Correlation in Research
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Questions and Answers

What type of research design is used in 'Correlation'?

Correlational Research Designs

What is the main focus of the research design 'Correlation'?

To explore and analyze the relationship between two or more variables.

What is the alternative phrase for 'Variable X'?

Predictor Variable

Correlation research designs can infer causation.

<p>False (B)</p> Signup and view all the answers

Which of the following is NOT a valid term for use in correlation research?

<p>Causation between variables (C)</p> Signup and view all the answers

A perfect positive correlation is represented by r calc = +1.

<p>True (A)</p> Signup and view all the answers

A 'r calc' value of 0 indicates a strong negative correlation.

<p>False (B)</p> Signup and view all the answers

'Pearson's r' is used for analyzing quantitative dependent variables including interval and ratio.

<p>True (A)</p> Signup and view all the answers

Spearman Rank Order Correlation Coefficient is used for analyzing quantitative dependent variables.

<p>False (B)</p> Signup and view all the answers

What type of research design is 'Simple Linear Regression'?

<p>Correlational</p> Signup and view all the answers

What is the goal of Simple Linear Regression?

<p>To develop a predictive equation that can predict the values of a dependent variable.</p> Signup and view all the answers

Flashcards

Correlation

A statistical relationship between two variables, showing how they tend to change together, but not implying causation.

Positive Correlation

As one variable increases, the other variable tends to increase as well.

Negative Correlation

As one variable increases, the other variable tends to decrease.

Correlation Coefficient (r)

A numerical value indicating the strength and direction of a correlation. Ranges from -1 to +1.

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Perfect Positive Correlation

r = +1; a strong and direct correlation. Variables move exactly in sync.

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Perfect Negative Correlation

r = -1; a strong and inverse correlation. Variables move in opposite directions.

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No Correlation

r = 0; there's no relationship between variables.

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Causation vs. Correlation

Correlation shows a relationship, but does not prove that one variable causes the other.

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Pearson's r

A statistical test for measuring the linear relationship between two quantitative variables.

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Spearman Rank Order Correlation

A test for measuring the relationship between two ordinal variables.

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Simple Linear Regression

A correlational method to predict one variable from another.

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Dependent Variable

The variable you're trying to predict or explain in a regression.

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Predictor Variable

The variable used to predict the dependent variable.

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Scatterplot

A graph used to display the relationship between two variables by plotting data points.

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Regression Line

The line on a scatterplot that best fits the data points.

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Linear Regression Formula

Y = bX + a; an equation to find the relationship between two variables.

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Slope (b)

The rate of change of the dependent variable (Y) for a unit change in the predictor (X).

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Y-intercept (a)

The value of Y when X is zero; where the line crosses the Y-axis on a graph.

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Study Notes

Correlation

  • Correlational research designs explore relationships between variables.
  • Correlation does not imply causation. There is no cause-and-effect relationship between variables.
  • To explore the correlation between variables, graphs are often used (e.g. scatterplots).
  • Scatterplots show the relationship between variables X and Y and their direction

Types of Correlation

  • Positive Correlation: As one variable increases, the other variable tends to increase. Example: Increased hours of study may correlate with improvement in exam scores.
  • Negative Correlation: As one variable increases, the other variable tends to decrease. Example: Increased time spent playing video games may correlate with decreased time spent on homework.
  • No Correlation: No apparent relationship between variables. Example: No relationship between shoe size and intelligence.

Variables

  • Variable X: One of the variables being examined for a relationship.
  • Variable Y: Another variable being examined for a relationship.
  • Variable Z (example): A third variable that might influence the relationship between X and Y. The relationship between X and Y could be correlated to Variable Z rather than to each other,

Statistical Assessments

  • Pearson's r: A measure of the strength and direction of a linear relationship between two quantitative variables (interval or ratio).
  • Spearman Rank Order Correlation Coefficient: A measure of the strength and direction of a relationship between two ordinal variables.

Simple Linear Regression

  • Predicting values of a dependent variable based on a single predictor variable.
  • The linear regression formula allows the researcher to determine a relationship that can be used for prediction.
  • Example: predicting a student's final exam score, given the student's score from mid-term exam.
  • Use a predictor variable (X) to predict a dependent variable (Y).

Data Visualization

  • Scatterplots are used to visualize the relationship between two variables. The plot shows the data points as individual dots. The line connecting most data points illustrates the correlation.

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Correlation and Regression Lecture PDF

Description

This quiz explores the concept of correlation in research designs, including the types of correlation such as positive, negative, and no correlation. It emphasizes that correlation does not imply causation and illustrates how scatterplots can be used to visualize relationships between variables. Test your understanding of these fundamental concepts!

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