Establishing Construct Validity using a Correlation Matrix with Survey Data
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This document discusses using a correlation matrix to provide construct-related evidence of validity. It shows high inter-item correlations between items targeting reading efficacy and items targeting math efficacy, demonstrating convergent validity. It also shows low inter-scale correlations between the reading efficacy items and math efficacy items, demonstrating divergent validity by showing the scales measure different constructs as intended.
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Validity
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1. Content validity refers to how well a test measures the specific construct it aims to assess. A test needs to be related to the relevant class content.
2. Criterion-related validity is the degree of agreement between a test and an independent, reliable standard. There are two types: concurrent and predictive validity.
3. Construct validity provides evidence that test items measure the intended underlying abilities. Think-aloud and retrospection methods can provide evidence of construct validity.
Validity in scoring and face validity are also discussed. To improve validity, test specifications and a representative sample of content should be used, and scoring should directly relate to what
Content validity
This document discusses content validity in assessment. It defines content validity as the extent to which an assessment measures the intended curriculum content. The key points made include:
1. Content validity is ensured when test questions represent the entire scope and range of curriculum topics.
2. A content validity sheet can be used to allocate test questions proportionately across different chapters and units based on their weightage.
3. Maintaining content validity helps develop exam papers that accurately reflect the curriculum and promote student learning rather than failure.
Validity
This document discusses different types of test validity. It defines validity as the extent to which a test measures what it is intended to measure. There are three main types of validity discussed: content validity, which involves comparing test items to learning objectives; criterion-related validity, which correlates test scores with an external criterion; and construct validity, which examines if test results align with theoretical constructs. Providing validity evidence is important for ensuring tests accurately measure the intended skills or attributes.
Malhotra17
This chapter discusses correlation and regression analysis. It covers product-moment correlation, partial correlation, nonmetric correlation, bivariate regression, multiple regression, and the statistics associated with regression analysis. The key steps in conducting bivariate regression are:
1) Plotting a scatter diagram of the variables
2) Formulating the general regression model
3) Estimating the parameters of the model
Construct validity factor analysis
This document discusses the results of an exploratory factor analysis (EFA). The EFA identified three factors, with certain items loading onto each factor. Items loading over 0.300 or 0.400 on the first factor. Other items loaded onto the second factor, and remaining items loaded onto the third factor identified by the analysis.
Louzel Report - Reliability & validity
The document discusses reliability and validity in research tools. It defines reliability as consistency of data collection and validity as measuring what is intended. It discusses different types of reliability - stability over time, equivalence of alternate forms, and internal consistency. It also discusses different types of validity - content, criterion, and construct validity. Factors like threats to groups, regression, time, and respondents' history can affect validity. Reliability ensures consistency while validity determines accuracy of what is measured.
Correlation and Regression
Chapter 14 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
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Learn About Range - Copyright updated
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
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The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
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The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
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- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updated
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
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The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
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The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
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Nature of the data (spread) - Copyright updated
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
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The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updated
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
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The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
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The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
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The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
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The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
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The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
Relationship nature of data
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
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The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
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The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
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The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
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Establishing Construct Validity using a Correlation Matrix with Survey Data
- 1. Correlation Matrix – Construct-Related Evidence of Validity • • 1
- 2. Correlation Matrix – Construct-Related Evidence of Validity 2
- 3. Correlation Matrix – Construct-Related Evidence of Validity 3
- 4. Correlation Matrix – Construct-Related Evidence of Validity 4
- 5. Correlation Matrix – Construct-Related Evidence of Validity 5 These items target “Reading Efficacy or Confidence”
- 6. Correlation Matrix – Construct-Related Evidence of Validity 6 These items target “Math Efficacy or Confidence”
- 7. Correlation Matrix – Construct-Related Evidence of Validity 7
- 8. Correlation Matrix – Construct-Related Evidence of Validity 8
- 9. Correlation Matrix – Construct-Related Evidence of Validity 9 Here we have High Inter-Item Correlation for these three items targeting “Reading Efficacy” (.70 or above is considered high)
- 10. Correlation Matrix – Construct-Related Evidence of Validity 10 Here we have High Inter-item Correlation for these three items targeting “Math Efficacy”
- 11. Correlation Matrix – Construct-Related Evidence of Validity 11 This is evidence of “Convergent Validity” for each of these constructs because the items within each scale are highly correlated
- 12. Correlation Matrix – Construct-Related Evidence of Validity 12 Low INTER-Scale Correlation between Scale 1 items and Scale 2 items.
- 13. Correlation Matrix – Construct-Related Evidence of Validity 13 This is an example of “Divergent Validity” because the scales diverge from one another.
- 14. Correlation Matrix – Construct-Related Evidence of Validity 14 Meaning, items targeting “Reading Confidence” do not correlate well with items targeting “Math Confidence”
- 15. Correlation Matrix – Construct-Related Evidence of Validity 15 Meaning, items targeting “Reading Confidence” do not correlate well with items targeting “Math Confidence” This is what you would expect, since the items target different traits or constructs.
Editor's Notes
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