Exploring online internships amidst the COVID-19 pandemic in 2020: Methods

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The research design used for this study is a multi-site case study, where the cases are bounded units
of social action where internship programs are designed and/or experienced by college students. In
this study we have three distinct cases: (1) two independent websites that provide online internship
networking platforms (OINP) for students seeking online internships and employers seeking student
interns, (2) 11 colleges and universities, and (3) a single employer-hosted online internship program at
TreeHouse Foods.
Our approach to case study analysis is more focused on comparing the experiences of students across
these different situations rather than providing in-depth and multi-faceted accounts of individual cases
(Yin, 2017), given the focus on the breadth of online internship experiences during the COVID-19
pandemic of 2020-2021. The research questions that guided our study are:

The research design used for this study is a multi-site case study, where the cases are bounded units
of social action where internship programs are designed and/or experienced by college students. In
this study we have three distinct cases: (1) two independent websites that provide online internship
networking platforms (OINP) for students seeking online internships and employers seeking student
interns, (2) 11 colleges and universities, and (3) a single employer-hosted online internship program at
TreeHouse Foods.
Our approach to case study analysis is more focused on comparing the experiences of students across
these different situations rather than providing in-depth and multi-faceted accounts of individual cases
(Yin, 2017), given the focus on the breadth of online internship experiences during the COVID-19
pandemic of 2020-2021. The research questions that guided our study are:

RQ1: How many students successfully completed an online internship in 2020, and what were
their demographic and academic characteristics (e.g., major or discipline)?
RQ2: What were some key structural features of these online internships such as duration,
compensation, type of mentorship, and the nature of interns’ tasks? Were these features
associated with particular student demographic or academic characteristics?
RQ3: How do students rate their satisfaction and developmental value (both academic and
career-related) of their online internship experience?
RQ4: How, if at all, do these data compare with students pursuing in-person internships?

These questions were pursued with respect to the three different types of cases included in our study. In
the remainder of this section we briefly review the sampling procedures used for each case, the nature
of the data collection instruments and subsequent datasets, analytic techniques, and limitations with the
overall study.

Case #1 Survey study of 11 colleges and universities (10 four-year, 1 two-year)

In order to provide insights about the prevalence, type, and outcomes of online internships among a
broader population of college students than available from the OINP’s, we drew upon data collected for a
larger study of college internships underway at our Center. The data reported here were collected as part
of a pilot phase of a new national, survey-based study, and included 11 colleges and universities.

Sampling and data collection
The 11 institutions volunteered to participate in the current study via registering for the pilot study on
internship list-serve. All the of the institutions conducted data collection using all registered undergraduate
students. Institutions distributed the online survey using an anonymous link to their students through
various channels such as their students serve list, event webpage, career center portal, etc. Table 4 listed
all participating institutions, their institution type, state, survey population (which is the total number of
registered students at each campus), sample size of the collected dataset, as well as response rate. Overall,
the study sample for the current analysis includes 9,964 students with an average response rate of 8.53%.

The survey was administered between November 2020 and March 2021, with the survey eliciting
responses about the students’ prior 12-months of experiences with internships and/or their desire to
pursue an internship. The survey was based on an instrument developed for the College Internship Study
(see http://ccwt.wceruw.org/resources/researchInstruments.html), and included questions about student
demographics, characteristics of internship programs (e.g., duration, compensation, type of supervision),
and barriers to internship participation. For the instrument used in this study, students were asked to
indicate the modality of their internship if they had in fact taken one (i.e., in-person, online, other), with
subsequent questions focusing on their experience with that particular type of internship. For many
students who indicated “other,” their experience was a combination of an in-person and online internship
that we call a “hybrid” internships in this report. The survey also includes items that are intended to
be used for the Internship Scorecard framework, but in the interests of keeping the survey short some
Scorecard components were not included (see Table 2). The entire survey instrument, variable codebook,
and psychometric report is available at: http://ccwt.wceruw.org/resources/researchinstruments.html.

Data analysis

The data analysis stage began with the cleaning of the initial survey data to remove illogical answers and
incomplete responses, especially from open-ended text based questions. The final dataset included 9,964
responses from students in 11 colleges and universities, and Table 4 shows the demographic characteristics
of our study sample. Descriptive analysis, chi-square tests, and one-way Analysis of Variance (ANOVA)
were applied to answer our research questions, especially differences in the experiences of students taking
an online, in-person, or hybrid internship experience.

Limitations

Our findings should be interpreted with caution due to the following limitations. A key limitation of this
case study is that the data was limited by students in a small number of institutions, which suggests that
generalizing our findings to the entire college students in the U.S. is unwarranted. A related limitation is
that we did not control for other confounding factors that may lead to observed patterns or use more
sophisticated statistical approaches (e.g., hierarchical linear modeling) that could isolate the specific
variables contributing to observed variation in key outcome measures. However, this is not too severe of a
limitation as our analysis is focused on portraying overall internship experiences during the pandemic and
illustrating patterns between different groups in a more general sense. Future researchers are encouraged
to disentangle complex relationship around students’ internship experiences by taking into account the
multiple aspects of contexts and sample characteristics that contribute to observed variations.

Case #2: Mixed-methods study of two online internship networking platforms (OINP)

The two OINP’s that are included in the first case study were contacted by the lead author and invited
to participate in the study, whereupon leadership from each organization agreed and self-selected into
the study. One additional OINP was contacted and elected to not participate in the study. The OINPs
included in our study both function as web-based platforms where employers post internship opportunities
(students register with the website and apply for these positions), and the OINP itself posts additional
resources for students, employers and postsecondary institutions. Thus, it is inaccurate to view these
OINP’s solely as a “matchmaker” service for online internships, but instead they act as a type of job board
with additional resources and support services for parties involved in the internship process.

OINP-A is a platform that focuses on providing students with short-term, paid online internships, with the
goal to expand companies’ recruiting pools, provide work-based learning opportunities to students, and to
help college and universities expand their students’ internships options (especially during the COVID-19
pandemic). OINP-B is different in that their focus is on providing employers with a platform for running
effective online internships, with a secondary focus on providing a venue for employers to post positions
(usually longer than OINP-A) and student registrants to find opportunities. Both of these OINPs tend to
focus on internships in business, management, and other non-STEM fields and occupations.

Sampling and data collection

The original intention for this study was to send online surveys to all students registered with OINP-A
and OINP-B in the summer of 2020. The number of responses from students at OINP-A (n=44), however,
was very small and thus insufficient for further analysis. For OINP-B, our sample population was 2,493
students who had registered to join the OINP-B’s database to obtain access to the remote internship
opportunities posted on their website. A total of 183 students completed the survey, which resulted in a
response rate of 7.3%.

When registering with the OINP-B system, students filled out an online form to join OINP-B’s community
with one of the questions inquiring about the type of internships they were interested in: 1) Software
Engineering (Backend, Frontend, Data Scientist, Product, UI/UX, etc); 2) Business (Sales, Operations, similar
roles); 3) Marketing; and 4) Other. In our study sample, 32% of the students indicated interests in business
internships, 40% indicated interest in software engineering internships, and the rest indicated other fields.
It is important to note that these disciplinary preferences are not the students’ majors, which is reported
below, but instead is their indicated preference for an internship with OINP-B. Further, demographic
information of the broader population of students registered with OINP-B were not available, making
comparisons between this population and our study sample not possible. Additional information about the
study sample is provided in Table 5.

The survey was administered during November and December of 2020, with the intention of capturing
students’ online internship experiences in the prior 12-month period. The survey was based on the same
instrument described above for the 11 colleges and universities but was revised to capture the fact that all
OINP student registrants were pursuing only an online internship. For the specific variables included in the
OINP survey from the Internship Scorecard, please see Table 2.

At the end of the survey, students were asked if they were willing to participate in a brief interview, and
118 volunteered and were contacted by project staff. Of those, 45 responded and were interviewed for
approximately 20 minutes. For this qualitative portion of the study, students who had registered with
OINP-A and who had completed our survey did indicate interest in an interview, and 24 students were
subsequently interviewed from OINP-A, while 21 students from OINP-B were interviewed. A semistructured interview protocol was used that included questions about the nature of their internship
experience or obstacles to their completing one, their general experiences during the COVID-19
pandemic, issues related to technology that may have impacted their online internship and academic
pursuits, and so on.

Data Analysis

First, we cleaned the data sets with incomplete responses and illogical entry. We followed similar
approaches to the cleaning and analyses of the 11 institutions noted above, with the exception of student
major variables due to small sample size. Descriptive statistics for key variables were generated in order
to determine the participation rate in OINP-B’s programs, the structure of their online internships, and
reported outcomes. In addition, a series of chi-square tests of independence and independent samples
T-tests were conducted to evaluate the associations between student characteristics (e.g., gender, major)
and internship program features (e.g., supervisor support) and outcomes (e.g., student satisfaction).

Second, interview data were transcribed and then analyzed in MaxQDA qualitative analysis software
(VERBI Software, 2019). The first step in the analysis was to segment the transcripts into units that
encapsulated a single thought or idea. Second, analysts created a list of open codes based on repeated
topics and themes in the data using an inductive process (Ryan & Bernard, 2003). These codes were
identified by the amount of times the same codes appeared in the data (i.e. recurrence). A final step of axial
or interpretive coding involved reviewing these preliminary codes (and coded text), to arrive at a more
limited number of summative codes, which were the basis for the descriptions included in this report.

Case #3: Study of an employers’ online internship program

Finally, we conducted a brief case study of the online internship program of TreeHouse Foods in order to
provide an employers’ account of their experiences with online internships during the COVID-19 pandemic
(Yin, 2017). An employer was identified through informal requests for potential study sites via the first
author’s professional network, and the name of a Human Resources (HR) professional was provided
from a colleague. After an initial email inquiry from the first author, the employer agreed to participate in
the study, which involved a 45-minute interview. The interview was unstructured and focused on their
company’s experiences with online internships, especially how they were designed, implemented, and then
conducted during 2020. Detailed notes were taken during the interview, and these notes were used as the source material for the brief write-up of this firm’s online internship program included in this report. In
addition, the employer shared documents and a promotional video about the internship program, which
also were used to develop the account included in this report.

— This is the fourth of eight articles in this series. Click here to go to next article in this series. This series of articles is courtesy of the University of Wisconsin (Madison) Center for Research on College-Workforce Transitions (CCWT). To download the full report, go to http://ccwt.wceruw.org/research/technicalreports.html