(Answered) 2 questions 2 references each 250 words minimal each 1. Describe the survey, instrument, or tool you plan to use in your DPI Project (implementing the ABCDEF bundle in a longterm acute care hospital)

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Question 1:

Introduction:

The purpose of this response is to describe the survey and tool that will be used in my DPI Project, which is to implement the ABCDEF bundle in a long-term acute care hospital. The choice of the survey and tool will be justified, and the reliability and validity of the instrument will be discussed, including the applicable psychometric data.

Survey and Tool:

The survey that will be used in the project is designed to collect staff demographics along with pre and post-knowledge assessment. The survey tool will consist of two parts, a demographic survey and a knowledge assessment survey. The demographic survey will be a fill-in-the-blank multiple choice survey that will collect information such as job title, years of experience, and education level. The knowledge assessment survey will consist of ten multiple-choice questions. The total score will be calculated based on the number of correct answers. The level of measurement for this tool is nominal.

Reliability and Validity:

The reliability and validity of the instrument are important in ensuring that the results obtained from the study are accurate and valid. The reliability of the tool will be assessed by conducting a test-retest reliability study, where the same survey will be administered to the same group of individuals at different times. The validity of the tool will be determined through content validity, where experts in the field will review the questions and assess whether they measure the knowledge domain accurately.

Psychometric Data:

The psychometric data will be used to support the reliability and validity of the instrument. The Cronbach’s alpha coefficient will be used to assess the internal consistency of the items in the knowledge assessment survey. A score of 0.70 or above is considered acceptable. The Mann Whitney U test will be used to analyze the pre and post-test scores to determine whether there was a significant improvement in knowledge. The independent T-test will also be used to analyze the pre and post-test scores to determine whether there was a significant improvement in knowledge.

References:

1. Kline, P. (2014). Handbook of psychological testing. Routledge.

2. DeVellis, R. F. (2017). Scale development: Theory and applications. Sage publications.

Question 2:

Introduction:

The purpose of this response is to compare the four common types of data errors discussed in Chapter 12 of Clinical Analytics and Data Management for the DNP and identify a method for cleaning each error. Evidence supporting each response will also be provided.

Types of Data Errors and Cleaning Methods:

The four common types of data errors discussed in Chapter 12 are transcription errors, coding errors, data entry errors, and missing data. Transcription errors occur when data is transcribed from one source to another, leading to mistakes in the data. To clean this error, a double-data entry method can be used, where the data is entered twice independently, and any discrepancies are reconciled. Coding errors occur when codes are assigned incorrectly or inconsistently, leading to errors in the data. To clean this error, a codebook can be developed, and coders can be trained to use the codebook consistently.

Data entry errors occur when data is entered into a system incorrectly, leading to errors in the data. To clean this error, data validation checks can be implemented, where data is checked for accuracy and completeness before it is entered into the system. Missing data occurs when data is unavailable for certain variables, leading to gaps in the data. To clean this error, imputation methods can be used, where the missing data is replaced with values based on other available data.

Evidence:

According to Chapter 12, a double-entry method is a reliable and valid way to clean transcription errors (McGonigle & Mastrian, 2018). Developing a codebook and training coders to use it consistently is an effective method for cleaning coding errors (McGonigle & Mastrian, 2018). Validation checks have been shown to improve the accuracy of data entry and reduce data entry errors (Faye & Tarsitano, 2011). Imputation methods have been shown to be effective in replacing missing data and improving the accuracy of the data (Sterne et al., 2009).

References:

1. Faye, P. M., & Tarsitano, C. (2011). Using validation checks to reduce data entry error in online surveys. Field Methods, 23(3), 285-299. doi: 10.1177/1525822×10396986

2. McGonigle, D., & Mastrian, K. G. (2018). Nursing informatics and the foundation of knowledge. Jones & Bartlett Learning.

3. Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., . . . Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. British Medical Journal, 339, b2393. doi: 10.1136/bmj.b2393

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