Chapter 7: Correlational Research and Surveys

Overview of Surveys

• Surveys often utilize correlational research methods.

• Importance of constructing effective surveys, focusing on reliability and validity.

Evaluating Items in Surveys

Examples of Survey Items

• Item Evaluation:

  • 1. Belonging to a Greek organization increases chances of success.

  • 2. Joining a fraternity or sorority will help achieve academic goals and meet like-minded individuals.

  • 3. Rushing a fraternity or sorority will aid in making friends.

  • 4. Fraternities and sororities are for students lacking intelligence.

Reliability of a Scale

Types of Reliability

• Internal Consistency Reliability: Measures the interrelation among individual items in a scale.

• Cronbach’s Alpha: Common statistical method for assessing internal consistency.

• Test-Retest Reliability: Assesses the stability of a measure over time; high correlation between Time 1 and Time 2 indicates reliability.

Further Types of Reliability

• Alternative-Form Reliability: Correlation between two different but similar measures of the same variable.

• Inter-Rater Reliability: Agreement between two or more raters observing the same behavior; often evaluated using the percentage agreement or kappa coefficient.

• Categorical Scales: Many scales fall into categorical rather than numeric categories.

Assessing Reliability in Practice

Examples

• Julie’s Mood Inventory Activity:

  • Uses two different mood questionnaires pre- and post-stressful events.

  • Assesses using alternative-form reliability based on correlations between the two different inventories.

• Rodger’s Depression Test:

  • Conducted on the same participants two months apart to evaluate test-retest reliability.

• Dr. Woolley’s Motivation Inventory:

  • Assesses internal consistency reliability by analyzing item interrelations within a single session.

• Professor X’s Knowledge Assessment:

  • Measures test-retest reliability by administering the same final exam twice before content instruction.

Validity of a Scale

Types of Validity

• Face Validity: The degree to which a scale appears to measure what it's intended to.

• Content Validity: The extent to which a scale covers the material necessary to measure the variable.

• Construct Validity: Evaluates how well a scale measures the intended construct, using:

  • Convergent Validity: Correlation with expected measures; based on theory.

  • Discriminant Validity: Lack of correlation with unrelated variables.

Criterion Validity

• Concurrent Validity: How well a measurement correlates with existing outcomes; often used to validate measures.

• Predictive Validity: How well a measurement forecasts future outcomes.

Correlation and Its Coefficients

Correlation Coefficient

• Indicates strength and direction of association between two variables.

• Ranges from -1.0 to +1.0, where:

  • 0 = no relationship

  • -1 = perfect negative relationship

  • +1 = perfect positive relationship

Key Concepts Related to Correlation

• Linearity: The degree to which the relationship resembles a straight line.

• Pearson r: Measures the correlation magnitude between two continuous variables.

Continuous vs. Categorical Variables

• Important distinction as causation implies correlation but does not equate to it.

• Example Correlations:

  • Crime rates and number of churches

  • Intelligence and shoe size

• Third Variable Problem and Temporal Precedence: Recognizing external factors influencing correlations.

Methods for Assessing Homelessness

Survey Methods

• Different methodologies for estimating rates of homelessness discussed:

  • Census counts

  • Service utilization (e.g., shelter bed counts)

  • Surveys, recognizing potential underestimates from excluded demographics (e.g., minors, lack of phone ownership).

Data Collection Across Nations

• Highlights of surveys conducted in various countries since 2001, assessing lifetime prevalence of homelessness using fixed and mobile survey methodologies.

Statistical Analysis in Correlational Research

Advanced Statistical Techniques

• Increasing use of sophisticated statistical techniques such as ANOVA, ANCOVA, multiple regression, and SEM in contemporary research.

Analysis of Age Differences in Homelessness

• Study evaluating age differences among homeless individuals, revealing gender and racial demographics across different age categories.

• Data highlighted age-related differences in experiences of homelessness and social support metrics.

Chapter 7: Correlational Research and Surveys

Overview of Surveys

Surveys are essential tools in the field of research, often utilizing correlational research methods to gather information from individuals. The design and construction of effective surveys are crucial, as they must focus on both reliability and validity to ensure that the data collected accurately reflects the constructs being studied.

Evaluating Items in Surveys

When creating surveys, specific items or statements are evaluated to gauge participants' opinions or behaviors. Below are examples of survey items related to Greek organizations:

• Belonging to a Greek organization increases chances of success: Participants may reflect on their experiences and the perceived benefits of networking and connection.

• Joining a fraternity or sorority will help achieve academic goals and meet like-minded individuals: This statement assesses beliefs about academic support within these organizations.

• Rushing a fraternity or sorority will aid in making friends: Evaluates social implications and bonding opportunities.

• Fraternities and sororities are for students lacking intelligence: This statement could assess social stigma and biases towards Greek life.

Reliability of a Scale

Reliability refers to the consistency of a measure, ensuring that it produces stable and consistent results under consistent conditions. Various types of reliability can be evaluated:

• Internal Consistency Reliability: This assesses the interrelation among individual items in a scale, ensuring that items measure the same construct.

  • Cronbach’s Alpha: A commonly used statistical method for assessing internal consistency, where a coefficient closer to 1 indicates higher reliability.

• Test-Retest Reliability: Evaluates the stability of a measure over time. A high correlation between two measurements taken at different times indicates reliable data.

• Alternative-Form Reliability: Measures correlation between two different but similar measures of the same variable.

• Inter-Rater Reliability: Assesses the degree of agreement among raters observing the same behavior. This can be evaluated using percentage agreement or kappa coefficient.

  • Categorical Scales: Often, many scales fall into categorical rather than numeric categories, affecting how reliability is interpreted.

Assessing Reliability in Practice

To illustrate the application of these reliability concepts, several examples are provided:

• Julie’s Mood Inventory Activity: Engages participants with two different mood questionnaires administered pre- and post-stressful events. It uses alternative-form reliability based on correlations between the two inventories to understand mood variation.

• Rodger’s Depression Test: Conducted on the same participants two months apart, this test evaluates test-retest reliability to determine the stability of depression measurements over time.

• Dr. Woolley’s Motivation Inventory: Analyzes internal consistency reliability through the interrelations among items compiled during a single session.

• Professor X’s Knowledge Assessment: To measure test-retest reliability, the same final exam is administered twice before content instruction to assess knowledge retention and learning effectiveness.

Validity of a Scale

Validity is critical in determining whether a scale truly measures the intended construct. Several forms of validity include:

• Face Validity: The extent to which a scale appears to measure what it intends. While subjective, it gives a preliminary insight into a scale's effectiveness.

• Content Validity: This involves examining whether a scale covers all relevant content areas necessary for measuring the variable in question.

• Construct Validity: Evaluates how well a scale measures the intended construct through:

  • Convergent Validity: Indicates correlation with expected measures based on established theory, enhancing predictive capabilities.

  • Discriminant Validity: Assesses lack of correlation with unrelated variables, confirming unique construct measurement.

• Criterion Validity: This includes two components:

  • Concurrent Validity: Examines how well a measurement correlates with outcomes measured simultaneously.

  • Predictive Validity: Assesses how well a measurement forecasts future outcomes based on current data.

Correlation and Its Coefficients

The correlation coefficient is a valuable statistic that indicates both the strength and direction of the association between two variables. It ranges from -1.0 to +1.0, where:

• 0 = no relationship

• -1 = perfect negative relationship

• +1 = perfect positive relationship

Key Concepts Related to Correlation

Key concepts include:

• Linearity: The degree to which the relationship between two variables resembles a straight line, affecting interpretability.

• Pearson r: A specific measure used for quantifying the correlation strength between two continuous variables, commonly used in statistical analysis.

• Continuous vs. Categorical Variables: Important distinction as causation implies correlation but does not equate to it.

Example Correlations include:

• Crime rates and the number of churches

• Intelligence and shoe size

• Correlation may be influenced by external factors, leading to a third-variable problem and temporal precedence: Recognizing extrinsic factors impacting correlations can clarify data interpretations.

Methods for Assessing Homelessness

Homelessness surveys employ different methodologies to estimate prevalence rates:

• Census counts to gather population data

• Service utilization, including shelter bed counts, to assess resource needs

• Surveys while recognizing potential underestimates from excluded demographics such as minors or individuals without telephone access.

Data Collection Across Nations

This section highlights surveys conducted across various countries since 2001, assessing the lifetime prevalence of homelessness. These surveys use both fixed and mobile methodologies, offering insights into global homelessness trends.

Statistical Analysis in Correlational Research

Recent trends show an increasing use of sophisticated statistical techniques in contemporary research, such as ANOVA, ANCOVA, multiple regression, and Structural Equation Modeling (SEM), elevating the capability of researchers to analyze complex data sets effectively.

Analysis of Age Differences in Homelessness

A study evaluating age differences among homeless individuals revealed significant gender and racial demographics across different age categories. Data highlighted age-related variations in the experiences of homelessness, illustrating disparities in social support and access to resources, therefore emphasizing the importance of tailoring interventions based on age-related needs.