and school-level factors: An ecological perspective.

Laura Wood1, Sarah Kiperman1, Rachel C Esch1

  • 1Department of Counseling and Psychological Services, Georgia State University.

School Psychology Quarterly : the Official Journal of the Division of School Psychology, American Psychological Association
|April 1, 2016
PubMed
Summary

Student academic achievement, family socioeconomic status (SES), and school size are key predictors of high school dropout. Understanding these factors is crucial for developing effective prevention strategies.

Related Experiment Videos

Last Updated: Mar 23, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Area of Science:

  • Educational Psychology
  • Sociology of Education

Background:

  • High school dropout is linked to adverse outcomes like unemployment, incarceration, and mortality.
  • Dropout rates are influenced by a complex interplay of individual and environmental factors.

Purpose of the Study:

  • To investigate student- and school-level predictors of high school dropout using an ecological perspective.
  • To inform the development of targeted prevention and intervention strategies.

Main Methods:

  • Utilized the Education Longitudinal Study of 2002 dataset, including 14,106 sophomores from 684 schools.
  • Employed hierarchical generalized linear modeling to analyze student- and school-level predictors.
  • Controlled for demographic and school characteristics in the final model.

Main Results:

  • Significant student-level predictors of dropout included academic achievement, retention, sex, family socioeconomic status (SES), and extracurricular involvement.
  • Significant school-level predictors identified were school SES and school size.
  • Race/ethnicity, special education status, nativity, English language proficiency, school urbanicity, and region did not significantly predict dropout after controlling for other variables.

Conclusions:

  • Academic performance, family background, and school characteristics significantly influence high school dropout rates.
  • Findings support a multitiered intervention model for dropout prevention.
  • Further research can refine strategies for supporting at-risk students.