psychology
bonneebee
ExampleQuantitativeProspectusPPT1.pptxFactors of Student Success in Nursing Education
Dr. J Quin
May 26, 2021
v.4.16.21
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Literature Review: Background to the Problem
What is Understood
The health care industry is facing a nursing shortage that is anticipated to worsen due to the vast amount of nurses reaching retirement age (Peruski, 2019).
The nursing profession has a higher than average growth rate (U.S. Bureau of Labor Statistics, 2019).
The ability to predict success upon admission, and with benchmark assessments during the program, would create opportunities to counsel students (Crouch, 2015) and offer additional support during the program as needed (Rolf et al., 2018).
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Literature Review: Background to the Problem
What is Understood
Nursing program attrition rates are a concern. One study found that 40% of non program-ready students did not complete the program (Peruski, 2019). Program ready students had a significantly higher program completion rate (Peruski, 2019).
NCLEX-RN First attempt pass rates are used to evaluate nursing program quality putting pressure on nursing programs to ensure graduates are prepared to pass the exam on the first attempt (Mathew & Akton, 2018).
The global pandemic, COVID-19 has impacted nursing education in both the classroom and in the clinical setting (Goni-Fuste et al., 2021)..
Literature Review: Background to the Problem
Prior Research
Horns et al. (1991) sought to evaluated preadmission variables and process variables associated with student NCLEX-RN performance from one University who took the NCLEX-RN in 1985. This study found that 67% of variance was accounted for by admission GPA and race, one 2nd year grade, and the 3rd year adult health grade, one fourth year theory grade, and NLN comprehensive predictor (Horn et al., 1991).
Chen and Voyles (2013) evaluated the relationship between HESI A2 scores and three first semester nursing course grades. The study found that the HESI A2 exam useful in predicting student success in the first semester of the nursing program when the highest rates of attrition occur (Chen & Voyles, 2013).
Literature Review: Background to the Problem
Prior Research
Bennett (2016) found that program grade point average, science grade point average, and HESI A2 Anatomy and Physiology subscale were significant in predicting student success. Bennett (2016) also discovered that 40% of unsuccessful students would have been identified prior to admission while retaining 84% of successful students
Peruski (2019) conducted a study to evaluate entrance exam scores in relation to attrition and passage of NCLEX exam scores for incoming nursing students. Peruski (2019) found that 71.3% of students found to be program ready completed the program compared to 49.13% program completion rate for those not considered program ready.
Literature Review: Problem Space
What Still Needs to be Understood
Prior studies face the similar problem of generalizing the findings of their research due to study limitations (Mathew & Akton, 2018, Harrison, 2018).
Further research is needed to evaluate use of standardized testing from multiple vendors to determine the relationship between the products for the population taking the exams (Gillespie & Nadeau, 2019).
Little research has been conducted on the impact of COVID 19 and cognitive measures of student success including program attrition and NCLEX-RN success.
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Literature Review: Problem Space
Empirical Research
Gillespie and Nadeau (2019)conducted a quantitative study to investigate the relationship between Kaplan exams, HESI exit exam, and initial NCLEX-RN success. The recommendation for further research was to conduct research utilizing standardized testing from multiple vendors to determine the relationship between the products for the population taking the exams.
The impact of working during COVID-19 on nursing students needs further evaluation of the short, intermediate, and long-term impacts (Goni-Fuste et al., 2021).
Literature Review: Problem Space
Harrison (2018) conducted a quantitative study to investigate standardized exam scores and NCLEX-RN success. Harrison (2018) discusses several limitations that affect the ability to generalize the findings including the fact research was conducted at a single site, four versions of the comprehensive predictor were given, and two versions of the NCLEX-RN were given.
Mathew and Akton (2018), conducted a retrospective, comparative study investigate factors associated with first attempt NCLEX-RN exam scores including GPA and SAT scores. The recommendation for future research was to evaluate further student factors that would impact first attempt NCLEX-RN pass rates and that the study should be repeated in other programs throughout the United States to evaluate the ability to generalize these findings (Mathew and Akton, 2018).
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Literature Review: Theoretical Foundations
ATI- Bloom’s Taxonomy and Framework of Client Needs
HESI A2- Bloom’s Taxonomy and Critical Thinking Theory.
GPA- Critical Thinking Theory
Program Completion- Critical Thinking Theory.
NCLEX-RN- Bloom’s Taxonomy and Framework of Client Needs
v.1.25.21
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Literature Review: Theoretical Foundations
Bloom’s Taxonomy
Developed by Benjamin Bloom, Max Englehart, Edward Furst, Walter Hill, and David Krathwohl in 1965 and was revised in 2001 (Armstrong, 2020).
A linear progression of learning (Gummineni, 2020).
Consists of six levels of learning (Gummineni, 2020).
Measures learner’s depth of knowledge (Gummineni, 2020).
NCLEX-RN contains questions at application or above. (NCSBN, 2019)
ATI Benchmark and Comprehensive Predictor Exams attempt to mimic NCLEX-RN utilizing questions that measure higher order thinking.
Literature Review: Theoretical Foundations
Framework of Client’s Needs
Defines registered nurse entry-level actions and competencies in all patient care settings.
Four major client needs categories included in the NCLEX-RN.
Safe and Effective Care Environment makes up 32% of exam questions
Health Promotion and Maintenance makes up 9% of exam questions
Psychosocial Integrity makes up 9% of exam questions
Physiologic Integrity makes up 50% of exam questions
(NCBSN, 2018)
This framework was also utilized by ATI when creating the comprehensive predictor examination (ATI, 2019).
Literature Review: Theoretical Foundations
Critical Thinking Theory
Developed by Richard Paul (Elder, 2010).
Founded on the premise that thinking is human nature (Elder, 2010).
Intervention and assessment of thinking are needed to improve the process of thinking (Elder, 2010).
Critical thinking develops by engaging in a journey through six stages of thinking (Paul, 1996).
Development of critical thinking requires challenging students to identify biases and intentionally practice critically thinking (Paul, 1996).
Literature Review: Review of Literature
Nursing Shortage
The United States is facing a nursing shortage (U.S. Bureau of Labor Statistics, 2020) that leaves many registered nurse positions unfilled threatening the health of our communities. Peruski (2019) states there is continued anticipation of a nursing shortage.
Multiple Modes of Entry Into Nursing Practice
There are multiple entry pathways into nursing including diploma programs, LPN to RN programs, ASN programs, BSN programs, and Accelerated BSN programs.
Harrison (2018) evaluated associate degree (ADN) students. Gillespie and Nadeau (2019) evaluated BSN students. Kaddoura et al. (2017) evaluated BSN and accelerated BSN predictors of success.
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Literature Review: Review of Literature
Admission Criteria and Examinations
Entrance requirements are not standardized across schools of nursing (Crouch, 2015).
Each program utilizes different standardized exams in different ways (Randolph, 2017).
Programs utilize a variety of different standardized exams (Gillespie & Nadeau, 2019).
Literature Review: Review of Literature
Measurements of Student Success
Measurements of student success include program completion and success on the first attempt taking NCLEX-RN examination (Peruski, 2019, Mathew & Akton, 2018).
Nursing programs historically have high attrition rates. Peruski (2019) found 40% of the sample did not complete the program.
Pressure from state boards of nursing for high pass rates on first attempt. Pass rates less than 80% require improvement plans overseen by the state board of nursing (Kaddoura et al., 2017).
Literature Review: Review of Literature
COVID-19
COVID 19 has impacted didactic nursing education as well as access to clinical education (Benton et al., 2020). Research regarding the impact of COVID 19 is needed including further examination of the outcomes (Benton et al., 2020). Further evaluation of the short, intermediate, and long-term impacts is needed (Goni-Fuste et al., 2021).
Problem Statement
It is not known if or to what degree a relationship exists between entrance requirements, ATI benchmark exam scores, and enrollment during the COVID-19 pandemic with measures of student success including program completion and performance on the NCLEX-RN exam in nursing students enrolled in a school of nursing.
Variables
| Variable | Conceptual Definition | Operational Definition | Measurement Level | Instrument/Data Source |
| NCLEX-RN First Attempt Pass Rates Dependent Variable Outcome | Measurement of student Success (Wambuguh et al., 2016). | Fail=0 Pass=1 (NSBSN, n.d.) | Nominal | Institution archived student records of NCLEX-RN Results |
| Program Completion Rates Dependent Variable Outcome | Measurement of student Success (Wumbuguh et al., 2016) | Attrition =0 Program Completion=1 (Wumbuguh et al., 2016) | Nominal | Archived Admissions Data |
| Enrollment During COVID 19 Pandemic Independent Variable Predictor | Educational impact by pandemic. | No=0 Yes=1 | Nominal | Archived Admissions Data |
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Variables
| Variable | Conceptual Definition | Operational Definition | Measurement Level | Instrument/Data Source |
| Pre-Admission GPA Independent Variable Predictor | Predictor of academic success (Gartrell et al., 2020) | 0.0-4.0 (Gartrell et al.,, 2020) | Interval | Archived Admissions Data |
| Pre-Admission Science GPA Independent Variable Predictor | Predictor of academic success (Gartrell et al., 2020) | 0.0-4.0 (Gattrell et al., 2020) | Interval | Archived Admissions Data |
| HESI A2 Exam Scores Independent Variable Predictor | Program Readiness (Chen & Voyles, 2013). | 0 – 100% (Hinderer et al., 2014) | Interval | Archived Admissions Data/ HESI A2 Score Report |
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Variables
| Variable | Conceptual Definition | Operational Definition | Measurement Level | Instrument/Data Source |
| ATI TEAS Entrance Exam Scores Independent Variable Predictor | Predictor of success (Wubuguh et al., 2016) | 0-100% (Wubuguh et al., 2016) | Interval | Archived Admissions Data/ ATI Score Report |
| ATI Benchmark Exam Scores: 1.Fundamentals 2. Adult Medical Surgical 3. Maternal Newborn 4. Mental Health 5. Nursing Care of Children 6. Pharmacology Independent Variable/Predictor | Student Achievement and evaluation of preparation for NCLEX-RN (ATITesting.org, 2019b) | 0-100% (ATITesting.org, 2019b) | Interval | ATI Score Report |
| ATI Comprehensive Predictor Independent Variable/ Predictor | Student achievement and evaluation of preparation for NCLEX-RN (ATITesting.org, 2019a) | 0-100% (ATITesting.org, 2019a) | Interval | ATI Score Report |
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Research Questions and Hypotheses
RQ1: • RQ1 Does a statistically significant relationship exist between admission criteria (overall pre-admission GPA, pre- admission Science GPA, HESI A2 Exam scores, ATI entrance exam score), enrollment during COVID-19, and ATI benchmark exam scores with program completion for students enrolled in a rural mid-west school of nursing?
H10: There is a not a significant relationship between potential predictive factors (overall pre-admission GPA, pre- admission Science GPA, HESI A2 Exam scores, ATI entrance exam score), enrollment during COVID-19, and ATI benchmark exam scores with rates of program completion.
H1: There is a significant relationship between potential predictive factors (overall pre-admission GPA, pre- admission Science GPA, HESI A2 Exam scores, ATI entrance exam score), enrollment during COVID-19, and ATI benchmark exam scores with rates of program completion.
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Research Questions and Hypotheses
RQ2: • RQ2 Does a statistically significant relationship exist between predictive factors of success (overall pre-admission GPA, Science GPA, HESI A2 Exam scores, ATI entrance exam score), enrollment during COVID-19, and ATI benchmark exam scores with success on the NCLEX-RN exam administered graduates of a rural mid-west school of nursing?
H20: There is a not a significant relationship between potential predictive factors (overall pre-admission GPA, pre- admission Science GPA, HESI A2 Exam scores, ATI entrance exam score), enrollment during COVID-19, and ATI benchmark exam scores with success on the NCLEX-RN exam.
H2: There is a significant relationship between potential predictive factors (overall pre-admission GPA, pre- admission Science GPA, HESI A2 Exam scores, ATI entrance exam score), enrollment during COVID-19, and ATI benchmark exam scores with success on the NCLEX-RN exam.
Methodology Justification
| Quantitative | Qualitative |
| Seminal sources describing quantitative methodology: Quantitative research is used to test hypothesis about the relationship between variables (Bloomfield & Fisher, 2019). Quantitative research tests the null hypothesis assuming no relationship exists between the independent and dependent variables (Bloomfield & Fisher, 2019). The evaluation of these relationships is done with statistical analysis meaning that the data collected is numerical (Bloomfield & Fisher, 2019). | Seminal sources describing qualitative methodology: Qualitative method is used to understand emotions and meaning applied to specific situations (Vinson, 2019). Blozen (2017) utilized qualitative methodology to evaluate student perceptions and insights. |
| Justification for quantitative: The purpose of the proposed dissertation is to evaluate if a relationship exists. The proposed dissertation will test null hypothesis. All of the data utilized in the proposed dissertation will be in numerical format and analyzed using statistics. | Justification against qualitative: The proposed dissertation seeks to evaluate if a relationship exists, not the experiences or meaning applied to the situation. The proposed dissertation does not seek to evaluate student insights. |
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Design
| Design | Definition | Justification (use /not use) |
| Pre-Experimental | Taylor et al. (2009) utilized pre and post tests in a study using a pre-experimental design to evaluate effects of an educational intervention in nursing students. Research in which a subject is evaluated after an intervention is performed, done to prepare for experimental research (Frey, 20018). | Pre-experimental design is not appropriate for this dissertation. The proposed dissertation does not utilize an intervention, nor does it use pre or post tests. |
| Quasi-Experimental | Quasi-experimental design was utilized to evaluate a control group and a group in which an intervention was applied to evaluate student's online discussion (Chen & Tsao, 2021). Quasi-experimental design utilizes an experimental group and a control group to evaluate effects of an intervention, but the groups are not randomly assigned (Frey, 2018). | Experimental design is not appropriate for this dissertation topic. The proposed dissertation does not compare an experimental group with a control group. Also, the study does not evaluate the effects of an intervention. |
| Correlational or Associative | Correlational design seeks to evaluate the strength and existence of a relationship between variables (Kavrayicl, 2021). Correlational design determines if a statistically significant relationship exists between variables (Nahnaee et al., 2020). | The proposed dissertation does seek to identify if a relationship exists, but it seeks to determine if a predictive relationship exists making correlational predictive more appropriate. |
| Correlational-predictive | Utilized multiple linear regression to identify to what degree two independent variables can explain an independent variable (Gutiérrez-Santiuste et al., 2015). Correlational predictive design is utilized to evaluate the relationship between input variables and outcomes, and can utilize retrospective data (Rolf & Kroposki, 2019). | The purpose of the proposed dissertation is to identify if a predictive relationship exists between variables, not intervention or manipulation will be completed, and the groups will not be compared making correlational predictive design appropriate for the proposed dissertation. |
| Comparative | Causal comparative compares two groups sample groups based are separated based upon a characteristic without use of an intervention or manipulation and can not definitively identify a cause-and-effect relationship (Mertins & Mc Laughlin, 2004). Causal comparative research evaluates causes of differences between existing groups when manipulation is not possible or appropriate (Frey, 2018). | The proposed dissertation will not utilize intervention or manipulation. The dissertation will not compare groups making comparative an inappropriate design for this dissertation. |
| Ex Post Facto | Ex Post Facto design is utilized to evaluate the sample population that differs , retrospectively and without manipulation, to analyze the differences (Peruski, 2019). Ex Post Facto design does not utilize intervention or clinical trial, sample groups are divided based on a specified difference and are not randomized (Pareek & Joshi, 2018). | The proposed dissertation is intended to use retrospective data. The study will not compare differing groups within the sample making ex post facto an inappropriate design for this study. |
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Purpose Statement
The purpose of this quantitative correlational design is to evaluate, if or to what degree, a relationship exists between entrance requirements, ATI benchmark exam scores, and enrollment during the COVID-19 pandemic and measures of student success including program completion and performance on the NCLEX-RN exam in nursing students enrolled in a rural Midwest school of nursing.
Population, Target Population, and Sample
| Population | Target | Sample |
| A group of people who share a characteristic (Elfil & Negida, 2017) | The entirety of a population that shares a common characteristic (Elfil & Negida, 2017). | The sample is a representation of the target population when it is not feasible to study the entire target population (Elfil & Negida, 2017) |
| BSN Nursing students in the Midwest United States | Nursing students who enrolled in a rural Midwest United States BSN nursing program between Spring 2017 and Spring 2021. | Intended sample will be a convenience sample of all students enrolled in the BSN and BSNA nursing program at one institution between the years of 2017 and 2021 (estimated sample size 450 students). This sampling method has been utilized in similar prior studies using logistic regression such as Rolf et al. (2019) and Harrison (2018). Total sample size calculated by G*Power =84 (Faul et al., 2007). |
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Population, Target Population, and Sample
(Faul et al., 2007).
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Instrumentation
| Instrument #1: NCLEX-RN Pass Rates | Instrument #2: Program Completion | Instrument #3: Grade Point Average (GPA) |
| The NCLEX-RN is a computerized adaptive test given to ensure candidates meet minimum entry level to practice (NCSBN, 2021). Candidates are given a minimum of 75 questions to a maximum of 145 questions depending on the need to continue to analyze student's ability and determine if the student meets the passing standard (NCSBN, 2021). Reliability determined by decision consistency statistics (NCSBN, n.d.). The score is a dichotomous pass or fail score (NCSBN, n.d.). The data will be accessed through the institution’s student archives. This is a nominal measurement. | Measured by completion of the program versus attrition from the program. This will me measured as met or did not meet (Dunham & Alameida, 2017). With met being represented by the numerical value of 1 and does not meet represented by the numerical value of 0 (Wambuguh et al., 2016). This is a nominal measurement. Data will be gathered from institutional archives of student transcripts. | Measured on a 0 to 4.0 scale and will be obtained from the student's official transcript (Gartrell et al., 2020). GPA has been utilized as an indicator of academic performance in many prior studies (Walsh, 2020). This is an interval measurement. |
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Instrumentation
| Instrument #4: ATI TEAS Exam Exam Scores | Instrument #5: HESI A2 Exam Scores | Instrument #6: ATI Fundamentals Benchmark Exam |
| ATI TEAS exam is commonly used as an entrance exam for nursing programs and was found to be significant with higher rates of program completion, higher program GPA, and higher NCLEX-RN pass rates (Wambuguh et al., 2016). Gattrell et al. (2020) found the TEAS scores and pre-admission GPA are associated with NCLEX-RN pass rates and success in the first semester of the nursing program. TEAS scores are calculated by averaging the English, Reading, Math, and Science scores to give a range of points from 0 to 100 (Gartrell et al., 2020). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. | HESI A2 Exam scores range from 1-100% (Hinderer et al., 2014). HESI A2 assesses program readiness and student’s knowledge in the categories of math, reading comprehension, grammar, vocabulary, biology, chemistry, and anatomy & physiology (Chen & Voyles, 2012). Kuder-Richardson Formula 20 to determine reliability was found to range from 0.97- 0.99 (Chen & Voyles, 2012). Hinderer et al. (2014) found the HESI A2 did not correlate with pre-admission GPA, and timely progression through the program. Interval measurement. Data will be accessed from archived records of student pre-admission requirements. | 60 question exam with a reliability coefficient of 0.91 and Rasch model, item analysis, and test development practices were used to determine validity (ATITesting.com, 2019b). Measured on a scale of 0 to 100% (ATITesting.com, 2019b). Purpose is to provide an evaluation of students mastery and inform remediation efforts (ATITesting.com, 2019b). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. |
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Instrumentation
| Instrument #7: ATI Adult Medical Surgical Benchmark Exam | Instrument #8: ATI Maternal Newborn Benchmark Exam | Instrument #9: ATI Mental Health Benchmark Exam |
| 90 question exam with a reliability coefficient 0.94 and Rasch model, item analysis, and test development practices were used to determine validity (ATITesting.com, 2019b). Measured on a scale of 0 to 100% (ATITesting.com, 2019b). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. | 60 question exam with a reliability coefficient 0.91 and Rasch model, item analysis, and test development practices were used to determine validity (ATITesting.com, 2019b). Measured on a scale of 0 to 100% (ATITesting.com, 2019b). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. | 60 question exam with a reliability coefficient 0.90 and Rasch model, item analysis, and test development practices were used to determine validity (ATITesting.com, 2019b). Measured on a scale of 0 to 100% (ATITesting.com, 2019b). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. |
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Instrumentation
| Instrument #10: ATI Nursing Care of Children Benchmark Exam | Instrument #11: ATI Pharmacology Benchmark Exam | Instrument #12: ATI Comprehensive Predictor |
| 60 question exam with a reliability coefficient 0.91 and Rasch model, item analysis, and test development practices were used to determine validity (ATITesting.com, 2019b). Measured on a scale of 0 to 100% (ATITesting.com, 2019b). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. | 60 question exam with a reliability coefficient 0.90 and Rasch model, item analysis, and test development practices were used to determine validity (ATITesting.com, 2019b). Measured on a scale of 0 to 100% (ATITesting.com, 2019b). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. | The comprehensive predictor is 180 questions given over 3 hours to evaluate mastery of nursing principles and gives a Score 0 -100% (ATITesting.com, 2019a). Evaluates potential of passing NCLEX-RN (Harrison, 20018). Face validity determined using the Matnel-Haenszel chi- square procedure and was supported by the study (Harrison, 2018). Interval measurement. Data will be accessed from archived records of student pre-admission requirements. |
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Instrumentation
| Instrument #13: Number of Semesters Impacted by COVID 19. | ||
| The impact of COVID 19 on nursing students is lacking in research. Xavier et al. (2020) explains that there is a need to research the impact of the COVID-19 pandemic upon higher education students. To measure if the student was enrolled during the COVID 19 pandemic a nominal approach will be used. Students will be categorized as either impacted or not impacted as a nominal variable. Not impacted will indicate they graduated by Fall of 2019 and will be represented with the numerical value of 0. Impacted will indicate the student was enrolled during the COVID 19 pandemic and be measured by the numerical value of 1. Nominal data meets the assumptions for independent variables in binomial logistic regression (Laerd Statistics, 2013). |
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Data Collection Steps: Slide 1 Required permissions
Site approval from Cox College
Permission to use each instrument from ATI, HESI, and NCSBN.
Permission from Cox College to use the archived data.
Obtaining administrative guide and validation information on each instrument from owner/literature
GCU Chair and Committee Approvals
AQR Approval
IRB Approval
Consent form from individual participants- Not applicable as data is retrospective archived data held by the institution. If IRB requires, request consent from participants.
Required permissions/approvals (prior to data collection)
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Data Collection Steps: Slide 2 Sampling Strategy and Sample Selection
| Strategy #1 | Strategy #2 | Strategy #3 | |
| Sampling Strategy Description | Convenience sample utilizes participants that are easily accessed, not randomized, and are inexpensive (Lewis-Beck et al., 2004). This is the preferred method as it is most in line with the purpose, method, and design. | Snowball sampling utilizes contact with one participant to gain access to additional participants that meet the sampling criteria (Lewis-Beck et al., 2004). | Purposive sampling is seeking out participants that meet the selection criteria (Lewis-Beck et al., 2014). |
| Sampling Steps | Determine minimum sample size to ensure minimum is met. Get permission to access data from institution Gather retrospective data for all students enrolled in the proposed institution during proposed time frame. | Determine minimum sample size to ensure minimum is met. Contact initial participant. Request access to additional participants. Contact additional participants. | 1.Determine minimum sample size to ensure minimum is met. 2. Create sampling criterion. 3. Contact sample population and request participation in the study. |
| Sampling Selection Criteria | BSN nursing student Enrolled in Cox College Nursing program between Spring 2017 and Spring 2021. | BSN nursing student enrolled in Cox College between Spring 2017 and Spring 2021. Maintain sample within one organization due to accessibility and permission needed to access the data. | BSN nursing student enrolled in Cox College between Spring 2017 and Spring 2021. Maintain sample within one organization due to accessibility and permission needed to access the data. |
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Data Collection Steps: Slide 3 Collecting the Data
Step 1: Receive permission to access archived data from Cox College.
Step 2: Receive approval from GCU IRB.
Step 3: Set up excel spread sheet with Cox College IT department to ensure encryption and security of student data until it can be de-identified to ensure student privacy.
Step 4: Utilize an excel spread sheet to collect demographic and pre-admission data from archived student records. Spread sheet to include gender, students age upon enrollment, student’s ethnicity, dates of enrollment, pre-admission GPA, pre-admission science GPA, HESI A2 scores and/or TEAS entrance exam scores, and first attempt pass rates on NCLEX-RN. Data is retrospective and has already been obtained and archived in student records.
Step 5: Record student benchmark exam scores from institution’s grading platform (Fundamentals, Medical Surgical, Maternal Newborn, Care of Child, Mental Health, and Pharmacology) and Comprehensive Predictors for all enrolled Cox College BSN students between Spring 2017 and Spring 2021. Record scores for each student on spread sheet.
Step 6: De-identified all data on spread sheet to protect student privacy.
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Data Collection Steps: Slide 4 Data Management and Storage
Data Management and Storage
Where will you store the data? On the Cox College computer to ensure security and approval of Cox College IT.
How long will you store the data? Data will be stored long enough to run the statistics and complete the dissertation process. Then under supervision of the IT department data will be destroyed to ensure student privacy is maintained.
How will you protect the data? The data will be maintained by storing it on a secure network, involving oversight of the IT department, de-identification of the data, and proper destruction of the data once it is no longer needed.
How will you destroy the data? Under supervision of the Cox College IT department, I will delete the spread sheet and ATI reports once they are no longer needed.
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Data Analysis Steps: Slide 1
Hypothesis #1 & 2 – Descriptive Statistics
Harrison (2018) conducted a similar study and utilized descriptive statistics to describe the make up of the sample. This description included percentage of female participants, percentage of male participants, and mean exam scores (Harrison, 2018).
Van Hofwegen et al. (2019) utilized descriptive statistics to give the age range of sample participants.
The best measure of central tendency of nominal data is the mode and the best measurement of ordinal data is the median (Laerd, 2018).
The proposed dissertation will utilize descriptive statistics to determine the age range of participants, percentage of male and female participants, mean exam scores, and mode of dichotomous variables will be calculated as seen in prior similar research.
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Data Analysis Steps: Slide 1
Hypothesis #1 & 2 – Binomial Logistic Regression
The dependent variables are nominally measured as pass/fail and complete/incomplete. Binomial logistic regression evaluates the likelihood of an observation falling into one of the dichotomous dependent variable outcomes (Laerd Statistics, 2013).
Binomial logistic regression utilizes independent variables are measured as continuous or nominal (Laerd Statistics, 2013). The independent variables of this dissertation are either continuous (test scores, GPA) or nominal (impacted by COVID-19 pandemic).
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Data Analysis Steps: Slide 1
Hypothesis #1 – Binomial Logistic Regression
Step 1: Gather data
Step 2: Download SPSS Statistics
Step 3: In SPSS, set up dichotomous dependent variable program completion.
Step 4: In SPSS, set up each of the independent variables.
Step 5: In SPSS, set up case identifier.
Step 6: In SPSS, go to data view and enter data.
Step 7: In SPSS, test for assumptions of linearity, multicollinearity, and significant outliers.
Step 8: In SPSS, run binomial logistic regression.
Step 9: Interpret results. (Laerd Statistics, 2013)
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Data Analysis Steps: Slide 2
Hypothesis #2 –Binomial Logistic Regression
Step 1: Gather data
Step 2: Download SPSS Statistics
Step 3: In SPSS, set up dichotomous dependent variable NCLEX-RN pass rates.
Step 4: In SPSS, set up each of the independent variables.
Step 5: In SPSS, set up case identifier.
Step 6: In SPSS, go to data view and enter data.
Step 7: In SPSS, test for assumptions of linearity, multicollinearity, and significant outliers.
Step 8: In SPSS, run binomial logistic regression.
Step 9: Interpret results. (Laerd Statistics, 2013)
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Feasibility - Slide 1
Resources for study:
Microsoft Excel
Subscription to Laerd Statistics /SPSS
Data from the School of Nursing
APA Manual
Access to GCU Templates
Authorization from the proposed institution to access data.
Access to institution data
Ethical Concerns:
All student identification should be removed from the data to ensure confidentiality.
Participation benefits future students by providing an understanding of the relationship between the factors.
Feasibility – Slide 2
Study Alignment with Program (Identify Program of Study):
Doctorate of Education in Organizational Leadership with an Emphasis in Higher Education
Leadership within nursing programs need evidenced based information on which to base admissions criteria and benchmark testing within the nursing program.
Findings could lead to identification of opportunities to intervene and promote student success and thus may impact attrition rates.
Feasibility Concerns:
Potential obstacle faced will be time needed to collect and transfer data to spreadsheet.
Based on information collected the study is feasible. Utilizing excel spreadsheet and SPSS to conduct data analysis increases feasibility of the study.
Defend
Defend how your theoretical foundation supports your research topic
The research topic is predictors of nursing student success
Bloom’s Taxonomy, Framework of Client Needs, and Critical Thinking Theories guide the proposed study. Student nurses need to achieve higher levels of Bloom’s Taxonomy and Critical Thinking to be successful in the program and on the NCLEX-RN.
Defend
Defend the need for your study from literature
This study would be of interest to stakeholders involved with a school of nursing.
Leadership, students, healthcare institutions, local and federal government
High attrition rates.
Peruski (2019) 40% of students didn’t complete the program.
High attrition rates lead to fewer nursing graduates available to fill nursing positions (Chen & Voyles, 2013).
Benefits of identifying students at risk for attrition
Potential to counsel students
Potential for remediation
Defend
Defend that this study is feasible
Measurement tools widely utilized.
Data is retrospective and is currently in possession of the proposed institution.
Institution expresses an interest in the study.
Next steps
Discuss the next steps you will take between now and the next residency.
Finish writing literature review.
Monitor alerts in the library.
Continue to review literature.
Apply to the institution IRB.
Complete paperwork for content expert.
Complete CITI Training.
List of References
Armstrong, P. (2020). Bloom’s Taxonomy. Vanderbuilt University. https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/
ATITesting.com. (2019a). RN comprehensive predictor 2019 test description. Retrieved from https://atitesting.com/docs/default-source/assessments/rn-assessments/rn_2019_cp_test_description.pdf?sfvrsn=2
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