Research Article


Effect of Undergraduate Research Output on Faculty Scholarly Research Impact


Adriana Popescu

Interim Dean of Library Services

Robert E. Kennedy Library

Cal Poly

San Luis Obispo, California, United States of America



Radu Popescu

Lecturer, Architectural Engineering

College of Architecture and Environmental Design

Cal Poly

San Luis Obispo, California, United States of America



Received: 04 July 2017    Accepted: 24 Oct. 2017   



cc-ca_logo_xl 2017 Popescu and Popescu. This is an Open Access article distributed under the terms of the Creative CommonsAttributionNoncommercialShare Alike License 4.0 International (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one.





ObjectiveIn the context of the ongoing discourse about the role of Institutional Repositories (IRs), the objective of the study is to investigate if there is any evidence of a relation between undergraduate student activity in an IR and the impact of faculty research.


Methods – The data used for the study is representative of six academic departments of the College of Science and Mathematics (CSM) at California Polytechnic State University (Cal Poly). Digital Commons@Cal Poly (DC) is the IR supported by the library. Regression analysis was used to investigate the interdependence between faculty research impact (dependent variable) and undergraduate student repository activity (independent variable). For each department, faculty research impact was quantified as a measure of the citation counts for all faculty publications indexed in Web of Science (WoS) between January 2008 and May 2017. Student repository activity was quantified for each department in two ways: (1) total number of student projects deposited in DC since 2008 (Sp) and (2) total number of student project downloads from DC (Sd). The dependent variable was regressed against each of the two elements of student repository activity (Sp and Sd), and the resulting statistics (sample correlation coefficients, coefficients of determination, and linear regression coefficients) were calculated and checked for statistical significance.


Results The statistical analysis showed that both components of student repository activity are positively and significantly correlated with the impact of faculty research quantified by a measure of the citation counts. It was also found that faculty repository activity, although positively correlated with faculty research impact, has no significant effect on the correlation between student repository activity and faculty research impact.


Conclusion The analysis considers two distinct groups of publications: one group of student publications (senior projects) from six academic departments, which are deposited in an open repository (DC), and one group of publications (not necessarily represented in DC) of faculty affiliated with the same six departments and whose citation impact is believed to be affected by the first group. The statistical correlation between student repository activity and faculty research impact can be seen as an indication that an active, open IR centered on collecting, preserving, and making discoverable student research output has a positive impact on faculty’s research impact. More research that includes additional factors and uses a larger data set is necessary to arrive at a definitive conclusion.






Cal Poly is a nationally ranked public university and part of the California State University (CSU) System, the largest public university system in United States. The school’s motto is “Learn by Doing,” which translates into a pedagogical focus on project-based curriculum. Throughout their Cal Poly experience, students actively engage in research, experimentation, studio work, and design, and the outcomes of their class experience and learning are reflected in the senior project submissions.


In 2008, the library launched Digital Commons@Cal Poly (DC), which serves as the institutional repository (IR). DC is powered by bepress (, which is used by over 500 educational institutions to preserve and showcase their scholarly output and special collections. The mission of Cal Poly’s IR is to collect, preserve, and make visible all institutional intellectual output, including pre-prints, working papers, journal articles, senior projects, master's theses, conference proceedings, presentations, images, and a wide variety of other content types. Although the library’s DC is an open access (OA) repository and is available for submission of student and faculty work, not all departments actively deposit to DC. The library’s ongoing efforts to promote the benefits of contributing scholarly and creative works to DC had mixed results, with some colleges (and departments) being more active participants than others.


Recent discussions about the purpose of IRs and a call to “disconnect them from the OA agenda for journal articles” and reposition them “in the broader context of managing and preserving institutional community assets” (Lynch, 2017, p. 127) triggered the interest to investigate whether community assets (e.g., student senior projects) preserved and exposed in IRs can have a positive influence on the overall faculty research impact. Senior projects, electronic theses, and dissertations represent a significant part of the institutional intellectual output. By exposing this output in IRs, libraries not only fulfill their mission to curate, archive, and preserve but by developing IRs centered on student work, they also facilitate the advancement of the faculty research agenda and profile.


Many studies have investigated the effect of open access (OA) on the research impact of publications. The general conclusion was that OA offers clear advantages over paid access with respect to accessibility and therefore visibility of published research and has a significant effect on the overall research impact expressed as a function of citation count (Brody, Harnad, & Carr, 2006; Gargouri et al., 2010). The novelty of the present study is that it aims to investigate using statistical methods whether an active, open IR centered on student work has a positive impact on faculty’s research impact independent of faculty’s participation in the IR. The study analyzes two distinct samples of publications:


1.       A group of faculty publications from the six CSM departments selected for the study for which research impact is calculated based on Web of Science (WoS) citation data

2.       A group of CSM student publications (senior projects) from the same six CSM departments that are deposited in DC


The first sample of publications consists of articles published between January 2008 and May 2017 by the faculty in the six departments of CSM at Cal Poly and indexed by WoS. Only articles published under the Cal Poly affiliation are included in the study. InCites (, a customized, web-based research evaluation tool that uses WoS data to generate institutional reports to showcase strengths and identify potential areas for growth, was used to acquire values for Journal Expected Citations (JEC) and Journal Normalized Citation Impact (JNCI) indicators.


The second sample of publications originates from DC. Three major categories of scholarly output are deposited in DC (among others): (1) faculty works (e.g., voluntarily deposited scholarly output), (2) undergraduate student senior projects, and (3) master’s theses. Deposit and download metrics for the first two categories were obtained from institutional activity reports for DC and were used in the study.


The goal was to identify a possible correlation between the scholarly impact of faculty research and undergraduate student repository activity. The faculty activity in DC is also included in the analysis to verify whether it affects the direct correlation between faculty research impact and student repository activity.


CSM at Cal Poly has a strong record of faculty and undergraduate research, which is also reflected in an active participation and submissions of student works to DC. Six departments were selected for the study: Biological Sciences, Chemistry & Biochemistry, Kinesiology, Mathematics, Physics, and Statistics. Two departments have been omitted for the following reasons: (1) faculty in the Liberal Studies Department have dual departmental affiliations (CSM and the College of Liberal Arts), and it was not possible to isolate the research contributions of the faculty specific to CSM; and (2) the School of Education does not offer undergraduate programs. The six selected departments were randomly assigned numbers, and the departments are identified in the study only by these numbers.


One limitation of the study is the small sample of academic units (N = 6), which may affect credibility of the conclusions on the grounds that some results could have been obtained by chance. To overcome this issue, all results were thoroughly checked for statistical significance.


Literature Review


Role of Institutional Repositories


The report of the Coalition for Networked Information (CNI) Executive Roundtable on “Rethinking Institutional Repository Strategies” held during the Spring 2017 CNI meeting in Albuquerque, New Mexico, highlighted the challenges faced by existing IRs (CNI, 2017). It is increasingly difficult to justify why faculty should place materials in an IR when other options, such as disciplinary repositories that meet funders open access mandates are available, or when commercially run systems, such as or ResearchGate that offer networking and analytics features, are available (CNI, 2017). Given perennial competing funding priorities, academic libraries are faced with the task of demonstrating value and return on investment for continuing to support and maintain IRs, which have been implemented, developed, and maintained since the early 2000s at significant costs and mostly supported by libraries. One way to demonstrate and make the case for the viability of an IR is to demonstrate that the undergraduate research output deposited in IR is reflected in the overall faculty research impact.


Undergraduate Research


The positive benefits of exposing and encouraging undergraduate research experiences have been studied and reported in the literature. Undergraduate research experiences translate into personal and professional gains for students and are reflected in elucidation of career paths and enhanced graduate school preparation (Seymour, Hunter, Laursen & DeAntoni, 2004). Positive impact on student retention (Gregerman, Lerner, von Hippel & Nagda, 1998) has also been reported. A limited number of studies (Lei & Chuang, 2009) show that faculty benefit indirectly because students who have gained publishing and practical original research experience while working on faculty research projects become contributors to scholarly publications. By generating publishable results from undergraduate research projects, faculty may have established a valuable future research collaboration with these students. However, no studies have been identified that attempt to demonstrate a direct influence of undergraduate research activity on faculty research impact.


Research Impact


When assessing research performance, it is important to take into account both the volume and the quality of research output. Citations are widely recognized as a proxy for quality. The citation impact quantifies the citation usage of scholarly works. Eugene Garfield, the creator of Web of Science, states that “citation frequency is a measure of research activity . . .” (Garfield, 1973), and that frequency of citations is an “indicator of quality . . . of productivity as well as impact” (Garfield, 1988). Moed (2005) discusses in detail the relationship between intellectual influence or research impact and citation impact. He shows that “even if one assumes that citations measure intellectual influence . . . intellectual influence needs to be valued in a wider cognitive framework” and that there are some factors that affect in a different manner intellectual influence and citation impact (p. 223). There are possible biases and errors in the interpretation of citation impact, and therefore, empirical analyses do not result in perfect correlations. Moed (2005, p. 224) concludes, however, that the fact that these correlations are positive provides an empirical justification for relating citation impact to intellectual influence (or research impact—as it is termed in this study). He further shows that analysis bias may be reduced to a considerable extent when analyzing aggregates of entities that have some aspects in common rather than analyzing individual units (p. 225).


Citation counts, or Times Cited (TC), were first used to evaluate importance of scientific work by Gross and Gross (1927) and since then have remained the main means to characterize research impact. While TC is a meaningful and accessible way to reflect scholarly output and measure the impact of an individual researcher, a group, or an institution, Garfield (1972) warned that TC is a function of many other variables besides scientific impact. Bornmann and Daniel (2008) list and discuss some of those factors: (1) time of publication, with more citations to recent than to older publications; (2) field of research, with the citation potential varying significantly from one field to another; (3) journal frequency of publication and journal impact factor; (4) article type (e.g., review, research, letter, note), language, and length; (5) number of coauthors; and (6) accessibility (i.e., OA or paid access).


To alleviate the effect of some factors, one can look at how the citation count (TC) compares with expected citation count for a field or discipline or for a specific journal. The expected citation count is available for most of the journals indexed in WoS as the Journal Expected Citations (JEC) indicator (Clarivate Analytics, 2017). The ratio between TC and JEC, for example, becomes a qualitative measure of the research impact that can be compared across various publications and even various disciplines.


Data Used in the Study


Independent Variables


Two categories of DC repository activity were included in the study as described below. The data were obtained from Cal Poly's DC institutional activity reports.


1.       Undergraduate student activity consisted of senior projects and was quantified for each department through the project counts (Sp) and the project download counts (Sd). As of May 2017, DC contained 263 undergraduate student projects totaling about 276,000 downloads for the six CSM departments considered. These data were normalized by the number of faculty (NF) in each department and are listed in columns 2 and 3 of Table 1. These data represent the independent variables for the correlation sought after in this study.

2.       Faculty activity consisted of research articles deposited in DC and was quantified for each department through the paper counts (Fp) and the paper download counts (Fd). This activity was included in the study to investigate if the correlation between student activity in DC and faculty research impact is in fact a result of the faculty repository activity in DC. These data were normalized by NF and are listed in columns 4 and 5 of Table 1.


The indicators of student repository activity (Sp and Sd) and faculty repository activity (Fp and Fd) were normalized by the department size expressed as number of faculty (NF). Given that all departments discussed here belong to the same academic unit, and most likely have comparable resources, the size of each department can be expressed as a function of either NF or the number of students. As the number of students in a department may vary significantly from one academic year to another, NF was selected as a measure of the department size.


Some of the CSM departments also offer graduate programs, and master’s theses are usually deposited in DC. The effect of graduate student repository activity on faculty research impact will be analyzed in a future phase of the study.


Dependent Variable


The dependent variable in this study is a measure of the scientific impact of all faculty in each department quantified by a measure of the citation count of their publications. As discussed in the literature review, faculty research impact or performance (in short research impact) can be quantified by a measure of the citation count from faculty publications. Raw citation counts are affected by other factors besides research performance. The measure of citation count used here aims to eliminate most of these factors. In this respect, InCites provides the Journal Normalized Citation Impact (JNCI) indicator for each publication. The JNCI is the total number of citations per paper (TC) “normalized for journal, year and document type subject” (Clarivate Analytics, 2017, p. 18). The normalizing factor is the Journal Expected Citations (JEC) indicator defined as the “average number of citations to articles of the same document type from the same journal in the same database year” (Clarivate Analytics, 2017, p. 18).



Table 1

Data Used in the Study



Repository activity in DC

(all values are divided by NF)

Faculty research impact indicator (JNCIav)

Undergraduate student projects

Faculty papers

Project count (Sp/NF)

Download count (Sd/NF)

Paper count (Fp/NF)

Download count (Fd/NF)







































In reference to the list of factors affecting TC discussed in the literature review, use of JEC as a normalizing factor eliminates the influence of the first three factors in the list (time of publication, research field, and journal impact factor). Given the relatively large groups of papers analyzed here, the elements characterizing the other three factors can be considered to be roughly similar for all departments. Based on these considerations, the JNCI indicator is used to assess the scientific impact of each individual paper. Each individual value of JNCI shows if the paper has been cited more than expected (JNCI > 1) or less than expected (JNCI < 1).


The research impact, denoted as JNCIav, is a qualitative measure of the impact of the faculty publications, is defined for an entire department, and is calculated here as the average of all JNCI values for all papers indexed by WoS published by the faculty in each department between January 2008 and May 2017 (a total of 871 articles for the six departments). Only active faculty as of May 2017 (according to departmental directory listings) have been considered in the study. InCites was used to extract and process WoS data used to calculate the research impact indicator.


A series of issues exist when using this research impact indicator:


1.       For some journals, the JEC value listed by InCites is zero or is not available; therefore, JNCI cannot be calculated.

2.       If the value of JEC is very small, one single citation would result in unusually large values of JNCI that may bias the resulting average value for some departments.


These limitations were addressed as follows:


1.       The papers where JEC is not available or zero were not included in the JNCIav indicator calculation. These papers represent 14% of all papers considered in this study.

2.       The papers with JEC lower than a given threshold were also eliminated from the research impact calculation. The threshold selected was JEC = 0.1. An additional 10% of all papers considered were eliminated due to this filter.


Thus, the indicator used to characterize the research impact in each department is the average of JNCI for all papers that have JEC ≥ 0.1. This indicator is referred to as research impact and denoted by the symbol JNCIav. The values of JNCIav for the six departments considered are listed in the last column of Table 1.


Tests for Normality


This study used linear regression analysis between the independent variables (various aspects of student repository activity) and the dependent variable (faculty research impact indicator). Though there is no general requirement for the input data in a regression analysis to be normally distributed, certain statistical tests used in the next section require normality, especially for small samples (Devore, 2000, p. 533). Therefore, the data used here is first checked for normality and transformed if necessary to achieve normality.


The test for normality is in general easily met for very small samples such as those in this study. One way to qualitatively assess the goodness of fit with the normal distribution is to visually compare the quantile-quantile plots (or QQ-plots) of the sample versus theoretical quantiles from the normal distribution. As the sample is closer to normal, the QQ-plot is closer to a straight line. QQ-plots for the quantities used here are presented in Figure 1 and are used to estimate whether original sample data or logarithm of sample data is closer to a normal distribution. Based on visual comparison, it appears that logarithms of the values in Table 1 are closer to the normal distribution for normalized student project downloads, Sd/NF, and for research impact indicator, JNCIav. No conclusion could be obtained from the plots regarding the normalized student project counts, Sp/NF.


Statistical quantitative assessments for goodness of fit are also available. The most popular test for assessing normality of a sample is the Chi-square test, but the sample size used here is too small to provide reliable results. Two other tests are used that accept small sample sizes, namely Kolmogorov-Smirnov (Massey, 1951) and Ryan-Joiner (Devore, 2000, p. 634). Based on these two statistical tests, all data sets fit the normal distribution at the 5% level of significance, but the log-value sets are closer to a normal distribution than the original values for all sets listed in Table 1. Therefore, to obtain samples closer to the normal distribution, logarithm of all values listed in Table 1 (independent and dependent variables) are used in the regression analyses. The statistical level of significance is briefly discussed in the next section.


Analysis and Results


Correlation Between Faculty Research Impact and Undergraduate Student Activity in DC


Regression Analyses


Regression analysis explores the relationship between two or more variables related in a nondeterministic fashion (Devore, 2000, p. 489). More specifically, a regression analysis between two sets of measured quantities, the dependent variable denoted by y and the independent variable denoted by x, explains how y changes as a function of the changes in x, or, in other words, it expresses y as a function of x. This function,  = f(x), is called regression function or regression model. Note that, for any value of x, the result of f(x) is not necessarily equal to the corresponding measured value of y but to a predicted value, . Linear regression seeks to find a linear functional relationship between y and x. In simple linear regression, as described here, there is only one independent variable. In multiple linear regression analysis, as described later in the section titled Effect of Faculty Activity in DC on Research Impact, the analysis includes more than one independent variable.



Figure 1

Quantile-quantile plots for assessing normality of the data samples used in the study.




The strength of the relation between y and x is measured through a series of quantities obtained from the regression analysis, such as the coefficient of determination and the sample correlation coefficient. The coefficient of determination, R2 (or R-squared), is defined as the proportion of the variance in the dependent variable that can be explained by the linear regression model (Devore, 2000, p. 506). In simple linear regression, R2 is equal to the square of the sample correlation coefficient between the independent and the dependent variables. This correlation coefficient, denoted here by R, is a measure of the strength of the linear association between the two quantities. The functional relationship between the dependent and the independent variables is expressed in simple linear regression as  = a0 + a1x. The parameters a0 and a1 are the intercept and the slope of the regression line and are also a result of the regression analysis.


The regression function in Excel has been used to perform two linear regression analyses: (1) between faculty research impact indicator, log(JNCIav), and the normalized student project counts, log(Sp/NF), and (2) between log(JNCIav) and the normalized student project downloads, log(Sd/NF). The results are shown in Figure 2. The dependent variable in these analyses is the research impact indicator, represented by a set of six observed values—the log of values shown in the last column of Table 1. The independent variable in each regression analysis, either log(Sp/NF) or log(Sd/NF), is also represented by a set of six observed values. Figure 2 shows scatter plots of the data along with the regression line (predicted values) that is estimated using the least squares method. The markers represent observed values, namely log of the values in Table 1. The other results discussed previously (R2 and the regression line parameters) are also shown in Figure 2. 



Figure 2

Results of regression analysis between undergraduate student repository activity and research impact.




The predictive linear equations are of the form  = a0 + a1x, where  = log() is the predicted log-value of research impact and x = log(Sp/NF) or x = log(Sd/NF). These equations can be written as power equations in terms of the original data from Table 1 as  = b0*vb1, where  =  and v = Sp/NF or v = Sd/NF. With the values of a0 and a1 shown in Figure 2, the predictive equations become:




Discussion of Regression Analysis Results


At this juncture, two questions still need to be addressed: (1) how significant is the linear dependence between research impact and student repository activity? and (2) how significant are the calculated regression line parameters a0 and a1? This significance is investigated here by means of statistical hypothesis testing that is used to check the validity of a result at a certain level of significance, α. A commonly accepted significance level, also selected here, is α = 5%. A simple interpretation of the level of significance in statistical testing can be stated as follows: when accepting the hypothesis that a certain quantity is statistically significant at the α = 5% level of significance there is still a 5% chance that the hypothesis is false. (NOTE: For brevity, the ad-hoc definition of significance level stated here is based on the alternate hypothesis, H1, rather than on the null hypothesis, H0.) The significance of regression analysis results was investigated using three statistical tests.


The strength of the linear dependence between faculty research impact and student repository activity was first verified through the p value of the observed relationship. This p value is an output of the regression function in Excel that directly indicates the level of statistical significance of the relationship between the dependent and independent variables (see Devore, 2000, p. 394 for more details on p value). For the level of significance selected, α = 5%, a calculated p value < 0.05 indicates that the observed relationship is significant at least at the 5% level (i.e., there is less than 5% probability that this relationship resulted by chance). The calculated p values for the two regression analyses are listed in Table 2.


The strength of the linear dependence between research impact and student activity was also assessed by comparing the calculated sample correlation coefficients, , with the minimum significant value of R at the α level of significance:




where Z is the standardized normal random variable and N = 6. For α = 5%, . The test, described in detail by Bendat and Piersol (2010, pp. 99-101), states that there is evidence of statistical correlation at the α level of significance if the absolute value of the sample correlation coefficient is . The resulting sample correlation coefficients are compared with  in Table 2.


One common type of statistical hypothesis testing uses t statistics (Devore, 2000, pp. 296-301). The t statistic of a certain result to be tested for significance is compared with the critical value from t distribution. The critical value depends on the number of degrees of freedom, n, and on the level of significance, α. Critical values of t distribution are tabulated in any statistics textbook. The critical t distribution value corresponding to the regression analyses performed here, with n = 4 degrees of freedom (n = N − 2 for simple linear regression, with N = 6, the sample size) and level of significance α = 5%, is tn(α)/2 = 2.776. If the absolute value of the t statistic for a certain parameter is larger than or equal to 2.776, the respective parameter is considered statistically significant at the 5% level. The regression function in Excel provides t statistic values for the regression parameters, a0 and a1. These t statistics are compared in Table 2 with the critical value from t distribution, t4,2.5% = 2.776.


From comparing the values in Table 2, it is concluded that all parameters considered here meet all statistical tests at the 5% level of significance. Therefore, there is significant linear dependence between student repository activity and faculty research impact, and the calculated linear regression coefficients can be used with confidence in a predictive model.


Effect of Faculty Activity in DC on Research Impact


As inferred from several previous studies on the effect of OA on research impact (Brody, Harnard, & Carr, 2006; Gargouri et al., 2010), faculty repository activity (self-archiving of faculty papers and download counts) in DC is expected to be correlated with faculty research impact. Even in the presence of significant correlation between student repository activity in DC and faculty research impact, a question arises: Could this correlation be a result only of the two variables (student repository activity and faculty research impact) each being strongly correlated to faculty repository activity? If so, then faculty repository activity may be the determining factor for research impact. Two variables being strongly correlated to a third variable is known as severe multicollinearity. The following analysis answers the question noted and determines whether severe multicollinearity exists in this situation.



Table 2

Hypothesis Testing of Regression Analysis Results at 5% Level of Significance

Statistics from regression analysis

Regression between log(JNCIav) and log(Sp/NF)

Regression between log(JNCIav) and log(Sd/NF)

Critical values

Strength of linear relationship

p value

0.006 < 0.05

0.029 < 0.05

pmax = 0.05

Sample correlation coefficient, R

0.937 > 0.812

0.859 > 0.812

Confidence in regression parameters

t statistics for a0

3.215 > 2.776

|–2.906| > 2.776

t4,2.5% = 2.776

t statistics for a1

5.346 > 2.776

3.351 > 2.776



Table 3

Sample Correlation Coefficients Between Various Pairs of Data Used in this Study

Data pairs

Sample correlation coefficient

Student project count, log(Sp/NF), and research impact, log(JNCIav)


Faculty paper counts in DC, log(Fp/NF), and research impact, log(JNCIav)


Student project count, log(Sp/NF), and Faculty paper count in DC, log(Fp/NF)


Student project downloads, log(Sd/NF), and research impact, log(JNCIav)


Faculty paper downloads in DC, log(Fd/NF), and research impact, log(JNCIav)


Student project downloads, log(Sd/NF), and Faculty paper downloads, log(Fd/NF)




Sample Correlation Coefficients


Significant correlation indicates strong linear dependence. As discussed earlier and as shown in Table 3, significant correlation exists between faculty research impact and student activity in DC (both student project counts and student project downloads) with values of the sample correlation coefficients R = 0.937 between log(JNCIav) and log(Sp/NF) and R = 0.859 between log(JNCIav) and log(Sd/NF), which are both larger than the critical value, . 


To investigate the effect of faculty repository activity in DC on research impact, sample correlation coefficients between other pairs of data have been calculated using the correlation function in Excel and are listed in Table 3. The sample correlation coefficient between log(JNCIav) and log(Fp/NF) is R = 0.741 and between log(JNCIav) and log(Fd/NF) is R = 0.625. Both values are smaller than , meaning that they do not pass the statistical test discussed before. This indicates that the correlation between faculty repository activity and research impact is not statistically significant at the 5% level, and therefore the dependence is not as strong as the one between research impact and student activity in DC.


The sample correlation coefficients between the two types of independent variables resulted as follows:


·         Between log(Sp/NF) and log(Fp/NF): R = 0.632, which is smaller than the corresponding correlation coefficients between each independent variable and the dependent variable, or 0.937 and 0.741

·         Between log(Sd/NF) and log(Fd/NF): R = 0.29, which is smaller than 0.859 and 0.625


Lower correlation between the independent variables than between each independent variable and the dependent variable (research impact) indicates that there is no severe multicollinearity.



Table 4

Adjusted R2 Between Research Impact Indicator and Repository Activity in DC

Regression analysis


Effect of adding factor

1. Between log(Sp/NF) and log(JNCIav)


86% − 44% = 42%

2. Between log(Fp/NF) and log(JNCIav)


86% − 85% = 1%

3. Between log(Sp/NF) & log(Fp/NF), the independent variables, and log(JNCIav), the dependent variable



4. Between log(Sd/NF) and log(JNCIav)


82% − 24% = 58%

5. Between log(Fd/NF) and log(JNCIav)


82% − 67% = 15%

6. Between log(Sd/NF) & log(Fd/NF), the independent variables, and log(JNCIav), the dependent variable






Adjusted R2


The adjusted R2 (AdjR2) is a modified version of R2 that is adjusted for the number of independent variables in the model and is always lower than R2. AdjR2 is one of the results of the regression analysis in Excel and is useful in multilinear regression analysis. The difference between AdjR2 of a bilinear regression analysis with independent variables x1 and x2 and the AdjR2 of a simple linear regression using only x1 indicates by how much the regression model is improved by adding the variable x2.


The resulting values of AdjR2 from the simple linear regression analyses discussed in the previous subsection are included in the second column of Table 4 (analyses 1 and 4). Two additional simple linear regression analyses were performed between the components of faculty activity in DC (independent variables) and the research impact (dependent variable). The resulting AdjR2 values are listed in Table 4 (analyses 2 and 5). Two bilinear regression analyses were also performed, and the resulting AdjR2 is listed in Table 4:


·         log(Sp/NF) and log(Fp/NF) as independent variables versus log(JNCIav); see analysis 3

·         log(Sd/NF) and log(Fd/NF) as independent variables versus log(JNCIav); see analysis 6


Finally, the third column of Table 4 shows by how much each independent variable would improve a linear regression model between another independent variable and the research impact. For example, a linear model linking log(Fp/NF) and log(JNCIav) is improved by 42% (86% − 44%) by adding log(Sp/NF) in the model, while a linear model linking log(Sp/NF) and log(JNCIav)is improved by only 1% (86% − 85%) by adding log(Fp/NF) in the model. From these results, it is clear that the student paper downloads (Sd) and student paper counts (Sp) contribute more significantly to the bilinear regression model for predicting research impact than the corresponding quantities from faculty papers deposited in the DC.


It is therefore safe to consider that, for the data analyzed here for the six CSM departments, the impact of faculty research can be correlated with the student research activity in DigitalCommons@Cal Poly with little interference from the CSM faculty deposits in DC. Note that this conclusion does not imply that the open availability of faculty works in DC has little influence on the faculty research impact. In this study, the correlation between faculty repository activity and research impact resulted weaker than the correlation between student repository activity and research impact This is probably due to the fact that faculty also participate and deposit OA publications in other repositories (disciplinary or commercial).




In the context of the ongoing conversation surrounding the role of IRs, this study investigates statistically if an IR focused on stewarding, preserving, and disseminating materials created by the student community has a positive impact on the visibility and performance of faculty scholarship, independent of faculty’s participation in the IR. This is done by analyzing two distinct samples of publications:


1.       A group of faculty publications from six CSM academic departments for which research impact is calculated based on WoS citation data

2.       A group of CSM student publications (senior projects) from the same six CSM departments that are in DC


The main conclusion of the statistical analysis is that student repository activity, quantified through undergraduate senior student projects deposited in an open IR and the download counts of these projects, is significantly correlated with the research impact of faculty publications, expressed as a measure of the citation counts. The authors postulate two factors that may contribute to this strong dependence.


The first factor is that undergraduate student senior projects follow (and sometimes anticipate) the topics of faculty research. Having student work deposited in an open IR, where it is easily discovered and accessed may constitute an effective conduit for promoting faculty research.


The second factor is rooted in the causality between student research quality and faculty research quality. For the departments analyzed, the results may indicate that the student research quality, quantified through download counts, reflects the quality of faculty research. It can be argued that the number of project downloads may not reflect quality of scholarly output on the same level as citations; however, downloads are still considered a significant quality indicator (Haustein, 2014). Haustein’s study surveyed bibliometricians to assess their opinions on the potential of alternative metrics (altmetrics). While the bibliometrics experts surveyed expressed mixed opinions on the value of altmetrics, 72% still valued download counts as a valuable source of impact data. Moreover, student project citations are not easily tracked; therefore, no other indicator was available for this study to infer student research quality besides IR downloads. Faculty repository activity in DC, while also positively correlated with the faculty research impact, had no significant effect on the correlation between student repository activity and faculty research impact.


To maintain some uniformity in the data, the study was performed on a coherent group of departments from the same college (CSM). This resulted in a relatively small sample of data (N = 6), which may be regarded as a limitation of the study. To overcome this issue, all results were thoroughly checked for statistical significance.


Though no definitive conclusion can be drawn based on the analysis of only six academic departments, the present study can be viewed as a first step in a broader research process that can be extended to investigate, among other factors, the effect of master’s theses IR exposure, direct correlation between individual faculty research impact and student advisees’ IR activity, and differences in scholarly communication practices across disciplines.




Bendat, J. S. & Piersol, A. G. (2010). Random data, analysis and measurement procedures. (4th ed.). Hoboken, NJ: Wiley.


Bornmann, L. & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45-80. 


Brody, T., Harnad, S. & Carr, L. (2006). Earlier web usage statistics as predictors of later citation impact. Journal of the American Association for Information Science and Technology (JASIST), 57(8), 1060-1072.


Clarivate Analytics (2017). InCites indicators handbook. Retrieved from


CNI (2017). Rethinking institutional repository strategies: Executive roundtable report, May 2017. Retrieved from


Devore, J.L. (2000). Probability and statistics for engineering and the sciences (5th ed.). Pacific Grove, CA: Duxbury.


Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178, 471-479. Retrieved from


Garfield, E. (1973). Citation frequency as a measure of research activity and performance. Essays of an Information Scientist, 1, 406-408. Retrieved from


Garfield, E. (1988). Can researchers bank on citation analysis? Essays of an Information Scientist11, 354-356. Retrieved from


Gargouri, Y., Hajjem, C., Larivière, V., Gingras, Y., Carr, L., Brody, T. & Harnad, S. (2010). Self-selected or mandated, open access increases citation impact for higher quality research. PLoS ONE, 5(10): e13636.


Gregerman, S. E., Lerner, S. J., von Hippel, W. & Nagda, B. A. (1998). Undergraduate student faculty research partnerships affect student retention. The Review of Higher Education, 22(1), 55-72. 


Gross, P. L. K. & Gross, E. M. (1927). College libraries and chemical education. Science, 66, 385-389. Retrieved from


Haustein, S., Peters, I., Bar-Ilan, J., Priem, J., Shema, H., & Terliesner, J. (2014). Coverage and adoption of altmetrics sources in the bibliometric community. Scientometrics, 1-19.


Lei, S. A. & Chuang, N. (2009). Undergraduate research assistantship: A comparison of benefits and costs from faculty and students' perspectives. Education, 130(2), 232-240.


Lynch, C. (2017). Updating the agenda for academic libraries and scholarly communications. College & Research Libraries, 78(2):126-130.


Massey, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68-78.


Moed, H.F. (2005). Citation analysis in research evaluation. Dordrecht: Springer Netherlands.


Seymour, E., Hunter, A-B., Laursen, S. L. & DeAntoni, T. (2004). Establishing the benefits of research experiences for undergraduates in the sciences: First findings from a three-year study. Science Education, 88(4), 493-534.








Level of significance


Intercept of the regression line


Slope of the regression line


Adjusted R2


Coalition for Networked Information


College of Science and Mathematics at Cal Poly


Number of faculty papers downloads in DC


Number of faculty papers deposited in DC


Institutional Repository


Institute for Scientific Information


Journal Expected Citations


Journal Normalized Citation Impact


Average of JNCI for all faculty publications in one department


Number of degrees of freedom


Sample size


Number of faculty in a department


Open access publication


Quantile-quantile plot


Sample correlation coefficient


Coefficient of determination


Number of undergraduate student project downloads from DC


Number of undergraduate student projects deposited in DC


Times cited (or citation count for a given paper)


Web of Science

x, x1, x2

Independent variable


Dependent variable

Predicted dependent variable


Standardized normal random variable