RDP: SMDC MATRIX EDUC 750
RESEARCH DESIGN PROJECT: SAMPLING, MEASUREMENT, AND DATA COLLECTION TYPE MATRIX TEMPLATE
Shanay Y. Howard
School of Education, Liberty University
Sampling, Measurement, and Data Collection Type Matrix
Complete this matrix using the articles approved from your pre-matrix tables (Modules 2-4). The matrices are set up with specific
spacing in each block. Please do not change. The reference block is already set up in APA with double-spacing and hanging indent.
You do not need to include your article references in the reference section; only the Bible reference(s).
Article Type QUANtitative Basic (QUAN-B)
Article Reference Yukhymenko-Lescroart, M. A. (2021). The role of passion for sport in college student-athletes’ motivation
and effort in academics and athletics. International Journal of Educational Research Open, 2,
100055. https://doi.org/10.1016/j.ijedro.2021.100055
Sampling Procedure The procedure for sampling was non-probability convenience.
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RDP: SMDC MATRIX EDUC 750
Participants (who,
how many,
demographics)
One hundred and eighty-seven participants were NCAA student-athletes invited through athletics staff,
from a U.S. university (Yukhymenko-Lescroart, 2021, p. 4). Multiple sports (16) were represented for
students aged 18-23. There were 50 freshmen, 46 sophomores, 49 juniors, 35 seniors, and 7 graduate
students (Yukhymenko-Lescroart, 4, para 2).
Intervention
Description (if
applicable; if not,
write NA)
N/A this is a non-experimental survey study
Measurements or
Instruments Used
The measurements used were various scales. The Passion scale (harmonious/obsessive passion), basic
psychological needs scales (competence, autonomy, relatedness), autonomous academic motivation
scales,
self-reported academic and athletic effort, all Likert-scale. They used also the AAIS which was the
Academic and Athletic Identity Scale (Yukhymenko-Lescroart, 2021, p. 4).
Data Collection Pre-printed and Pre-arranged surveys, in envelopes, were given to student athletes and proctored by the
(who, how, when) team captains. Once the surveys were done they were placed in an envelope and then given to team
captains who then returned them back to athletics department (Yukhymenko-Lescroart, 2021, p. 5). It was
single time point data collection.
Data Analysis (what
statistical analyses
were used?)
The statistical analyses used were correlation analyses, regression models and structural/motivational
pathway modeling (SEM-style analysis). Path analysis was used to examine the hypothesis of relationships
among the measured variables (Yukhymenko-Lescroart, 2021, p.5 ).
Article Type QUANtitative Applied (QUAN-A)
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RDP: SMDC MATRIX EDUC 750
Article Reference Daum, H. T., Daum, L. t., & Scholten, S. D. (2024). Academic achievement among NCAA Divison II
student-athletes and non-athletes. Youth, 4(3), 1260-1270. https://doi.org/10.3390/youth4030079
Sampling Procedure Non-probability volunteer sample
Participants (who,
how many,
demographics)
There were a total of 170 college student participants. 92 were student-athletes and 78 were non-student
athletes (Daum et al., 2024, p. 1262). The average age of the participants was 21.5 and the age range of all
who participated was 18 to 41 years old (Daum et al., 2024).
Intervention
Description (if
applicable; if not,
write NA)
N/a ( comparative non-experimental study)
Measurements or
Instruments Used The data was collected through a survey called: An assessment of Academic Achievement and Future
Success amongst Student Athletes and Traditional Students at Augustana University. The survey was a 29
item Google form (Daum et al., 2024, p. 1261).
Data Collection (who,
how, when) The date was collected through the online survey (google form) from May 6-May 23, 2022. The survey
was randomly distributed and done through self-reported measures (Daum et al., 2024, p. 1261, para 4).
Data Analysis (what
statistical analyses
were used?)
Using a GraphPad statistical analyses were done with standard deviation (SD), t-tests, and z-test ( Daum et
al., 2024, p. 1262). Group comparisons were also done between student-athletes and non-athletes.
Article Type QUALitative Basic (QUAL-B)
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RDP: SMDC MATRIX EDUC 750
Article Reference Brouwer, A. M., Johanson, J., & Carlson, T. (2022). College Athletes’ views on academics: A qualitive
assessment of perceptions of academic success. Journal of Athlete Development & Experience, 4
(2), 122-138. https://doi.org/10.25035/jade.04.02.01
Sampling Procedure Purposive (Purposeful), volunteer sample of college students
Participants (who,
how many,
demographics)
There were a total of 62 student athletes ( 32 men and 30 women) from six different sports at a midsized
public NCAA division II university in Midwestern US (Brouwer et al., 2022, p. 124, para 4). Most of the
participants were white and between the ages of 18-22 years old. There were also 12 focus groups with 4-
8 student athletes in each group (Brouwer et al., 2022, p. 124).
Intervention
Description (if
applicable; if not,
write NA)
n/a (qualitative focus-group inquiry)
Measurements or
Instruments Used
The study used a semi-structured interview that was based on the theoretical model of Comeaux &
Harrison and questions of interest by the authors (Brouwer et al., 2022, p. 124). There was no
standardized
scale and measurement was made through rich narrative data.
Data Collection (who,
how, when) The info was collected by in person focus groups that took place for 60-90 minutes conducted by the
researchers in the academic resource center (Brouwer et al., 2022).
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RDP: SMDC MATRIX EDUC 750
Data Analysis (how
was data analyzed?) Data was transcribed verbatim and upload into NVivo 10 for coding analysis (Brouwer et al., 2022, p. 125).
The interviews were analyzed through Consensual Qualitative Research Methodology (CQR), additionally
thematic analysis was used (Brouwer et al., 2022). A coding team was also used that consisted up three
men and four women.
Article Type QUALitative Applied (QUAL-A)
Article Reference Rutledge, M.E. II. (2023). Exploring how student athletes balance athletic, academic, and personal needs
though Learned Needs Theory. Journal of Research Initiatives, 7(2), Article 4.
https://digitalcommons.uncfsu.edu/jri/vol7/iss2/4/
Sampling Procedure Purposive sample of collegiate student-athletes
Participants (who,
how many,
demographics)
This study used ethnographic case study methods to generate and understanding of how LNT applies to
participants (Rutledge, 2023, p. 9). There were a total of 4 participants who were studied during
childhood, adolescence and adulthood on their athletic and academic experiences. The ages of the
participants were , 25, 29, 30 and 32. Three played football and one ran track (Rutledge, 2023, p. 10).
Intervention
Description (if
applicable; if not,
write NA)
N/A ( qualitative exploration)
Measurements or
Instruments Used The measurements used was a semi-structured interview that was aligned with Learned Needs Theory.
The authors/researchers reviewed reflexives journal notes and analytic notes for measurement.
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RDP: SMDC MATRIX EDUC 750
Data Collection (who,
how, when) The participants were studied through their entire lives. The data collection was done through one-on-one
interviews that were audio recorded and done by a researcher (Rutledge , 2023, p.11). Each interview was
an average time of 70 minutes. Some were longer or shorter based on the responses to the questions.
Data Analysis (how
was data analyzed?) The participants along with the researchers, reviewed transcripts and interview summaries. Coding
process was used as well as respondent-centered analysis to review notes and establish validity and
trustworthiness (Rutledge, 2023, p. 11). Charting was also used for thematic and comparative analysis.
Article Type Mixed Methods Basic (MM-B)
Article Reference Thompson, F., Rongen, F., Cowburn, I., & Till, K. (2023). What is it like to be a sport school student
athlete? A mixed-method evaluation of holistic impacts and experiences. PLOS ONE, 18(11),
e0289265. https://doi.org/10.1371/journal.pone.0289265
Sampling Procedure Purposive sample of adolescent student-athletes
Participants (who,
how many,
demographics; if
different participants
were in quantitative
and qualitative
portions, please
indicate the
The study consisted of 83 students in year 12-13 in sport school. All participants were 16 and older , 36
were boarders and 47 were non-boarders representing multiple sports (Thompson et al., 2023, p.5).
differences)
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RDP: SMDC MATRIX EDUC 750
Intervention
Description (if
applicable; if not,
write NA)
N/A (Program experience evaluation)
Measurements or
Instruments Used
(what was used for
quantitative portion?
Qualitative portion?)
The measurements used in this study that were quantitative was standardized scales that was used to
assess life satisfaction, burnout, well-being, and dual -career support. The qualitative measurements were
semistructured interviews exploring daily life in sport school and their perceptions of academic and
athletic load, relationships and school support (Thompson et al., 2023, p. 5).
Data Collection (who,
how, when for
quantitative and
qualitative portions)
The date was collected over a two-month period using an explanatory sequential mixed-methods design.
Data was also collected by the participants completing one online questionnaire (Thompson et al., 2023,
p. 5). The quantitative measurements came through scales and assessments, questionnaires and
assessment grades. The qualitative measurements and data came through log diaries and open ended
questions (Thompson et al., 2023, p.6). Because this study was an explanatory sequential design first the
questionnaires were administered to the sport school students and then secondly the in-depth interviews
through focus groups were used for qualitative date to explain patterns (Thompson et al., 2023).
Data Analysis (how
was data analyzed
quantitatively and
qualitatively)
The quantitative and qualitative data were analyzed separately. The quantitative statistical data was
processed using SPSS and Excel. Qualitative data used thematic analysis and content analysis to generate
descriptive themes. This was done to describe the characteristic of the findings’ content (Thompson et
al., 2023, p. 10). Inductive coding was also used to analyze the data.
Article Type Mixed Methods Applied (MM-A)
Article Reference Chadler, G.E., Kalmakis, K.A., Chiodo, L.M., & Helling, J. (2020). The efficacy of a resilience
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RDP: SMDC MATRIX EDUC 750
intervention among diverse, at-risk, college athletes: A mixed-methods study. Journal of the
American Psychiatric Nurses Association, 26(3), 269-281.
https://doi.org/10.1177/1078390319886923
Sampling Procedure Non-probability volunteer sample of college students considered at risk
Participants (who,
how many,
demographics; if
different participants
were in quantitative
and qualitative
portions, please
indicate the
differences)
There were 47 first year male football players and 15 women’s basketball players who went to a northeast
university and participated in this study during the summers of 2016-2018 (Chandler et al, 2020, p. 271).
Participants were aged 18 and 19 years old and the large majority were African American.
Intervention
Description (if
applicable; if not,
write NA)
The 5- week resilience training course which included modules on stress management, coping and
emotional awareness were part of the intervention in this study. The 10 session course used the resilience
training model of the ABCS (Chandler et al., 2020, p. 272).
Measurements or
Instruments Used
(what was used for
quantitative portion?
Qualitative portion?)
The quantitative measurements used in this study were pre and post self-reported scales that measured
perceived stress, resilience, emotional awareness, and sense of belonging (Chandler et al., 2020, p.272).
The qualitative measurements were expressive writing assignments and open-ended reflections that
measured the changes in coping, relationships and help-seeking.
Data Collection
(who, how, when for
Quantitative Data was collected with surveys at baseline and post intervention for both groups. The
qualitative data that was collected was during and after the course which helped explain quantitative
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RDP: SMDC MATRIX EDUC 750
quantitative and
qualitative portions) changes (Chandler et al., 2020). The data collection done through a pretest in week1 and then a post test
in week 5. The qualitative reflections were collected during and after the intervention.
Data Analysis (how
was data analyzed
quantitatively and
qualitatively)
The general linear model was used to evaluate the groups and their change. In class reflective writings
and final presentations were analyzed by a model by Miles and Huberman (Chandler et al., 2020, p.272,
para 8-9). There was repeated measures analyses, qualitative thematic analysis, paired t-test, integration.
Article Type Action Research (AR- QUAN)
Article Reference Firth-Clark, A., Sutterlin, S., & Lugo, R. G. (2019). Using cognitive behavioural techniques to improve
academic achievement in student-athletes. Education Sciences, 9(2), 89.
https://doi.org/10.3390/educsci9020089
Sampling Procedure Purposive sample of student athletes who were academically underperforming but athletically talented
Participants (who,
how many,
demographics)
There was a total of 94 students, 31 which were females and participated in a variety of sports. A total of
123 students were originally considered for the study (Firth-Clark et al., 2019).
Intervention
Description (if
applicable; if not,
write NA)
Intervention was done in a 6-week cognitive -behavioral + HRV biofeedback training. Academic skills, self-
regulation and stress management were measured.
Measurements or
Instruments Used The measurements used in this study self-efficacy scales, academic grades pre and post intervention,
selfregulation and profile measures were used (Firth-Clark et al., 2019).
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RDP: SMDC MATRIX EDUC 750
Data Collection (who,
how, when) The data was collected through pre and posttest grade reports and surveys that was done by school staff
and researchers over one term. Data was collected through school records and researcher- administrated
questionnaires (Firth-Clark et al., 2019).
Data Analysis (what
statistical analyses
were used?)
Statistical analyses used were repeated measures (ANOVA), between group comparisons and effect size
calculations (Firth-Clark et al., 2019, p. 5).
Article Type Action Research (AR- QUAL or MM)
Article Reference Shipherd, A. M., Frye, B. M., & Duffy, K. (2025). A gamified intervention to enhance first- year student-
athletes’ academic self-efficacy and well being: A mixed methods study. Journal of Amateur Sport,
11(1), 1-25. https://doi.org/10.17161/jas.v11i2.23098
Sampling Procedure Convenience sample of first year students enrolled in academic support or seminar
Participants (who,
how many,
demographics)
There were 36 student athletes and 42 non-athletes that completed initial questionnaires at the beginning
of the fall 2022 semester. But a total of 24 incoming student athletes and 35 non-athletes completed the
entire study (Shipherd et al., 2025, p. 140).
Intervention
Description (if
applicable; if not,
write NA)
Gamified academic support program that has built in systems, badges and motivation boosters was the
intervention in this study.
Measurements or
Instruments Used (if
using MM, indicate
The quantitative measurements used were pre and post intervention scales that assessed well-being and
engagement, academic self-efficacy, basic psychological needs or motivation related to course work
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RDP: SMDC MATRIX EDUC 750
which were
qualitative and which
were quantitative)
(CASES) as well as Stress through the Stress Mindset Measure(SMM). The qualitative measurements used
were open-ended reflections, interviews about the student’s experience with gamified platform (Shipherd
et al., 2025, p. 140-142).
Data Collection (who,
how, when; if using
MM, indicate which
were qualitative and
which were
quantitative)
Data was collected through surveys administered at multiple times before, during and after the gamified
intervention (Shiperd et al., 2025, p. 142). The qualitative data collected through interviews and open
ended reflections was done to understand why and how the game elements affected self-efficacy and
wellbeing (Shipherd et al., 2025, p. 142).
Data Analysis (how
was data analyzed
qualitatively or if
mixed methods,
quantitatively and
qualitatively)
The data was analyzed through mixed methods integration. The quantitative data was screened using
SPSS and t-tests. The qualitative data was analyzed and coded. There was thematic analysis for qualitative
responses ( Shipherd et al., 2025, p. 143).
Article Type Program Evaluation (PE-QUAN)
Article Reference Stamatis, A., Nichols, A. L., Gastinger, N., Augustus, L., & Oates, R. (2025). Program-level evaluation of
one-on-one and team-based mental performance services among collegiate student-athletes.
International Journal of Exercise Science: Conference Proceedings, 15(6), 45-59.
https://digitalcommons.wku.edu/ijesab/vol15/iss6/7
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RDP: SMDC MATRIX EDUC 750
Sampling Procedure Non- probability program-based sample of college student athletes in mental performance
Participants (who,
how many,
demographics)
There was a total of 128 students as a part of this study who played multiple sports. 82 of the students
participated in one-on-one session and 68 of them in team sessions (Stamatis et al., 2025).
Intervention
Description (if
applicable; if not,
write NA)
Mental-performance services that measured mindset, coping, focus, and confidence sessions was the
intervention in this study (Stamatis et al., 2025).
Measurements or
Instruments Used The measurements used in this study was pre and post surveys and well as likert -type scales for ratings
assessing athletic performance, mindset, well-being, leadership, sports experience and team culture
(Stamatis et al., 2025).
Data Collection (who,
how, when) Data was collected online from paired t-tests. Effect sizes were used to evaluate change and compare
service formats. The program evaluation survey using pre -post design was used by athletes to rate
themselves before and after services(Stamatis et al., 2025, p. 1). This took place in the fall 2024 semester.
Data Analysis (what
statistical analyses
were used?)
The statistic analysis used was paired t-test, effect sizes and group comparisons between one on one vs.
team based.
Article Type Program Evaluation (PE-QUAL or MM)
Article Reference O’Hara, E., Harms, C., Ma’ayah, F., & Speelman, C. (2021). Educational outcomes of adolescents
participating in specialist sport programs in low-SES areas of Western Australia: A mixed methods
study. Frontiers in Psychology, 12, 667628. https://doi.org/10.3389/fpsyg.2021.667628
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RDP: SMDC MATRIX EDUC 750
Sampling Procedure Purposive sampling was used to target schools offering specialist sports programs (SSP)
Participants (who,
how many,
demographics)
There were two groups of students in this study. The groups consisted of those involved in an SSP and
those attending the same school but not in the program (O’Hara et al., 2021, p. 3). Students were invited
to participate who were in year 7 though year 10 and aged 12-15 years old.
Intervention
Description (if
applicable; if not,
write NA)
Specialist Sport Program where there was extended sport-training and academic programming. The
program was already implemented, and the study evaluated its impact (O’Hara et al., 2021).
Measurements or
Instruments Used (if
using MM, indicate
which were
qualitative and which
were quantitative)
The measurements used in the study that were quantitative were academic grades, school administrative
data and school engagement indicators like attendance and behavior. For the qualitative measurements,
semi-structured interviews were done with specialist parents, key stakeholders (IPA), parents, teachers
and graduates (O’Hara et al., 2021, p. 4-5).
Data Collection (who,
how, when; if using
MM, indicate which
were qualitative and
which were
quantitative)
This was a mixed methods study, so the school admin records were collected over an academic year
which was the quantitative data. Then the qualitative data collected was through interviews conduced
mid-year by the researchers (O’Hara et al., 2021). The data was collected concurrently.
Data Analysis (how
was data analyzed
The quantitative and qualitative data was analyzed independently ( O’Hara et al., 2021, p. 5). This allowed
the authors to see how each phase informed each other. The data was analyzed quantitatively through
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RDP: SMDC MATRIX EDUC 750
qualitatively or if
mixed methods,
quantitatively and
qualitatively)
descriptive statistics and comparisons, and the qualitative date was analyzed through thematic analysis.
Additionally, t-test were used to info the specialist students’ school engagement ( O’Hara et al., 2021).
Biblical Integration
For the following questions, you will take a biblical worldview connecting research and the Bible. Each question must be supported
with a different Bible verse. Answer each question with 5-7 sentences. Be sure to cite in-text and reference.
1. The use of artificial intelligence (AI) in research critique and design has come under fire due to people’s unethical use of the
tool. In and of itself, it is not bad; however, using it to do your because you do not understand what you need to do, you ran
out of time, or you want to make it look pretty is the same thing as cheating/plagiarism (the modern version of having
another human do your work or copying someone else’s work).
What does the Bible say about its unethical use and how can you use it responsibly?
Artificial intelligence, as we all know, is here to stay so we must learn to use it properly to support research and work but
never replace personal effort or actually work. It is unethical to misrepresent one’s work, and it is cheating. In the bible, it
states in Proverbs 10:9, “whoever walks in integrity walks securely, but he who makes his ways crooked will be found out”(
English Standard Version, 2001). As I say to my students, intentions matter. So as this scripture states, we must work and walk
in integrity. Additionally, I believe the lord has gifted us all with the ability to do work no artificial intelligence and interpret or
do. In 1 Peter 4:10, the scriptures instruct us, as believers, to be “good stewards of God’s varied grace” ( English Standard
Version, 2001). This scripture gives guidance on how to use artificial intelligence responsibly and ethically. This means we
must approach the use of AI with integrity and not replace our critical thinking and academic labor. As believers, we must
steward technology in ways that honor the word and protect academic integrity.
References
Holy Bible, English Standard Version. (2001). Crossway Bibles. (Original work published 1611)
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