An Explanatory Study on Multitasking

Divya Kothari kotharid@uw.edu

Gauri Chitre gauric@uw.edu

Kushagra Mall mallk@uw.edu

Maria George gmaria@uw.edu

Information School

University of Washington, Seattle

June 6, 2016

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ABSTRACT

Multitasking has been a topic of research interest for a long time. The aim of this research project

is to study the effect of Internet and Communication Technology (ICT) multitasking on the Grade

Point Average (GPA) of graduate students. Our study focuses on the impact of four ICTs namely

Facebook, Email, Texting and searching non-school related information online on the current

overall GPA. We carried out the research using a combination of quantitative and qualitative

research methods. Using web survey data from a sample of graduate students, we observed that

graduate students generally spent long time on these ICTs and using Facebook, Texting and

Searching non-school related content online while doing schoolwork has a negative correlation

with the overall GPA. However, Email multitasking showed a weak positive correlation with the

overall GPA. A subsequent qualitative study through interviews revealed that the students

consider multitasking to have an adverse effect on their academic performance. The research

process used in this study is generalizable to another sample population to study the educational

impacts of multitasking.

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Table of Contents

1. Research Proposal ..................................................................................................................................... 4

2. Research Design and Method ................................................................................................................ 7

2.1 Description of Method .................................................................................................................... 7

2.2 Operational Definition of Variables .......................................................................................... 7

2.3 Subject Selection and Sampling Procedure/Rationale for Participant

Recruitment ....................................................................................................................................................... 8

2.4 Step-wise Data Collection/Generation Procedure .............................................................. 8

2.5 Instruments ......................................................................................................................................... 9

2.6 Method for Addressing Reliability .......................................................................................... 10

2.7 Method of Analysis ........................................................................................................................ 11

2.8 Ethical Considerations ................................................................................................................. 11

3. Quantitative Data Analysis .................................................................................................................. 13

3.1 Initial Analysis and Data Wrangling ...................................................................................... 13

3.2 Results ................................................................................................................................................ 14

4. Qualitative Data Analysis ..................................................................................................................... 17

4.1 Conducting Interviews and Initial Analysis ........................................................................ 17

4.2 Results ................................................................................................................................................ 18

5. Discussion .................................................................................................................................................. 20

5.1 Research Question 1 ..................................................................................................................... 20

5.2 Research Question 2 ..................................................................................................................... 20

6. Limitations and Future Work ............................................................................................................. 22

7. Conclusion .................................................................................................................................................. 22

8. References .................................................................................................................................................. 23

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1. Research Proposal

Take a moment and think about how you are carrying out the task at hand. Apart from reading

this paper, are you also checking Facebook through another tab? Or using Yelp to find the right

place for dinner tonight? Or maybe texting your friend and discussing this new sci-fi novel that

just came out! The point being, if you are doing all these things different things at once, you are

“multitasking”. And even though it may appear that you fare well at this balancing act and are

probably saving time, it is likely that you may not be as efficient in some or all of these tasks due

to the divided attention. Our research project aims to understand this phenomenon, namely

multitasking, caused particularly due to the use of Information and Communications Technology.

Multitasking is often mistakenly considered as a human behavior that increases productivity.

However, the term multitasking in itself is a misnomer. This is because when a person claims that

he is multitasking, he is not performing all the tasks simultaneously. In fact, the reality is that the

person is performing task switching. On the flip side, there are arguments that multitasking

increases creativity (Reddy, 2014). Given the massive amount of research underway and that

which is already out there on the effect of multitasking, will consensus be reached? But does that

mean we shouldn’t be aware of this issue either? Also, is it possible to stop multitasking at all?

Thinking along the same vein, now that social media has become such an integral part of our

lives, do we have any other option but to embrace it? As University of Washington Professor

Michelle Carter (2015) recognizes this ‘internet identity’ behavior:

"We're not going to go back to where we don't have these expectations so we just have to

kind of get used to the fact that moving forward, that being human is going to involve

technology being everywhere in our environment and in us."[1]

(Carter, 2015)

Today, everyone claims that they are efficient in multitasking (Taylor, 2011). As researcher

Clifford Nass (2010) explains, “If you mention multitasking, people go insane – it is all they want

to talk about.” In this study, we intend to understand the rationale that graduate students usually

resort to when multitasking with ICT as forms of distraction.

Multitasking has been a topic of research interest for a very long time. There have been numerous

studies on how multitasking reduces the overall efficiency. Experiments have shown that

multitasking results in the reduction of accuracy, increases the time taken to complete a task and

increases stress level (Ophira, Nass & Wagner, 2009). While the explanations for decreased

efficiency have evolved over time, there has been limited number of studies probing both the

quantitative and qualitative aspects of such reduced efficiency.

In order to limit the scope of the project, we decided to study the effect of ICT on various

qualitative and quantitative variables of graduate students. Scholars from different domains have

been conducting experimental and observational studies in this field. There are also multiple

studies on the behavioral analysis of the youth in this digital age (Wihbey, 2013). The study by

Ophira, Nass and Wagner explains the effect of multitasking on the cognitive control and ability

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to process the information. It was found that heavy media multitaskers were on average 77

milliseconds slower than light media multitaskers in recognizing change in patterns. It was also

found that heavy multitaskers had poor recall/ information retrieval of specific incidents from the

past (Ophira, Nass & Wagner, 2009).

There have been multiple studies on how social media affects the grade point average of the

students. The study by Rosen, Carrier and Cheever reveals that usage of Facebook and Texting

resulted in lower GPAs than those who avoided it (Rosen, Carrier & Cheever, 2013). Similarly,

the study by Lee analyzed the relation between multitasking orientation, gender, age and income

on the grade point average (Lee, 2012). We also performed an extensive literature review to

understand the effect of multitasking on quantitative variables like time of completion of the task

and long-term memory retrieval. There are experiments showing how time complexity depends

on the familiarity of the task and the number of tasks switched (Rubinstein, Meyer & Evans,

2001). Researches have also argued on how multitasking reduces long-term memory retrieval

(Mayr & Kliegel, 2000).

As we were interested in studying the effect of ICT on the academic performance of our target

population, we decided to replicate the results of a notable study in this field by Junco & Cotten.

The study analyzes the impact of engaging in multiple ICT activities like using Facebook, IM,

talking and texting while studying on the overall GPA (Junco & Cotten, 2011). We decided to

limit the scope of our research to analyze the effect of using Facebook, searching for non-school

related content online, email and texting while studying on the GPA. In addition to this, we tried

to understand the perception of our target audience about multitasking and analyze qualitative

variables like stress involved while multitasking.

Our research paper aims at identifying the effect of different variables due to ICT multitasking

and how the amalgam of all these variables affect the efficiency of graduate students during

media multitasking. We believe that despite numerous research papers on the existing topic, there

is still no consensus on how the efficiency relates to the multitasking. The paper also aims to

study the combination of all the dependent and independent variables in order to give a clearer

picture of whether the use of technology while studying is actually helping graduate students’

cause or not. Also, since there are no definitive studies that take into account all the above-

mentioned variables, we feel that our research in this field could provide patterns to connect the

dots for this problem.

The study utilizes the mixed research analysis method in order to analyze both quantitative and

qualitative variables. There are two main components to this research study. The first component

makes use of the research study by Junco & Cotten where the effect of time spent on multiple

ICTs while studying on the GPA has been analyzed using quantitative research methods. The

final component of the study is an explanatory analysis where we elucidate the perception about

multitasking by graduate students. This has been achieved by conducting interviews. The

qualitative variables observed will be level of stress, perception, level of multitasking (high,

medium or low), ethnicity and gender.

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The research questions examined for the current study are:

1. What is the impact of the using technology while studying on the overall GPA?

2. What is the rationale behind multitasking while studying by the graduate students? Does

multitasking result in increased level of stress?

From the study by Junco & Cotten (2011), we hypothesize that the use of ICT while doing

schoolwork will result in detrimental educational outcomes. The qualitative study done in

conjunction will help gauge the perception about multitasking of the graduate students. If the

students think that they are efficient in multitasking, we can then use the quantitative study to

inspect the validity of that statement.

In this digital world of social media interactions and overdependence on technology, our research

findings can benefit many people who indulge in multitasking. In a nutshell, as our scope is

limited to graduate students, it shall benefit graduate students who have the habit of multitasking.

Also, we hope that the findings and methodologies of our research work can be used as a

yardstick by other researchers, already investigating the relationship of ICT multitasking and its

efficiency, to add to or clarify their own existing work.

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2. Research Design and Method

2.1 Description of Method

For the purpose of this research, we used a mixed research design method that combined both

qualitative and quantitative methods. Our target population was graduate students at the

University of Washington. We understand that the population is not representative of the overall

population of graduate students. However, considering the time and resources available for this

research, we chose the target population based on convenience.

The first component was a quantitative study that was a replication of the study conducted by

Junco and Cotten (2011). We carried out this step by conducting surveys. We surveyed 62

students from the population though our initial aim was to survey around 40 students (about 20 %

of the population) so as to ensure reliable reults.

The second component was a qualitative study. It involved collection of demographic details

about the research subjects. Case in point, level of stress, level of multitasking, ethnicity and

gender. The aim of the second part was to gauge the perception of research subjects about

multitasking. The subjects were asked to give their own evaluation about their level of

multitasking and whether they were efficient in multitasking. We carried out this step by

conducting interviews with at least 6-8 participants.

2.2 Operational Definition of Variables

For the purpose of this research paper, we defined multitasking as “as divided attention and non-

sequential task switching for ill-defined tasks as they are performed in learning situations.”

(Junco & Cotten, 2011). Case in point, students using Facebook while doing their schoolwork. In

this study, our intention was to study the effect of multitasking on academic performance through

a set of qualitative and quantitative variables.

2.2.1 Qualitative Variables

1. Stress: In psychology, stress is defined as “a state of mental or emotional strain or

suspense.” In this case, we measured stress as a categorical variable having three states: High,

Medium and Low. The participants were asked for the level of stress they encountered while

multitasking.

2. Degree of multitasking: An individual was classified as High-level, Medium-level or

Low-level multitasker based on the amount of time spent on ICT while doing schoolwork. This

was calculated by the frequency of multitasking reported by the participant. Thus if a participant

reported that he uses Facebook 10% of the time while studying, he is a low-level multitasker.

However, if a participant searches for information online 90% of the time while studying, he is a

high-level multitasker.

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3. Familiarity with Internet: The individual self-reports his Internet familiarity from ‘Full’

to ‘None’. For this study, “None” was coded as 1; “Little” was coded as 2; “Some” was coded as

3; “Good” was coded as 4; and “Full” was coded as 5. The coding scheme was adopted from the

original study by Junco & Cotten (2011).

2.2.2 Quantitative Variables

1. Average time spent on ICT: An individual reports the average time on searching

information online, Facebook, email and texting.

2. Average time spent for studying: An individual reports the average time spent on

studying.

3. Frequency of multitasking: The average amount of time spent on ICT while studying. All

time measurements are recorded in minutes.

4. Undergraduate GPA: An individual reports his undergraduate level GPA on a 4.0 scale

ranging from 0 for ‘F’ to 4.0 for ‘A’.

5. Current GPA: An individual reports his current overall GPA on a 4.0 scale ranging from

0 for ‘F’ to 4.0 for ‘A’.

2.3 Subject Selection and Sampling Procedure/Rationale for Participant Recruitment

The sample population for our study was confined to current graduate students enrolled at the

Information School of University of Washington, Seattle. The graduate level programs at the

Information School enroll students of varying demographics that can choose to specialize in

interdisciplinary fields. The reason for choosing this sample population was that our research

replicates the study by Junco & Cotten (2011) and takes it a step ahead to study how multitasking

affects grades of graduate students in their day-to-day academic lives.

In order to identify the sample, we used a simple random sampling technique. Each individual in

the population was treated equally and we randomly chose a fixed number of subjects for our

quantitative study using surveys. This also helped minimize potential biases that might have come

into play while choosing subjects from the researcher’s end. Based on the results from the survey,

we used purposive sampling to identify participants with varying degree of multitasking for the

interview.

2.4 Step-wise Data Collection/Generation Procedure

Since both quantitative and qualitative data were to be collected to map the issue, we employed

two forms of data collection procedures, namely Survey and Individual interviews.

We deployed online surveys via Survey Monkey since it was easy to reach out to graduate

students who may not always be available given their busy schedule. This online tool allowed

open as well as closed ended questions. Moreover, it also had interactive features to keep

respondents engaged during the process of filling the survey. As for interview, Pickard (2007)

quotes Bertrand and Hughes (2005):

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“Interviews are usually used when we are seeking qualitative, descriptive, in-depth data

that is specific to the individual and when the nature of the data is too complicated to be

asked and answered easily….it allows for some degree of interaction between the

researcher and the subject...you talk to people to discover what they think, feel and

remember about events. Interviews allow people to respond on their own terms and

within their own linguistic parameters, providing them and the interviewer with the

opportunity to clarify meanings and shared understanding.” (Bertrand and Hughes,

2005)

Pickard along with Bertrand and Hughes capture the essence of conducting the interview process

in addition to a survey. The survey was released to over 40 students, while the number of

interviews planned ranged from 6-8. We believed that as first time interviewers, it would be a

good idea to conduct a pilot interview and revise our procedure, questions and style for the

further ones, also depending on the attitude and ease of answering questions by the subjects. This

iterative process helped us identify a strong set of questions to be used for the subsequent

interviews. Our overall process essentially comprised of 8 steps: 1) Send survey; 2) Screening the

data; 3) Summarizing and analyzing the data received; 4) Amending initial questionnaire; 4)

Conduct in-person interviews; 5) Transcribing and coding; 6) Inter-rater reliability; 7) Collation

of results & 8) Interpretation of final results.

2.5 Instruments

In this section, we will be discussing about the various instruments we intend to use for this

research project.

2.5.1 Questionnaire

We intended to collect all the required data for the quantitative analysis through surveys. The

survey comprised of well-defined and structured questions. The following section describes the

various questions we asked in the questionnaire to collect the dependent and the independent

variables and the various coding schemes used to categorize the response.

2.5.1.1 Independent variables:

The variable measured to assess the usage of ICT has been adapted from the study by Junco &

Cotten (2011). As in the original study, the ICT usage was measured using two main questions.

“On average, about how much time per day do you spend on the following activities?” and “How

much time did you spend on each of these activities yesterday?” The time of usage of ICT on the

previous day was collected in order to ensure accuracy. Ideally, the average time reported for ICT

usage for the previous day should be close to the average time reported for a week. The ICT

activities included in the study are searching information online, Facebook, Email and texting. In

addition to this, the number of hours spent in a week on studying was also recorded.

Frequency of multitasking was recorded by “How often do you do schoolwork at the same time

that you are doing the following activities?” with prompts for searching for information online

that is not part of schoolwork, Facebook, email and texting on their cell phones (Junco & Cotten,

2011). The choices will be “Very Frequently (close to 100% of the time);” “Somewhat

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Frequently (75%);” “Sometimes (50%);” “Rarely (25%);” and “Never.” The responses were then

coded to a five-point scale with ‘Never’ coded as 1 and ‘Very Frequently’ coded as 5 (Junco &

Cotten, 2011). As our target population was graduate students, we also recorded the

undergraduate GPA of the participants to account for the impact of past academic performance on

the current overall GPA. Undergraduate GPA was collected on a 4.0 scale ranging from 0 for ‘F’

to 4.0 for ‘A’.

2.5.1.2 Dependent Variable

We asked participants to report their current graduate overall GPA. Overall GPAs measured on a

4.0 scale were converted, ranging from 0 for ‘F’ to 4.0 for ‘A’.

2.5.2 Interview Guides

We used this direct and reliable means of collecting data based on the results from the survey.

This was a semi-structured interview with a set of questions to guide the interviewer. The

interview questions were inspired from the study by Lee (2012) include (but not limited to):

1. How frequently do you multitask while studying?

2. How do you multitask?

3. Do you find it easy to switch from one activity to another while studying? Why or why

not?

4. Do you think ICT multitasking helps or interferes with learning?

In the beginning of the interview, the interviewer asked the participants about themselves and

how they were doing in their courses. The interviewer then proceeded with the aforementioned

questions to understand the perception about multitasking. The interview culminated with an

open-ended question where the participant could share their views about multitasking that were

not covered by the questions.

2.6 Method for Addressing Reliability

To ensure reliability of the research method, we followed a two-fold process. Proper sampling

techniques were used to remove the self-induced bias from data collection. While simple random

sampling removed the bias from the quantitative study, purposive sampling was used to select the

participants for the interview. The validity of the quantitative data was obtained using Null

Hypothesis Significant Testing (NHST). A p-value less than the 0.05 significance level will

ensure the internal validity of the results. As the sample size of the target population was low, the

results do not guarantee external validity.

To ensure reliability of the qualitative research phase of the study, careful procedures were

followed before, during and after data collection. The interview questions were prepared after

carefully understanding its implications on the results. The data used in the study was obtained

directly from the sources to maintain reliability. Thus, data collected from the surveys and the

interviews was compared to ensure consistency and proper steps were taken to keep the backup of

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data used in the research, which was obtained from the different sources. In order to avoid errors

during the data collection procedures, the response during the interview was recorded as well as

noted down clearly in capital letters. Inter-rater reliability was used to test the consistency of the

coding scheme used for the qualitative variables.

2.7 Method of Analysis

The purpose of the study made a mixed-research method imperative. In order to understand how

multitasking affect the overall GPA, we conducted a quantitative study. To strengthen the

validity of the quantitative results, we conducted a qualitative study to understand the rationale

behind multitasking. We then followed the sequential exploratory design used by Lee (2012).

Figure 1: Sequential explanatory design

We first performed an exploratory data analysis to understand the diversity of the population.

Descriptive statistics were used to analyze qualitative data like gender, ethnicity etc. The data

wrangling and statistical analysis was performed in Python. Aggregate variables were calculated

to measure the frequency of multitasking (Refer section 3.1 for more details). We then analyzed

the data by computing the Pearson’s correlation coefficient to understand the relation of the

different independent variables on the output measure, the overall GPA.

The results obtained from the quantitative analysis were then used to identify low, medium and

high level multitaskers and its correlation to the GPA. Through purposive sampling, the

participants for the interview were selected so that there is a representation from different levels

of multitasking. The qualitative text helped to explain or elaborate on the results from the

quantitative analysis. The text was analyzed to test the veracity of the perception about

multitasking of the participant with the quantitative results. In the final stage, results from the two

phases were connected to comprehend the research problem.

2.8 Ethical Considerations

For the purpose of this research, we collected different personal information from the

participants. This includes ethnicity, academic performance (as overall GPA) etc. We understand

that each individual has a right to privacy and would like to maintain the confidentiality of this

information. It is also necessary that in any research there are no negative/side effects on the

subjects. We therefore took the following actions to address the various ethical considerations.

Quantitative Data

Collection

Quantitative Data

Analysis

Qualitative Data

Collection

Qualitative Data

Analysis

Interpretation of entire analysis

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1. All the details about participants gathered through surveys and/or interviews were

gathered anonymously. We did not collect or preserve or refer any personally identifiable

information about the participants.

2. Each participant was clearly explained about the research goals and how their

information will be used. The subjects’ participation was completely voluntary and the

participants were allowed to opt-out of the research at any time.

3. The study did not cause any harm to the subjects. However, even in case of any

unexpected negative outcomes, our first priority would have been to minimize any harm caused

to the subjects.

4. We treated all the participants equally irrespective of their race, ethnicity, religion,

gender, academic performance or any other distinguishing factors. Thus, responses of each

participant had an equal importance in the analysis. We also conducted the surveys and

interviews for all the subjects in a uniform and conducive environment.

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3. Quantitative Data Analysis

3.1 Initial Analysis and Data Wrangling

A survey containing 10 questions was sent to a random sample of participants who gave their

informed consent to participate in the research study. We obtained 62 responses through the

survey. A total number of 22 variables were recorded. Descriptive statistics were run to

understand the demographic characteristics of the participants and to analyze the various

multitasking behavioral indicators including time spent on ICT while studying.

All the times reported were converted to minutes to maintain uniformity across all the variables.

Participants were asked to report the time spent preparing for class, assignments and quizzes.

Combining all the aforementioned three variables, a single variable was created to indicate the

total hours spent on studying. Aggregate variables were computed in order to calculate the time

spent on ICT while studying by multiplying the frequency of multitasking with the overall time

spent on that particular ICT. Thus, if a participant reported that that he uses Facebook 50% of the

time while he was studying and spent a total time of 500 minutes on Facebook, then the value of

the aggregate variable is 250 minutes.

After all the data transformations were performed, necessary steps were taken to handle the

outliers. In order to satisfy the normality assumption of regression, observations with current

GPA less than 3.5 were removed from the analysis. A total of 11 observations were removed

because of Facebook multitasking (spending more than 182 minutes per day), email multitasking

(spending more than 328 minutes per day), texting multitasking (spending more than 115 minutes

per day) and search multitasking (spending more than 204 minutes per day). This resulted in the

final sample size of 51 observations. The above threshold was calculated from the 68-95-99.7 rule

that states that 95% of the observations lie within two standard deviations. For this study, we have

included only the observations within two standard deviations.

The collinearity diagnostics showed that the independent variables were not highly correlated.

However, even after the removal of the outliers, the condition for normality failed for the

independent variables. The violation of this assumption prevented us from fitting a multivariate

regression model to analyze the relationship between current GPA and time spent of ICT.

After the initial data cleaning process, correlation tests were run to evaluate the relationship

between reported average time spent on ICTs and time spent “yesterday”. This was performed to

test the accuracy of the time reported by the participants. Correlation test were also performed to

understand the relation between undergraduate GPA and current GPA. Finally, correlation tests

were run to analyze the relation between time spent on various ICTs and the current GPA.

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3.2 Results

3.2.1 Descriptive Statistics

Sixty-six percent of the people who took the survey were female. In terms of race and ethnicity,

55% were whites, 37% were Asians/Pacific Islander, 7% were Hispanics or Latinos and 1% were

identified as others. Figure 2 shows the distribution of the participants by gender and ethnicity.

The gender, race and ethnicity was similar to the overall target population, however, there was a

slight overrepresentation of females. The average undergraduate GPA of the sample was 3.46

with a standard deviation of 0.58. The average current overall GPA of the sample was 3.72 with a

standard deviation of 0.51.

Figure 2: Histogram showing the distribution of participants based on gender and ethnicity.

(Visualization created in Tableau)

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3.2.2 Correlations

The usage of Facebook, email and searching for information other than academic strongly

correlated with their associated ‘yesterday’ measures with the range of the Pearson’s correlation

coefficient between 0.83 and 0.91. However, the reported usage of texting with its corresponding

‘yesterday’ usage had a low correlation of 0.13. Contrary to the time spent on texting, the number

of text messages sent had a strong correlation with its corresponding ‘yesterday’ measure with a

Pearson’s correlation coefficient of 0.975. Undergraduate GPA was included in the analysis as a

control variable to account for how the past academic performance affects the current overall

GPA. However, the correlation test run between undergraduate GPA and current GPA showed a

weak relation with the Pearson’s correlation coefficient of 0.02.

3.2.3 Frequency of Multitasking

Table 1 shows the summary statistics of the time spent (overall on a day) on the various ICTs and

the time spent on the various ICTs while studying (multitasking). The participants spent most of

the time on Facebook, Email and searching for non-school related information online. The

participants sent 92 messages on average per day. While analyzing the frequency of multitasking

(Table 2), it was observed Email and Texting multitasking were most common with 60% reported

using email and 40% reported texting while studying somewhat frequently to very frequently.

Facebook multitasking and searching for non-school related information while studying were not

popular with 23% participants reporting that they never use Facebook while doing school work.

Table 1:

Mean and standard deviation of overall time spent using ICT and the amount of time spent using

each ICT while doing schoolwork (N = 62)

Activity Mean min/day (SD)

Overall Multitasking

Facebook 68 (88) 43 (70)

Email 121 (229) 79 (125)

Texting 49 (47) 33 (42)

Search 92 (83) 57 (73)

Number of text messages 39 (129) -

*Summary statistics of the various activities are reported in minutes. The grain of the analysis is

per day.

Table 2:

Frequency with which students reported doing schoolwork while using each ICT (N = 62).

Activity Multitasking frequency

Never Rarely (25%) Sometimes

(50 %)

Somewhat

Frequently

(75%)

Very

Frequently

(100 %)

Facebook 14 17 7 19 5

Email 3 6 16 22 15

Texting 9 10 18 14 11

Search 5 10 24 16 7

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3.2.4 Correlation between Current Overall GPA and Multitasking

Figure 3: Scatter plot showing the relationship between various ICT multitasking and current

GPA. Red line represents the regression line.

Pearson’s correlation test was run to analyze the relation between ICT multitasking and current

overall GPA (see Figure 3 for results). Facebook multitasking, Texting multitasking and Search

multitasking showed a weak negative correlation with the current GPA with a Pearson’s

correlation coefficient of -0.1, -0.25 and -0.03 respectively. Email multitasking showed a weak

positive correlation with the current GPA with a Pearson’s correlation coefficient of 0.073.

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4. Qualitative Data Analysis

4.1 Conducting Interviews and Initial Analysis

After several rounds of iteration we finalized a set of questions around the important variables.

After the first round of interview that was a pilot interview, we made several changes and then

proceeded with the remaining interviews. We carried out this process in teams of two, with the

interviewer asking questions and audio-recording the interview and the other team member

writing down the responses. Given the division of labor, the answers typed while the person was

speaking, were fairly accurate hence we saved time transcribing them all over again by referring

back to the recorded versions.

This was followed by developing an affinity diagram (see Figure 4 for results) based on our

results for questions to determine the primary research question that revolved around the variable

‘stress’ (emphasis supplied). After this, we utilized methods such as open coding, axial coding

and selective coding and came up with a coding manual. The same was tested within our group

via an inter-rater reliability test (see Figure 5 for results) that showed a high kappa value. In order

to confirm the reliability, we carried out another inter-rater reliability test (see Figure 6 for

results) that also resulted in a high kappa value. Hence we arrived at the conclusion that the

coding manual was fairly straightforward and easy to use, hence, reliable.

Figure 4: Affinity Diagram

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Figure 5: Kappa calculator for assessing inter-rater reliability (within team)

Figure 6: Kappa calculator for assessing inter-rater reliability

4.2 Results

We chose 9 participants by way of purposive sampling, the first one being a pilot interview based

on which we went through a round of iterations and made improvements to the questionnaire. Just

by eyeballing, we did a quick initial analysis by way of which we realized:

The median age of our sample participants was 25.5 years, the youngest being 22 and the

eldest being 27 years of age.

All are participants identified themselves as males.

The sample group was primarily Asian.

Their mean GPA was 3.76, the lowest being 3.6 and the highest as 3.93.

The average number of hours spent multitasking in a day was 2.47 hours.

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Majority of the participants considered themselves to be Good or fully versed with the

ways of the internet.

Interestingly, most people identified as Medium-level multitaskers, despite having a

varied time difference when it came to number of hours spent on multitasking in a day.

For instance, D3 who spends 0.75 hours in a day multitasking and K5 who spends 4.5

hours in a day, multitasking, both consider themselves as ‘Medium level’ multitaskers.

Majority of the people found that use of ICT while multitasking was helpful to them.

Figure 7: Summary of Initial Analysis on the interviewees

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5. Discussion

5.1 Research Question 1

What is the impact of using ICTs while studying on the overall GPA?

Results show that participants spent a large amount of time on ICTs. Participants spent on

average 2 hours on Email and 1.5 hours on searching for non-school related information online.

Results also show that participants used ICT frequently while doing their schoolwork.

Participants spent time on texting and email very frequently at the same time they were doing

their schoolwork. The results related to frequency of multitasking were in general congruent to

the results from the original study by Junco & Cotten, 2011. Junco & Cotten found Facebook,

email and search multitasking as the most frequent forms of multitasking. The difference in the

results can be attributed to the inherent difference between the sample population in this study

and the original study by Junco & Cotten. While our study focused on the impact on texting

multitasking, research by Junco & Cotton (2011) focused on IM multitasking.

The negative correlation between Facebook, texting and search multitasking with current GPA is

congruent to the initial hypothesis that multitasking will have a negative impact on the overall

GPA. However, the weak negative correlation can be attributed to the fact that there was high

variance in the time reported for ICT multitasking (captured by the aggregate variable) while

studying. The standard deviation of the time reported for Facebook, texting and search

multitasking are 70 minutes, 42 minutes and 73 minutes respectively. Email multitasking showed

a very weak positive correlation with the current GPA. This may be because the participants may

be sending work-related emails during their study time. This high variance in the time reported

for the different ICT multitasking can also be the reason for failing the normality condition

necessary for fitting a multivariate regression model.

In conclusion, while the study does not show a strong conclusive result on the impact of

multitasking on the current GPA through statistical methods, it was able to identify the general

trend that high level of multitasking can negatively impact the current GPA as proposed by the

Junco & Cotten (2011). All the variables in this study were self-reported, thus there is no method

to test the veracity of the reported values. However, in the original study by Junco & Cotten

(2011), the current GPA was directly collected from the university registrar with the consent of

the students. Also, the original study was conducted on undergraduate students while the sample

population for this study was graduate students. The variation in results could also indicate that

the sample population of this study is adept in multitasking.

5.2 Research Question 2

What is the rationale behind multitasking while studying by the graduate students? Does

multitasking result in increased level of stress?

This was an extremely interesting research question to delve into. The varied responses by our

participants represented a fascinating interplay of external factors forcing the participants to

21

multitask and their own willingness to do so. Elements of psychology were also woven into the

latter, as some people answered saying that multitasking was akin to second nature for them now.

One of the consistencies that we noticed was that on a general note, people did consider

multitasking to be a distraction while studying. Their personal opinion of multitask varied from

liking it to disliking to leaving it open ended by saying that it depended on external factors such

as urgency of a new task or multiple upcoming deadlines. Majority of the participants admitted to

experiencing higher stress levels when multitasking while studying. However this was not a

unanimous opinion since some people replied saying that it depends on the severity and size of

your work/assignment/homework. There was also not much unanimity in terms of ease of task

switching when multitasking, with most participants responding with a ‘depends’ on the tasks

involved. In terms of highest use of ICT while studying, we recorded the maximum score for

emails, texting/IM and searching for information online. We had two outliers during our

qualitative study. One who said that he did not multitask but agreed that they experience higher

stress levels if he multitasked while studying. However since he denied to multitasking at all, we

decided to exclude his responses. The second outlier was an individual who found studying in

general a stressful activity and resorted to multitask so as to reduce the level of stress.

After studying all the responses, we have arrived at the conclusion, that since the focus of our

explanatory study targets graduate students, given their school life, assignments and team-related

activities, it is a logical inference that they would have to rely on various modes of ICTs (Email,

IM, Research being the important ones. Facebook was reportedly not used as much as other ICTs

which can be attributed to their busy schedules). This is substantiated by the answers recorded

wherein students agreed to be multitasking while studying using email, texting and searching for

information online inter alia. Moreover, for a graduate student these are tasks that cannot be

avoided, which is also validated by the element of external factors that forces them to multitask.

However, it may be noted that there seems to be an agreement of sorts amongst some students

that multitasking increases efficiency and they like doing it despite experiencing stress, as long as

it ‘gets work done’.

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6. Limitations and Future Work

For the quantitative study, we were unable to come up with a statistically significant relation

between the degree of multitasking and current GPA. One of the main reasons for this was the

high variance in the reported time spent on various ICTs while multitasking. This may be because

people resort to guessing the number of hours instead of reporting an accurate amount of time

spent. Another limitation is that given the sensitive nature of data required from participants,

namely their GPA, it is a reasonable assumption that not all participants would be comfortable

answering this question. In order to account for effect of control variables such as demographics

(age, ethnicity, gender etc.) on the response variable (overall GPA) requires a higher number of

survey responses. This was a third limitation as the number of responses we received was very

low (62). For the qualitative study, we lacked a good number of participants to get a consistent

and conclusive result. Furthermore, we realize that our questions very highly open ended which

left room for different kinds of interpretation by the participants.

One way to overcome the above mentioned limitations are to conduct an observational study to

capture the exact time spent and a more thorough and well-structured interview to overcome

shortcomings of the qualitative process we followed. As for collection of sensitive information

such as GPA, it may be a good idea to obtain it from an official source directly with the informed

consent of the participants. Lastly, it is necessary to increase the sample size so as to deduce a

fairly accurate result.

7. Conclusion

Results show that there is weak negative correlation between Facebook, texting and search

multitasking and current GPA. This result was congruent with the original study by Junco &

Cotten (2011). Both the qualitative and quantitative study showed that frequency of multitasking

was high among the participants. Majority of the participants in the interview also found that

multitasking has an adverse effect on their academic performance. The weak positive relation of

email multitasking with current GPA can be attributed to the fact that people may use email for

academic purpose while all the other ICTs (Facebook, texting and searching) for social purposes.

(Junco & Cotten, 2011). Future research should attempt to increase the sample size, conduct

observational studies, and investigate further into the discrepancies between the effects of various

ICT multitasking with the overall GPA.

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