About: Construct validity is a research topic. Over the lifetime, 24708 publications have been published within this topic receiving 1260243 citations.
Papers published on a yearly basis
TL;DR: The CES-D scale as discussed by the authors is a short self-report scale designed to measure depressive symptomatology in the general population, which has been used in household interview surveys and in psychiatric settings.
Abstract: The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.
TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.
Abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
TL;DR: In addition to making criteria-based diagnoses of depressive disorders, the PHQ-9 is also a reliable and valid measure of depression severity, which makes it a useful clinical and research tool.
Abstract: OBJECTIVE: While considerable attention has focused on improving the detection of depression, assessment of severity is also important in guiding treatment decisions. Therefore, we examined the validity of a brief, new measure of depression severity.
01 Nov 2000
TL;DR: In this article, the EQS program is used to test the factorial verifiability of a theoretical construct and its invariance to a Causal Structure using the First-Order CFA model.
Abstract: Contents: Part I: Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part II: Single-Group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part III: Multiple-Group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-Order CFA Model). Application 8: Testing for Latent Mean Differences (Second-Order CFA Model). Part IV: Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model.
01 Jan 2001
TL;DR: In this article, the authors present experiments and generalized Causal inference methods for single and multiple studies, using both control groups and pretest observations on the outcome of the experiment, and a critical assessment of their assumptions.
Abstract: 1. Experiments and Generalized Causal Inference 2. Statistical Conclusion Validity and Internal Validity 3. Construct Validity and External Validity 4. Quasi-Experimental Designs That Either Lack a Control Group or Lack Pretest Observations on the Outcome 5. Quasi-Experimental Designs That Use Both Control Groups and Pretests 6. Quasi-Experimentation: Interrupted Time Series Designs 7. Regression Discontinuity Designs 8. Randomized Experiments: Rationale, Designs, and Conditions Conducive to Doing Them 9. Practical Problems 1: Ethics, Participant Recruitment, and Random Assignment 10. Practical Problems 2: Treatment Implementation and Attrition 11. Generalized Causal Inference: A Grounded Theory 12. Generalized Causal Inference: Methods for Single Studies 13. Generalized Causal Inference: Methods for Multiple Studies 14. A Critical Assessment of Our Assumptions