DOI: doi.org/10.21776/ub.ijds.2023.10.02.13
305
Association between Self-advocacy and Academic Performance of Higher Education Students with
Disabilities: A Meta-analysis
Unita Werdi Rahajeng ,
Doctoral Program Faculty of Psychology Universitas Airlangga;
Department Psychology Universitas Brawijaya, Indonesia
Wiwin Hendriani ,
Faculty of Psychology Universitas Airlangga, Indonesia
Pramesti Pradna Paramita ,
Faculty of Psychology Universitas Airlangga, Indonesia
Corresponding author:
Unita Werdi Rahajeng, [email protected] Article history:
Received: 19 June 2023 Revised: 1 November 2023 Accepted: 23 November 2023 Published online at
ijds.ub.ac.id
Copyright © 2023 Author(s) Licensed under CC BY NC.
Abstract
Self-advocacy is one of the personal factors essential for students with disabilities in college and for obtaining satisfactory achievements.
This meta-analysis proves the strength of the relationship between self-advocacy and one of the success parameters of students with disabilities, namely academic achievement. It followed the guidelines from PRISMA. A literature search was conducted in four databases:
Scopus, Web of Science, Psychnet, and Proquest, and 328 articles were obtained. Finally, six articles remained for further analysis. Based on the analysis using the DerSimonian and Laird (DL) estimator model, the estimated effect size was 0.29 (p<0.001; 95% CI=0.20 – 0.38).
Although the results of the meta-analysis show a significant positive correlation between variables, there are things that need to be considered in terms of heterogeneity and the risk of publication bias.
Keywords: academic achievement, higher education, meta-analysis, self-advocacy, students with disabilities
1. Research Background
Inclusive education policies and equal access to education open up greater opportunities for persons with disabilities to pursue higher education. In the United States, the number of students with disabilities has significantly increased since the enactment of the Americans with Disabilities Act (ADA) in 1990 (Kimball et al., 2016).
Globally, similar trends are also found in other countries, for example in the UK (Hubble &
Bolton, 2021), Israel (Lipka et al., 2020), and Indonesia (Badan Pusat Statistik, 2020;
Christiyaningsih, 2017).
In general, students with disabilities have to face a variety of barriers that typical students have not experienced, such as physical and attitudinal barriers. Physical barriers, among others, are related to the complexity of accessing the physical environment and infrastructure, assistive technology, special services, and learning accommodations. Meanwhile, the attitudinal barrier from the people around him is related to the stigma against persons with disabilities (Kimball et al., 2016) and a lack of awareness of the needs of persons with disabilities (Majoko, 2018). Anctil et al., (2008) argued that self-advocacy is essential for students with disabilities to navigate these barriers. Students with disabilities who are proficient in self-advocacy will be more likely to get support from their surroundings (Daly-Cano et al., 2015; Pfeifer et al., 2020).
Therefore, self-advocacy is one of the many skills prepared for persons with disabilities who are going to college (Algozzine et al., 2001).
Self-advocacy can be defined as a skill to communicate needs and desires and make decisions regarding the support needed to meet their needs (Stodden et al., 2003). Self- advocacy has four components: knowledge about themselves, knowledge of their rights, communication, and leadership (Test et al., 2005). Knowledge of self and rights are the basis of self-advocacy. Students should recognize their strengths, weaknesses, and special needs so that they can determine the right support for themselves. Students also need to understand their rights as persons with disabilities, especially in the context of higher education practices. Communication is a core skill of self-advocacy, including assertiveness, expressing opinions, and negotiation. Leadership -although less discussed- is a component that makes it easier for students with disabilities to understand the dynamics of the environment and make their self-advocacy have a wider impact.
In particular, various studies have concluded that self-advocacy is a determinant of the students’ with disabilities success (Moriña & Biagiotti, 2021). There are various parameters of success in pursuing higher education, including persistence, college completeness, academic achievement, learning, self-development, and employment (Kimball et al., 2016). These parameters are measurable and objective (Russak & Hellwing, 2019). This research will focus on a parameter, namely academic achievement.
Academic achievement is one of the most important component of student success as a proxy of student success (York et al., 2015). Academic achievement is a representation of academic ability which is portraying student performance. York et al., (2015) also found that academic achievement is the most commonly measurement of student success.
Academic achievement is usually identified through the grade point average (GPA) (Kimball et al., 2016; Rodríguez-Hernández et al., 2020; York et al., 2015) and a few others are identified through grades during lectures, rankings, and value constancy (Rodríguez- Hernández et al., 2020; York et al., 2015). This is unsurprising, since GPA, grades, and ranking are the most readily available assessment from institution, so it is easier for researcher to obtain (York et al., 2015).
This study determines the relationship between self-advocacy and academic achievement of students with disabilities in a higher education setting. Instead of conducting empirical research, this study will conduct a meta-analysis of findings related to the association between self-advocacy and academic achievement. Meta-analysis is a review approach by systematically synthesizing data from various studies and then calculating the estimated effect size using statistical methods (Egger & Smith, 1997). The purpose of this meta-analysis was to obtain the estimated effect size of the association between self-advocacy and academic achievement so that there is more convincing evidence about the claim that self-advocacy contributes to the study success of students with disabilities, especially academic achievement. This meta-analysis follows the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement (Page et al., 2021).
2. Method
2.1 Data sources and search strategy
The relevant studies were searched comprehensively in four databases: Scopus, Web of Science, Proquest, and PsycInfo, from 18 to 23 May 2022. The search term represented three core topics: students with disabilities, self-advocacy, and academic performance. Each keyword is expressed in Boolean operators, which are referred to in previous literature reviews. Details of the search strategy and keywords are shown in Table 1. Additionally, a manual search was carried out for potential studies by checking references in the included articles.
Table 1. Search strategy used in this meta-analysis
No Search topic Keyword search with Boolean operators Reference
1 Higher
education students with disabilities
"College student" OR "university student" OR "postsecondary education" OR "college admission" OR "higher education" OR
"student affairs" OR "student services" OR "student personnel"
Gelbar et al., (2020)
disabilit* OR "hearing impair*" OR deaf OR disabled OR handicap OR adhd OR add OR dyslex* OR blind OR disabilities OR accommodation OR "mental illness" OR "mobility impairment" OR "visual impair*"
2 Self-advocacy "self-advocacy" OR "self-determination" OR "self-advancement"
OR "stan* up for oneself" Schena et al.,
(2022) 3 Academic
performance achievement OR "academic performance" OR "learning
outcome" Chen and Yang,
(2019)
2.2 Eligibility criteria
Studies included in this meta-analysis if they met the following criteria:
1. An empirical study using a quantitative approach was published in the form of peer- reviewed articles, theses, or reports.
2. Research participants were higher education students with disabilities 3. Measuring self-advocacy and academic performance using a valid instrument
Records identified from*:
Scopus (n = 119) Web of science (n = 18) Proquest (n=166) Psychnet (n=25)
Records removed before screening:
Duplicate records removed (n = 15)
Records screened by tittle, keyword, and abstract (n = 313)
Records excluded**
Wrong study design (n = 121)
Wrong population (n=54)
Wrong publication type (n=4)
Foreign language (n=5) Wrong outcome (n=96) Article not available (n=1)
Full text assessed for eligibility (n = 32)
Reports excluded:
Wrong outcome (n = 22) Wrong study design (n = 2)
Wrong population (n = 1) Full text not available (n=3)
Effect size not available (n=1)
Records identified from:
Citation searching (n
= 12)
Reports assessed for eligibility
(n = 12)
Reports excluded:
(n=9)
New studies included in review (n = 3)
Reports of new included studies (n = 3)
Identification of new studies via databases and
registers Identification of new studies via
other methods
IdentificatScreeningIncluded
Total studies included in review
(n = 6)
Reports sought for retrieval
(n = 12)
Reports not retrieved (n = 0)
4. Published in English or Indonesian
5. Report effect sizes or properties that can be converted into effect sizes 6. The full-text article can be accessed
2.3 Selection and extraction
Potential articles were exported from each database and then recorded and selected using the Rayyan Application (Ouzzani et al., 2016). The initial stage was to identify duplicate articles. The next stage was screening in two phases: 1) title, keyword, and abstract screening and 2) full paper screening. We checked references from the included studies to obtain additional potential articles. The stages of selection are presented in Figure 1.
The data extracted from the included studies comprised author, title, year of publication, types of article, country, participant characteristics (age, gender, type of disabilities), assessment tools with reliability score, sample size, mean and SD for each variable, effect size or statistical analysis results. Extraction data were recorded using Microsoft Excel.
Figure 1. PRISMA Flow Diagram
ion
2.4 Quality assessment
All articles that meet the selection requirements are assessed for quality. Quality assessments were performed using the Mixed Method Appraisal Tool (MMAT) version 2018 (Hong et al., 2018) used in the previous meta-analysis (see Chen et al., 2021; Xulu-Kasaba
& Kalinda, 2021). The assessment is carried out based on five criteria, each of which is worth 20%. If the article does not meet all the criteria, it will be worth 0%, and if it meets all the criteria, it will be worth 100%. Articles that are of low quality (have a value of 20%) are not included in the meta-analysis.
2.5 Statistical analysis
This meta-analysis processes the effect size data in the form of a correlation value (r) and the sample size of the participants. If the article did not provide an r score but provided a regression coefficient and SD dependent variable, which is academic performance, then the effect size was calculated and converted using the R studio packages Esc (Lüdecke, 2018).
We performed a meta-analysis using Jamovi (The jamovi project, 2021).
Heterogeneity was analyzed using the DerSimonian and Laird (DL) estimator model considering the value of I2. The total effect size was analyzed using a random effects model and visualized in the form of a forest plot. Finally, the risk bias assessment was analyzed using Egger's Regression and visualized in the form of a funnel plot.
3. Results and Discussion
3.1 Result
We found 325 potential articles from selected databases. After excluding 15 duplicate articles, we checked the titles, keywords, and abstracts of 313 articles as a preliminary screening process. Thirty-two articles were selected for the full-text screening process, and 29 articles did not meet all criteria. In addition, we conducted a manual search from references in potential articles and found 12 potential articles to be screened, of which three met all criteria. Finally, six studies that remained were retained in this meta-analysis.
We provide a summary of the included studies in Table 2. The studies were published between 2010 and 2017. All studies were conducted in a higher education setting in the United States. The total number of participants in the studies was 906, ranging from 20 to 325 (mean=151, SD=113). Among all participants, 61.9% were female with disabilities. Five studies provided mean age of the participants ranging from 20.0 to 30.4. Only Hanna (2016) specified a student with autism-specific disorder, whereas another five studies involved a diverse type of disability. The most common type of disability is Attention Deficit and Hyperactivity Disorder (ADHD)/attention deficit disorder (ADD) or Learning Disorder (LD), which accounts for 49.3% of the total participants.
Table 2. The summary of the included studies in this meta-analysis Reference The type of
publication Country Participant’s characteristic SA Measurement n Age
(M;
SD)
Gender Type of disability (n)
Murray et al. (2014) Peer
reviewed journal
United
States 200 22.9;
5.98 81 males, 119 females
ADHD (68), LD (61), health cond. (23), mental health (17), hearing (9), brain injury (7), physical (3), neurological (3), visual (3), other (6)
SA factors from CSDCC
Fleming et al.
(2017)
Peer reviewed journal
United
States 325 27.62;
10.58 107 males, 218 females
ADHD or LD (114), deaf or hearing (14), mobility impairment (12), intellectual or cognitive (5), brain injury (25), chronic health (44), psychological or mental health (80), visual (7), autism spectrum (13), other (11)
SA factors from CSDCC
Kinney and Eakman (2017)
Peer reviewed journal
United
States 49 30.39;
6.5 41 males,7 1 other
PTSD (28), TBI (22), physical or orthopedic (22), sensory (10), anxiety (7), other physical condition (6), depression (5), other psychological conditions (5), cognitive (4), LD, dyslexia, or ADD (4)
SV-SASA
Lombardi et al.
(2011)
Peer reviewed journal
United
States 197 na 73 males, 124 females
ADD/ADHD (75), LD (77), psychological (36), other (17), health cond (12), hearing (9), mobility/orthopedic (9), visual (6), neurological/seizure (3), head injury (3)
SA factors from CSDCC
Hanna
(2016) Master
theses United
States 20 20; na 8 males, 12 females
ASD (20) SA factors
from CSDCC
Adams and Proctor (2010)
Peer reviewed journal
United
States 115 26.67;
10.27 34 males, 81 females
LD (48), physical or sensory (45), mental/psychiatric (18), other (4)
SAQ
All studies were cross-sectional. Five studies were published in peer-reviewed journals, and only one was a master thesis. All studies used GPA as a measurement of academic performance; however, a variety of self-advocacy measures was used. Self- advocacy factors from College Students with Disabilities Campus Climate (CSDCC) were widely used instruments to measure self-advocacy (k=4). It had six items ranging from 0.80 to 0.85. Furthermore, another measurement was Self-advocacy Questionnaire (SAQ, 15 items, 𝜶=0.88) and Student Veteran Self-advocacy Skill Assessment (SV-SASA, 7 items 𝜶=0.88). CSDCC and SA were measurements for students with disabilities, whereas SV- SASA was specific for student veterans with disabilities. Since only Kinney & Eakman (2017) used SV-SASA, we notice that it involved veteran students as participants, which is different from other studies. Also, we notice that Hanna (2016) only involved participants with ASD, whereas the other studies included students with various disabilities.
Figure 2. Forest plot
The estimated effect sizes were 0.29 (p<0.001; 95% CI=0.195–0.378), so there is significant evidence that self-advocacy is positively correlated with academic performance. According to Cohen (1992), the total effect size is included in the small category. Meanwhile, the heterogeneity test using the DerSimonian and Laird (DL) model estimator showed moderate heterogeneity (Q(5) = 9.98, tau2 = 0.0061, I2=48.9%, p=0.076). This is relevant to the finding that all studies presented a positive correlation between self-advocacy and academic performance and have CI lower and upper-bound >0, except Hanna (2016) that had the largest variance range likewise CI lower bound <0. The Forest Plot in Figure 2 shows the detailed effect size in each study as well as the total effect size in this meta-analysis. Egger’s regression estimated any funnel
plot asymmetry (z=1.712, p=0.087) thus detected any risk of publication bias. The visualization of the funnel plot is shown in Figure 3.
Figure 3. Funnel plot
3.2 Discussion
All studies stated that self-advocacy and academic achievement have a positive relationship. The results of this meta-analysis corroborate this and emphasize that self- advocacy of students with disabilities and academic achievement are positively correlated with low strength. The results of this meta-analysis are relevant to the literature review by Moriña and Biagiotti (2021) and Ju et al. (2017), which state that self- advocacy is one of the factors that supports the success of students with disabilities. Self- advocacy helps students with disabilities to communicate the learning accommodations they need to teachers and disability service units so that they get support according to their needs (Getzel & Thoma, 2008). Pfeifer et al. (2020) also stated that self-advocacy helps students with disabilities to recognize opportunities to get learning assistance and support when the support system available on campus is inadequate. Self-advocacy is an important determinant so that students with disabilities can get support for the smooth running of their learning and ultimately achieve satisfactory academic achievement, among the practice of inclusion in higher education, which is not ideal yet.
The necessity that needs to be observed from the results of this meta-analysis is that there is still a tendency to detect heterogeneity at a moderate level. Although in general it can still be tolerated, there are indications of various research characteristics that can
affect variations in findings. Referring to the visualization of the forest plot, Hanna (2016) has the widest CI range, crossing the value of 0. This study also has the lowest fitting weight model among the results of other studies and has characteristics that are different from other studies, related to the small number of participants (n=20) and specifically only involving students with ASD. In addition, Kinney and Eakman (2017) also have a wide CI range and an effect size that is far above the upper-bound of the total CI. It also has participant characteristics that are different from other studies because it only involves veteran students. The effect size in this study is also far above the upper-bound estimated effect size.
Based on this explanation, there is a possibility that there are differences in the correlation between self-advocacy and academic achievement in special populations. In particular, this deviation was detected from the results of research with ASD participants Hanna (2016) and student veterans (Kinney & Eakman, 2017). Since there are few studies regarding self-advocacy and academic achievement in ASD and student veteran populations, replication and re-examination are necessary to ensure the effect size in these populations. Also considering that there is a large diversity of students with disabilities, it is also necessary to examine whether other variations affect the size of the effect size, such as in the postgraduate student population, the population of students with intellectual disabilities, physical and mental health problems, or other inter- sectional characteristics.
Based on the assessment of the risk of bias, it was found that there was an asymmetrical tendency in the funnel plot, so there is a risk of bias that must be considered. The risk of bias in research may be the result of publication bias or other factors, such as variations in data collection procedures, standards, quality of study design, or unpublished results (Song et al., 2002). From the results of the quality assessment, it was observed that most of the research was deemed not to have adequate output data. . Fleming et al. (2017), Lombardi et al. (2011), and Murray et al. (2014) have a low response rate of less than 50%. Meanwhile, as previously discussed, Hanna (2016) has a very small number of participants, even at the same time reporting missing data.
Meanwhile, Adams and Proctor (2010) and Kinney and Eakman (2017) have not reported any information regarding the response rate or missing data.
One of the risks that may occur is the susceptibility of bias in the parameters of academic achievement measurement. All studies use GPA parameters to identify academic achievement. Rodríguez-Hernández et al. (2020) stated that GPA is the most popular measurement used to identify academic achievement. Note that the GPA assessment systems and standards in various studies are relatively different from one another. In general, GPA is a scoring system that consists of a 4.0-point scale, such as Adams and Proctor (2010) and Murray et al., (2014). Whereas Lombardi et al., (2011) have a different range that is range from 0 to 4.3 (0=F; 4.3=A+). The other articles do not explain the GPA ranges used. Unlike other instruments that have been tested for validity and reliability, most of the articles do not explain how researchers guarantee that GPA can be
a valid and reliable indicator of academic achievement. Murray and Wren (2003) argued that GPA is considered an unstable metric because it is very susceptible to subjective judgments and teacher bias. Kuncel et al. (2005) added that GPA is an invalid indicator of academic achievement for minority student populations and students who have low GPA scores.
A striking difference also occurs in GPA data collection techniques. Kinney and Eakman (2017), Lombardi et al. (2011), and Murray et al. (2014) obtained GPA from university records, while Adams and Proctor (2010), Fleming et al., (2017), and Hanna (2016) obtained it from self-reports. Self-reported GPA is considered more practical and widely practiced in various studies. However, Kuncel et al., (2005) underlined that self- reported GPA is prone to bias and individuals may report erroneous GPA values due to inaccurate memory factors. Another alternative is that researchers can obtain GPA scores from university records, which guarantee the validity of the GPA scores obtained from researchers. This method can be used by researchers who have access to the institution database, but there will be ethical issues related to participant confidentiality if the researcher is not part of the institution (Gonyea, 2005).
In general, academic achievement is not the only parameter to identify student success. One of the limitations of this meta-analysis is that it only focuses on one indicator of student success, namely academic achievement. Related to the vulnerability of using GPA as a parameter, future researchers can involve other parameters of student success such as retention, graduation, or success in getting a job after graduation. Other parameters can strengthen the validity and reliability of the GPA as an indicator as well as strengthen the claims of self-advocacy effects on the success of students with disabilities.
4. Conclusion
This meta-analysis supports the assumption that self-advocacy is positively correlated with the academic achievement of students with disabilities with a low effect size. Note that the estimated effect size may not be relevant for research on special populations with disabilities, for example in groups of student veterans or groups of students with ASD. In the future, data regarding the correlation between self-advocacy and academic achievement are still needed to ensure that self-advocacy is a determinant that needs to be considered in supporting the success of students with disabilities.
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