Selectively Social Politics The DifferingRoles of Media Use.docx

Selectively Social Politics: The Differing
Roles of Media Use on Political
Discussion
J. D. Ponder and Paul Haridakis
Department of Studies Studies
Kent State University
In the modern media environment, people are afforded a variety
of options for
political information. In addition, people now use multiple
media sources (e.g.,
television, radio, blogs) to obtain information about all aspects
of politics
(Eveland, 2004; Pew Research Center for the People and the
Press, 2009).
The purpose of this study was to examine how use of particular
media sources
influenced the frequency of political discussion with people
from the same
political party (political in-group members) and people from a
different polit-
ical party (political out-group members). Guided by a uses and
gratifications
perspective, which emphasizes the role of the user in media
effects, we exam-
ined how specific user background characteristics (e.g., age,
sex, political opi-
nion leadership, political social identity, political content
affinity), motives for

using traditional and social media for political information, and
use of
different media sources work together to influence discussion
with political
in-group and out-group members. Our results allowed us to
identify several
distinct differences between people who talk to political in-
group and out-
group members.
J.D. Ponder (Ph.D., Kent State University, 2012) is an Assistant
Professor in the
Department of Studies Studies at Kent State University. His
research interests include media
uses and effects, identity, learning, and political
communication.
Paul Haridakis (Ph.D., Kent State University, 2000) is a
Professor in the Department of
Studies Studies at Kent State University. His research interests
include media uses and effects,
new communication technologies, freedom of expression and
media history.
Correspondence should be addressed to J. D. Ponder,
Department of Studies Studies, Kent
State University, 135 Taylor Hall, 300 Midway Drive, P.O. Box
5190, Kent, OH 44240. E-mail:
[email protected]
Mass Communication and Society, 18:281–302, 2015

Copyright # Mass Communication & Society Division
of the Association for Education in Journalism and Mass
Communication
ISSN: 1520-5436 print=1532-7825 online
DOI: 10.1080/15205436.2014.940977
281
mailto:[email protected]
http://dx.doi.org/10.1080/15205436.2014.940977
INTRODUCTION
In the contemporary media environment, people have a variety
of different
media sources from which to acquire political information.
People interested
in learning about politics or gaining political information can
watch tele-
vision; listen to the radio; read newspapers, magazines, or
books; in addition
to using online sources (e.g., blogs, social networking sites,
video sharing
sites). In most cases, people now use multiple media sources
(e.g., television,
radio, blogs) to obtain information about politics, political
actors, and polit-
ical issues (Eveland, 2004; Pew Research Center for the People
and the
Press, 2009).
Perhaps even more important, people also talk with others about
politics.

They seek out people who can help them make sense of
mediated political
information (Eveland & Shah, 2003; Mutz, 2002). Although the
attitudinal
and behavioral effects of political discussion have been well
documented,
especially how it can positively affect political knowledge or
voting (e.g.,
Delli-Carpini & Keeter, 1996; Kwak, Williams, Wang, & Lee,
2005), the
latter in particular, is related to social capital. There have been
claims that
some media use (e.g., television) detracts from civic debate and
participation
(Putnam, 2000). On the other hand, newer social media have
played an
instrumental role in the creation of political movements (i.e.,
Occupy Wall
Street, Tea Party movement), and even revolutionary
movements (i.e., Arab
Spring). Although such outcomes of media use and discussion
have received
much attention, predictors of discussion are relatively
unstudied. In
addition, a few scholars have found that people use media
differently based
upon the political affiliation of their discussion partners (e.g.,
Eveland &
Shah, 2003; Mutz, 2002). However, without considering how
media users’
individual differences work in conjunction with their media
selection to
influence postviewing discussion, we can only speculate about
how people
use media content from different media sources to talk about
politics with

their political allies versus those who hold opposing political
views, or
how viewers of certain types of programs may be more likely to
debate poli-
tics with political friends and=or foes than are viewers of other
programs.
The purpose of this study is to examine how media use
influences the
frequency of political discussion with people from the same
political party
and people from a different party. This research is guided by a
uses and
gratifications perspective. Uses and gratifications researchers
posit media
effects, such as post-media-use discussion, as a product of user
personal
characteristics, motives for using media, and media use. More
important,
this is a process that builds on itself in that personal
characteristics influence
motives for using media, which influence media use, which in
turn influence
the effects of that use.
282 PONDER AND HARIDAKIS
THE ROLE OF DISCUSSION IN POLITICS
Denton and Woodward (1998) explained that political
communication
research focuses on the production, dissemination, processing,
and effects
of information, both through the media and interpersonally. In

this light,
political discussion is a key component in both disseminating
information
and helping people make sense of the information presented to
them via
mass media (Delli-Carpini & Keeter, 1996; Eveland & Hively,
2009). People
not only turn to others to help them make sense of the
information but also
differentiate between people in their own party (political in-
group members)
and people from a different party (political out-group members)
as conver-
sation partners (Eveland & Shah, 2003; Eveland & Hively,
2009). Further,
they seek out politically similar others more frequently than
politically
dissimilar others when trying to process political information
presented
to them via mass media (Borgida, Federico, & Sullivan, 2009;
Eveland &
Hively, 2009).
USES AND GRATIFICATIONS
Uses and gratifications is an audience-centered, media effects
perspective
that focuses on what people do with the media as opposed to
what the media
do to people. Uses and gratifications scholars suggest that
individual social
and psychological differences; motives for media use; use of
media; and levels
of activity at various points before, during, and after exposure
to media all
work together to influence the effects of media exposure

(Rubin, 2009).
Uses and gratifications has been applied to examine the use of
media
for political information (Eveland, 2004; Hanson, Haridakis,
Wagstaff,
Sharma, & Ponder, 2011), motives for using media for political
information
(Kaye & Johnson, 2004; McLeod & Becker, 1974), and the
effects of polit-
ical media use, such as political discussion (Eveland, 2004). In
the uses and
gratifications framework, discussion has been identified as a
common post-
activity manifestation of audience activity (Levy & Windahl,
1984; Rubin,
2009). In the context of politics, political discussion allows
people to reflect
on and integrate that information into their lives (Eveland,
2004; Rubin &
Perse, 1987).
As just referenced, uses and gratifications is based on the
notion that
media users’ social and psychological circumstances influence
their media
use. Certain personal characteristics have been particularly
salient in the
study of politics and political discussion. In the following
sections, we discuss
the role that these particular characteristics play in influencing
use of media
for political information and=or political discussion.
IN-GROUP AND OUT-GROUP 283

Demographics
Multiple scholars have examined the role that age and gender
play in
explaining differences in using media for political information,
media use,
and political discussion. Historically, age and gender have been
associated
with different motives people have for using media, the media
sources
people use, and the amount of political discussion in which
people engage
(Mendez & Osborn, 2010; Mutz, 2002). Specifically, some
research has sug-
gested that younger voters tend to be less engaged and less
willing to discuss
politics with others than are older voters (Hardy & Scheufele,
2009; Shah,
Kwak, & Holbert, 2001). In addition, there is a substantial body
of literature
outlining the differences between men and women in relation to
political
participation (Mendez & Obsorn, 2010). For example,
Lazarsfeld, Berelson,
and Gaudet (1948) found that women in 1940 tended to vote
largely in line
with their husbands. Historically, although women have been
activists and
leaders of social movements like prohibition and suffrage, they
have largely
been reticent to engage in conversations about the state of the
political
environment (e.g., Mendez & Osborn, 2010). Therefore, it is
important to

include these characteristics in any model that predicts political
discussion.
Political Opinion Leadership
In terms of politics, scholars have suggested that opinion
leaders are less
likely to attend to messages from politically different people
and institutions
and less likely to speak to out-group members than members of
the public
(e.g., Guerin et al., 2004). Conversely, scholars have also found
that opinion
leaders are more likely to talk to members of their own political
party than
the general public (e.g., Eveland & Shah, 2003; Shah &
Scheufele, 2006). In
addition, research on political opinion leadership has
consistently suggested
that opinion leaders use more media content in their specific
interest area,
are more active during this use, and disseminate it to others in
their political
in-group (Shah & Scheufele, 2006). Therefore, it is important to
understand
how a person’s degree of opinion leadership could influence
motives for
using media for political information, which media sources he
or she uses,
and how this use can influence political discussion with
political in-group
and out-group members.
Political Social Identity
Previous uses and gratifications researchers have examined how

a person’s
membership in a group may influence media use. However, the
studies have
not considered the psychological or emotional connection that
members feel
with that particular group. This connection, called a person’s
social identity,
has been linked to motives for media exposure (Huddy, 2001),
use of specific
media sources (Stroud, 2008), and even the choice of discussion
partners
(Borgida et al., 2009; Stroud, 2008). For instance, Stroud
(2008) found that
people who identify with a party selectively use cable
television, newspapers,
and the Internet as sources for political information. Further,
scholars have
suggested that a person’s identification with a particular social
group is
highly influential in determining which discussion partners
people choose
after media exposure (Borgida et al., 2009).
Political Content Affinity
Affinity with particular content refers to the level of importance
placed
upon the content of media messages by the user (Rubin, 2009).
In the uses
and gratifications perspective, people’s affinity with particular
content has

been conceptualized as an indicator of audience activity,
suggesting
that audience members are more active in their media use and
what they
subsequently do with content consumed when they enjoy a
specific type
of programming (Holbrook & Hill, 2005). Uses and gratification
scholars
have linked media content affinity to more purposive motives
for using
media and engaging in postexposure discussion (Holbrook &
Hill, 2005;
Levy & Windahl, 1984).
Motives for Using Media for Political Information
Uses and gratifications researchers emphasize that people’s
social and
psychological characteristics are sources for the needs and
desires people seek
to satisfy through using media and other communication
channels. These
needs and desires are manifested in motives that people have for
using media.
Researchers have explored a variety of possible motives that
people have
for seeking political fare (McLeod & Becker, 1974) and for
using specific
media such as the Internet for political fare (Kaye & Johnson,
2004). These
include seeking information, entertainment, relaxation, social
utility, and
vote guidance, among others. In addition, when it comes to
using the Internet
or specific Internet functions such as YouTube, some users are

motivated to
use these newer media sources, in part, because of the
convenience of online
use (Hanson et al., 2011; Papacharissi & Rubin, 2000). Other
scholars have
suggested that users may be motivated to use media fare to
increase their
positive feelings attached to group memberships (Harwood,
1999; Huddy,
2001). According to uses and gratifications, the motives that
people have
for using particular media content also influence their decisions
to select
and use particular media.
Media Use
In today’s media landscape, people are afforded a variety of
media options
for political information. In such a diverse media environment,
people are
given the opportunity to choose the medium (or variety of
media) that best
satisfies their needs. Political uses and gratifications
researchers have begun
to focus on how people use specific media sources to satisfy
their needs
related to politics. Understanding what affects people’s media
use for polit-
ical information has been a common focus of political uses and
gratifications
research. For instance, Bennett, Flickinger, and Rhine (2000)

found that
Americans who obtained political information from magazines,
the news-
paper, and television were more likely to engage in political
discussion than
those who used more traditional media sources. Conversely,
Moy and Gastil
(2006) suggested that the use of newspaper, television
network=cable news,
political talk radio, the Internet, and late-night comedy
programs tended
to promote political discussion among their users. In light of
these varying
conclusions, it was our aim to seek to examine how people
selectively seek
out and use a variety of different media to acquire political
information,
and ultimately how that media use can influence discussions
about politics
with others. In addition, although previous scholars have sought
to uncover
the use of various media in the political conversation process,
these investiga-
tions have been limited in the breadth of media sources
measured. Typically,
investigations in this vein use a limited number of media
analyzed. Thus in
the fragmented but rich media environment currently available,
scholars
need to examine the role of newer media in concert with more
traditional
forms in facilitating discussion among citizens. Indeed, scholars
have criti-
cized media studies for this very reason, calling for further
investigations into
how all media work together to influence political behaviors

(e.g., Pfau,
Houston, & Semmler, 2005). In this investigation, we expanded
this
approach to multiple media types (18 in all) to examine the
media system
more thoroughly.
Elaboration
Elaboration plays an important role in facilitating political
discussion.
According to Kwak et al. (2005), elaboration is an important
contributor
to political engagement, including discussion. Existing research
has demon-
strated a strong relationship between elaboration on political
information
and the ability to recall and use that information in later
discussions
(Eveland, 2004; Scheufele, 2002). In addition, Eveland and
Dunwoody
(2000) explained that cognitive elaboration on mediated
political infor-
mation occurs when people connect the media information to
information
in existing schemas. Eveland (2004) explained that this process
occurs when
people anticipate engaging in discussions after their use of
media for political
information. Therefore, it is evident that cognitive elaboration
should play a

key role in predicting postexposure political discussion with
others.
Political Discussion
Scholars consistently have found that people turn to the media
to provide
them with topics and information to use in later discussions and
how that dis-
cussion leads to lasting effects on audience members (e.g.,
Bennett et al.,
2000; Kim, Wyatt, & Katz, 1999). For instance, Kim et al.
(1999) suggested
that political discussion serves as a way for citizens to bridge
their personal
experiences to the larger political world. Political discussion
was also one of
the major foci in early media effects research that positioned
the user as an
active participant (e.g., Lazarsfeld et al., 1948). More recently,
researchers
have found that people discuss important news and political
issues after
exposure to media content (Eveland, 2004; Kim et al., 1999).
These scholars
have argued that discussion (or even the anticipation of it) is an
important
part in how people interact with and make sense of political
information
available to them through the media.
In terms of in-group and out-group behaviors, researchers have
found
that once people identify with a political group, they seek out
politically
similar others to make sense of mediated political information

(Borgida
et al., 2009; Hogg & Reid, 2006). At the same time, people do
talk to politi-
cally different others about political information, albeit at a less
frequent
rate. In addition, out-group discussion has not been studied as
in-depth as
discussion with in-group members (Eveland & Hively, 2009;
Mutz, 2002).
RESEARCH QUESTIONS AND HYPOTHESES
In sum, according to the basic suppositions of uses and
gratifications, media
effects are a result of much more than sheer exposure. Effects
of media use
are the result of a variety of different factors working together.
In the context
of the study, uses and gratifications suggests that (a) individual
personal
characteristics of media users (i.e., political opinion leadership,
political
social identity, political content affinity) (b) influence motives
for using
media for political information, which in turn (c) influence
selection of media
for political information, (d) elaboration on political media
content, and ulti-
mately the outcome of interest, (e) political discussion. The
main research
questions of this particular study asked about the relationship
between the
personal characteristics, motives for using media for political
information,

media use, and elaboration, in predicting political discussion
with two
distinct types of political discussants—in-group members and
out-group
members. We examined these relationships by posing two
research questions.
RQ1: How do user personal characteristics, motives, types, and
amount of
media use for political information and elaboration on political
content
obtained predict discussion with political in-group members?
RQ2: How do user personal characteristics, motives, types, and
amount of
media use for political information and elaboration on political
content
obtained predict discussion with political out-group members?
METHOD
Sample
Participants composed a nationwide sample solicited through an
online
survey provider—Qualtrics. Qualtrics’ partners recruited a pool
of 1,516
participants (approximating the demographic makeup of U.S.
voters)
through a variety of online survey sample websites that enable
users to sign
up to take surveys. Participants were offered an incentive of
$2.25 for filling

in the survey, which took approximately 15 minutes to
complete. Three
hundred fifty-one people initiated the survey, for a response
rate of 23.1%
based on the American Association of Public Opinion
Research’s RR3 cal-
culation. This response rate was within the acceptable range for
panel-based
surveys (e.g., Curtin, Presser, & Singer, 2000; Keeter, Kennedy,
Dimock,
Best, & Craighill, 2006). In addition, of those who did start the
survey,
93% (327) completed it. However, as the sample was based on
those who
initially self-selected for participation in the panel, rather than
a true random
probability sample, no estimates of sampling error can be
calculated.
Of the 327 respondents, those who incorrectly answered the
quality
control questions (e.g., those who did not click Strongly
Disagree when
prompted) were removed from the sample pool. The final
sample
(N¼ 279) consisted of 142 male and 137 female participants
(male coded
0, female coded 1). The minimum reported age was 18 years old
and the
maximum reported age was 70 years old (M¼ 47.42, SD¼
12.80). This
gender and age breakdown of the sample approximated the
average age
and gender breakdown of the average voter in the United States
(U.S.
Census Bureau, 2013). When asked to report their education

levels, 1.4%
reported they had completed some high school, 22.9% reported
they were
high school graduates, 31.1% said they had completed some
college,
26.4% said they were college graduates, 4.6% reported they had
completed
some graduate school, and 13.6% reported having graduate
degrees. When
asked about their political party affiliation, 115 reported they
were
Democrats, 84 reported they were Republican, 75 reported they
were
Independent, and five reported ‘‘Other’’ (1 Green Party, 1
Liberal, 1 non-
party affiliate, 1 politically neutral, 1 undecided).
Measures
Political Social Identity
We used Greene’s (1999) partisan social identity scale to
measure political
social identity. This scale has been applied in numerous studies
to measure
a person’s level of identification with a political party (Blais,
Gidengil,
Nadeau, & Nevitte, 2001). A typical item for this measure
reads, ‘‘I have
a number of qualities typical of members of this group’’
(Greene, 1999,

p. 396). This scale consisted of 10 items. Each item asked the
respondent
to rate his or her agreement with each item on a 5-point scale
ranging from
1 (strongly disagree) to 5 (strongly agree). The responses were
summed
and averaged to create an index of a person’s level of
identification with a
political party (M¼ 2.98, SD¼ 0.60, a¼ .81).
Political Opinion Leadership
We used the opinion leadership measure developed by Flynn,
Goldsmith,
and Eastman (1996) to identify a person’s level of political
opinion
leadership. A typical item reads, ‘‘I often influence people’s
opinions about
[PRODUCT CATEGORY]’’ (Flynn et al., 1996, p. 146). For the
current
study, PRODUCT CATEGORY was replaced with politics. The
six-item
measure focused on participants’ perceived influence over other
people’s
opinions with regard to specific topics of interest (e.g., music,
cars—and in
this study, politics). Participants responded to each of the six
items of mea-
sure using a 7-point Likert-type scale ranging from 1 (strongly
disagree) to 7
(strongly agree). Participants’ responses were summed and
averaged to create
a single opinion leadership score for each participant (M¼ 3.74,
SD¼ 1.08,
a¼ .83).

Political Content Affinity
We used an adapted version of the political humor affinity scale
developed by
Hmielowski, Holbert, and Lee (2011). Items related to political
humor or
satire were adapted to measure the broader category of political
information.
A typical item reads, ‘‘I appreciate political information
because it can make
me feel more knowledgeable about politics’’ (Hmielowski et al.,
2011, p. 114).
This measure consisted of 11 items scaled on a 5-point scale
ranging from 1
(strongly disagree) to 5 (strongly agree); higher scores indicated
a greater
affinity for political content. Participants’ scores for all 11
items in the
measure were summed and averaged to create a single affinity
score for each
participant (M¼ 3.46, SD¼ 0.63, a¼ .90).
Motives
We measured motives for using media for political information
with a
51-item scale drawn from previous research, which identified
various motives
for interpersonal communication, media use for political
information, social
identity, sports, and online media use (Flanigan & Metzger,

2001; Kaye &
Johnson, 2004; Rubin, 1983; Trail & James, 2001; Wann, 1995).
Some
motive items were adapted to reflect reasons for using media for
political
information (e.g., to feel like I have won when my team wins
was changed
to to feel like I have won when my party wins to reflect reasons
for using media
for political information).
Respondents were asked to indicate how much each of the 51
motive
statements reflected their reasons for using media for political
information,
ranging from 1 (not at all) to 5 (exactly). Principal components
factor analy-
sis with varimax rotation was used to analyze respondents’
statements.
Three different motives for using media for political
information were ident-
ified. These factors explained 66.51% of the total variance after
rotation.
The resulting 34-item index resulted in three motive factors (see
Table 1
for items and loadings). The three motive factors were (a)
idiomatic-use,
(b) political utility, and (c) political in-group achievement.
Factor 1, idiomatic-use, contained 15 items (M¼ 1.99, SD¼
0.88, a¼ .96),
reflecting how a person uses mediated political information for
personal
utility. Factor 2, political utility, contained 14 items (M¼ 3.31,
SD¼ 0.86,
a¼ .95), reflecting the use of media to gain information for

making political
decisions. Factor 3, political in-group achievement, contained
four items
(M¼ 2.53, SD¼ 0.98, a¼ .89). This motive reflected how people
use media
to increase positive feelings associated with their membership
in their polit-
ical party.
Media use. Respondents were asked to indicate how often (1¼
never,
5¼ very often) they used each of 14 media sources for acquiring
political
information about politics. These sources were adapted from the
Political
Media Diet scale, with four additional sources (wikis, political
fact checking
sites, home pages, political action websites) added to represent
the current
state of the information environment available to voters (e.g.,
Kaid,
Fernandes, & Painter, 2011; Rill & McKinney, 2011). Means
and standard
deviations are reported in Table 2.
TABLE 1
Factor Loadings for Motives for Using Media for Political
Information Items
Motive items Idiomatic–use

Political
utility
Political
in-group
achievement
Idiomatic-use
So I can get away from what I’m doing .87 .10 .05
So I can get away from the rest of the family or others .84 �.01
.15
When there’s no one else to talk to or be with .83 .06 .17
To feel less lonely .83 .04 .15
So I won’t be alone .82 .07 .12
So I can forget about school, work, or other things .82 .03 .01
Because it helps me relax .80 .11 .25
Because it’s a pleasant rest .80 .17 .18
Because it passes time when bored .79 .05 �.02
Because it allows me to unwind .79 .15 .24
Because it makes me feel less lonely .79 �.03 .20
When I have nothing better to do .77 .02 �.06
Because it’s a habit, just something I do .75 .20 .16
Because it peps me up .73 .20 .34

To learn more about myself .72 .25 .20
Political utility
To see how the candidates stand on various issues .00 .90 .03
To help me make up my mind about the important issues �.04
.85 .09
To see what a candidate would do if elected .03 .85 .06
To find out about issues affecting people like myself �.02 .81
.11
To keep up with the main issues of the day .03 .80 .01
To make up my mind how to vote in an election .01 .78 .06
To access political information from home .07 .77 .23
To judge the personal qualities of candidates .11 .77 .08
To access political information any time .10 .75 .21
To access political information quickly .14 .74 .16
To learn more about others .22 .73 .10
To judge who is likely to win an election .26 .63 .25
To remind me of my candidates strong points .12 .63 .31
To get unbiased viewpoints .30 .61 .14
Political in-group achievement
To feel like I have won when my party wins .37 .31 .74

To feel a personal sense of achievement when my
party does well
.43 .37 .67
To feel proud when my party does well .24 .47 .66
To get to know others in my party .45 .35 .63
Eigenvalue 13.64 7.06 1.25
Variance explained 41.32 21.40 3.80
Cronbach’s a .96 .95 .89
M 1.99 3.31 2.53
SD 0.88 0.86 0.98
Elaboration. The Perse (1990) five-item cognitive elaboration
scale was
used to measure respondents’ level of elaboration on political
content.
Participants reported how often they have thoughts referenced
in the five
statements, ranging from 1 (never) to 5 (very often). Responses
to the five
items were summed and averaged to create an index of cognitive
elaboration
(M¼ 3.45, SD¼ 0.82, a¼ .90).

Discussion. Eveland and Shah (2003) measured in-group
discussion
with one item (e.g., asking liberals how often do they talk with
other liberals)
and out-group discussion (e.g., asking liberals how often they
talk with con-
servatives). To obtain a more nuanced measure of in-group and
out-group
discussion, we expanded this measure to obtain a broader
approximation
of in-group discussion across five different groups of people
from the same
political party (coworkers, neighbors, friends, family, and
acquaintances)
and out-group discussion across those same groups.. Previous
researchers
have found that a majority of political discussion occurs
between people in
TABLE 2
Media Use for Political Information Means and Standard
Deviations
Medium M SD
Television news 3.71 1.15
Home pages (e.g., MSN, Yahoo, Google) 3.03 1.26
Internet news sites (e.g., CNN.com, CBS.com, Reuters.com,
Politico,
NYTimes.com)
2.84 1.20

Radio news 2.74 1.24
Newspapers (e.g., USA Today, New York Times, Wall Street
Journal) 2.62 1.22
Nonsatirical political commentary programming (e.g., Hardball
with Chris
Matthews, The O’Reilly Factor, The Rachel Maddow Show)
2.51 1.31
Satirical late-night programming (e.g., Saturday Night Live,
The Colbert
Report, The Daily Show, The Tonight Show with Jay Leno, The
Late Show
with David Letterman)
2.40 1.26
Magazines (e.g., Time, Newsweek, People, Fortune) 2.34 1.21
Social networking sites (e.g., MySpace, Facebook) 2.32 1.24
Political talk radio (e.g., Rush Limbaugh, Glenn Beck, Dennis
Miller) 2.15 1.28
Video-sharing websites (e.g., YouTube, Hulu) 2.12 1.17
Books (e.g., biographies=autobiographies of candidates, books
about politics
or political issues)

2.08 1.15
Political websites (e.g., MoveOn.org, candidate websites,
Snopes,
FactCheck.org)
2.05 1.14
Blogs=Microblogs (e.g., Drudge Report, The Republican Guru,
Fidler’s
Politics, Daily Kos, Twitter)
1.96 1.17
Note. Means are based on a 5-point scale. Scores ranged from 1
(never) to 5 (very often).
N¼ 279.
these groups (e.g., Mutz, 2002; Mutz & Martin, 2001). To score
the amount
of discussion, we summed these measures, as the number of
discussions was
additive, to develop a general political in-group discussion
measure
(M¼ 6.68, SD¼ 10.47, a¼ .80) and political out-group measure
(M¼ 4.45,
SD¼ 6.54, a¼ .78).

RESULTS
In each regression analysis, demographic variables (e.g., age,
gender) and
personal characteristics (e.g., political opinion leadership,
political content
affinity, and political social identity) were entered on the first
step. Motives
for using media for political information were entered on the
second step.
Media use was entered on the third step. Finally, elaboration
was entered
on the fourth step. This order is in line with the theoretical
assumptions of
the uses and gratifications model that guided this study. To
reiterate, the
theoretical model suggests that personal characteristics
influence motives
for using media for political information, which in turn
influence media
use, which influences elaboration on political information, and
ultimately
these variables work in concert with each other to influence
media effects.
Political Discussion with Political in-Group Members
Demographics entered in the first step accounted for 26% of the
variance in
in-group discussion. Male gender (b¼�.23, p< .01), political
opinion lead-
ership (b¼ .29, p< .001), and political content affinity (b¼ .22,
p< .01) were
significant predictors of discussion with political in-group
members.

Motives for using media for political information, entered on
Step 2,
explained an additional 4.5% of variance in political discussion
with political
in-group members. The F change was significant (p< .01).
Political in-group
achievement (b¼ .20, p< .05) and political utility (b¼ .16, p<
.05) were sig-
nificant predictors. Political opinion leadership and gender
remained signifi-
cant predictors on this step. Political content affinity was no
longer significant.
Media use was entered on Step 3. This resulted in an additional
5.2% of
the variance explained. The F change was not significant (p¼
.33). Political
website use (b¼ .17, p< .05) and nonsatirical political
commentary
programming viewing (b¼ .15, p< .05) were the only significant
predictors.
Political opinion leadership and gender remained significant
predictors on
this step. The political in-group affiliation motive ceased to be
a predictor.
Elaboration was entered on Step 4. This accounted for only .3%
of the
variance in political discussion with political in-group
members. The F
change was not significant (p¼ .25), and elaboration was not a
significant

predictor. Political opinion leadership, gender, political
websites, and
nonsatirical political commentary programming remained
significant
predictors of political discussion with political in-group
members.
The final equation accounted for 36.0% of the variance in
political
discussion with political in-group members (see Table 3). The
results
suggest that male political opinion leaders who used political
websites and
nonsatirical political commentary programming for political
information
TABLE 3
Summary of Regression Analysis for Predicting Political
Discussion with Political In-Group
and Out-Group Members
Variable b (In-Group) b (Out-Group)
Step 1: Audience characteristics
Age .03 .09
Gender �.12�� .15��
Political social identity �.00 �.06
Political opinion leadership .22�� .23��
Political content affinity .06 .05

Step 2: Motives for using media
Idiomatic-use �.08 .10
Political utility .05 .11
Political in-group achievement .11 .01
Step 3: Media use
Home pages �.03 �.01
Internet news sites .03 .01
Blogs=Microblogs �.02 �.20��
Political websites .17� �.02
Video-sharing websites .05 .11
Social networking sites .05 .06
Television news .05 �.03
Nonsatirical political commentary .15�� .20��
Programming
Satirical late-night programming �.02 .02
Television advertisements .04 �.04
Movies=Documentaries .03 �.04
Political talk radio .00 .12
Radio news �.09 �.03
Radio advertisements �.01 �.02
Newspapers �.07 �.06
Magazines .13 .08
Books �.16 .10
Print advertisements .01 .07

Step 4: Cognitive elaboration
Elaboration .09 .03
Note. All betas are final betas on the last step of the regression.
N¼ 279.
�p< .05. ��p< .01. ��p< .001.
discussed politics with people in their political party more than
did their
counterparts.
Political Discussion with Political Out-Group Members
Personal factors entered in the first step accounted for 23.6% of
the variance
in political discussion with out-group members. Male gender
(b¼�.17,
p< .01), political opinion leadership (b¼ .29, p< .001), and
political content
affinity (b¼ .19, p< .01) were significant predictors of
discussion with
political out-group members.
Motives for using media for political information, entered on
Step 2, con-
tributed an additional 5.4% of variance in political discussion
with political
in-group members. The F change was significant (p< .001). The
political
utility motive was a significant predictor of discussion
frequency with polit-

ical out-group members (b¼ .19, p< .05). Political opinion
leadership and
gender remained significant predictors on this step, and
political content
affinity ceased to be a predictor.
Entering media use on Step 3 resulted in an additional 8.3% of
the vari-
ance explained. The F change was significant (p< .01). Use of
nonsatirical
political commentary programming (b¼ .20, p< .05) was a
positive predic-
tor and use of blogs=microblogs (b¼�.20, p< .05) was a
significant nega-
tive predictor of out-group discussion. Political opinion
leadership and
gender remained significant predictors.
Elaboration, entered on Step 4, accounted for less than 1%
change in
variance explained. The F change was not significant (p¼ .72).
Political opi-
nion leadership, gender, use of nonsatirical political
commentary program-
ming, and blogs=microblogs remained significant predictors of
political
discussion with political out-group members.
The final equation accounted for 37.4% of the variance in
political dis-
cussion with political out-group members (see Table 3). The
results suggest
that male political opinion leaders, who tended to use
nonsatirical political
commentary programming but not blogs or microblogs for
political infor-

mation, tended to discuss politics with people outside their
political party
more than did their counterparts.
DISCUSSION
In this study, we argued that in order to understand political
discussion
better, scholars need to consider the role of personal
characteristics, motives
for using media for political information, media use, and
elaboration on
political issues and examine how these work in concert to
influence political
discussion with political in-group and out-group members.
Guided by uses
and gratifications theory, we developed and tested a model
designed to
examine these relationships. The results of our analyses provide
a starting
point for future investigations into the media system as a whole,
and how
certain background characteristics, media-use motives, and the
use of parti-
cular media play a role in facilitating in-group and out-group
discussion.
Personal Characteristics
As suggested by previous researchers, we found that male
participants and

political opinion leaders engaged in political conversations with
in-group
and out-group members. Perhaps even more interesting is that
these relation-
ships remained consistent, regardless of those with whom they
discussed
politics. However, because the tenor of political discussions in
which the
participants engaged was not examined, it is possible that the
conversations
occurring between in-group members and out-group members
were funda-
mentally different. For example, these conversations could be
arguments
or discussions of politics as a whole. However, because we did
not examine
the tenor of these conversations, we are unable to postulate the
nature of
these interactions. Future researcher should look at the tenor of
these conver-
sations (e.g., discussions or arguments) with political out-group
members.
Media Use
An interesting finding of this study was the role that media use
played in
predicting political discussion. We looked at 18 different media
sources in this
study, examining how frequently each media source was used
for political
information (see Table 2) and, ultimately, examining how these
sources
related to in-group political discussion. Of interest, none of the
five most com-
monly used sources for political information was related to

political discussion
when controlling for personal characteristics and motives for
using media for
political information. This suggests that although political
discussion may be
a common outcome of using media for political information,
some media
sources were more influential than others. In addition, the
media sources that
did seem to influence political discussion were not the most
widely used. As
identified in the regression equations, only nonsatirical political
commentary
programming and political website use predicted political
discussion with
in-group members, and nonsatirical political commentary
programming and
blog=microblog use predicted discussion with out-group
members.
Of interest, nonsatirical political commentary programming was
the
only type of programming that was positively related to both in-
group and
out-group discussion. Nonsatirical political commentary
programming
hosts take time to break down political issues and provide
commentary
on the potential ramifications of a piece of legislation, or even
provide an
examination of the motives of certain political agents. In doing

so, these
hosts can serve as opinion leaders for the specific groups in the
public.
Previous scholars have found political commentary programs
such as The
O’Reilly Factor and The Rachel Maddow Show are popular
programs
because viewers use these programs to develop, influence, and
inform their
own opinions on politics (e.g., Conway, Grabe, & Grieves,
2007; McCombs,
Holbert, Kiousis, & Wanta, 2011). Furthermore, Norton (2011)
suggested
that the stylistic approaches used by hosts of these types of
programs provide
interpretive frames for their viewers to understand the world
around them
and readily recall that information in later conversations,
essentially training
them for the political discourse boxing ring.
Moreover, these programs provide users with the opportunity to
learn
about their own party’s views on specific issues, as well as the
views of oppos-
ing parties. Many of these programs commonly bring on guest
commentators
from a variety of political stances that allow viewers to be
exposed to the
opinions of others. In addition, many of these programs’ hosts
dissect the
arguments of their opponents on the show, allowing their
viewers to learn
more about politics and arming these viewers with the tools to
defend their
own opinions from politically different stances. Furthermore,

this format
allows viewers to understand how an argument with a person
from a differ-
ent political party may progress from a relatively safe vantage
point.
Of interest, political blogs and microblogs were a negative
predictor of dis-
cussion with political out-group members. This may suggest
that the people
who use blogs and microblogs are a unique cross-section of
society. Specifi-
cally, scholars have found that people who regularly use
political blogs and
microblogs for their political information are the most likely to
immerse
themselves in partisan echo chambers (Kim & Johnson, 2012;
Saleton,
2012). Previous scholars have suggested that political blog
users—a small
section of society—are strongly partisan people who seek out
only those
political messages that are in-line with their already existing
political views
while avoiding information that contradicts their notions of the
political
arena (e.g., Brundidge & Rice, 2009). Our results suggest that it
would seem
that the people who use political blogs and microblogs for their
political
information fit into this cross-section of society—at least to the
extent that
they may not use these particular social media to discuss
politics with politi-
cally disparate others. In addition, although we did not
specifically measure

for political polarization, other scholars have suggested that
consistent
avoidance of differing political opinions can lead people to
become more
polarized in the political attitudes, which can lead to the
election of more par-
tisan officials and, ultimately, more partisan gridlock (Jamieson
& Cappella,
2008; Hetherington, 2001).
This finding may provide a fruitful venue for future research in
examining
the role of blogs and other online information sources to
determine the
impact of these sources on political extremism, political
polarization, and
attitudes toward out-groups.
The fact that certain social media—blogs and microblogs—were
negative
predictors of outgroup discussion may temper claims of others
that these are
robust tools for political dialogue. For instance, Vis (2014)
explained that
Twitter and other blogs have a history of quashing large-scale
misinformation
campaigns, specifically citing the 2011 UK riots as examples
where people took
to Twitter to debunk the rumor well before any traditional
media channel. The
fact that use of these tools did not predict in-group discussion,
but detracted

from out-group discussion suggests that these particular media
sources were
not used widely for these purposes in our sample.
Limitations
There were many different limitations to this study. One such
limitation was
the response rate to our survey. Although 327 participants were
sufficient
for our analysis, it is important to recognize that this is a small
number
of people from which to reach definitive conclusions about what
influences
the entire U.S. population to discuss politics. Although previous
researchers
have reported similar response rates, and indeed have found that
response
rates as low as 20% do not impact the results of a survey,
ideally a higher
response rate would have allowed for more generalizable
findings.
Next, we did not account for who initiated the discussions.
Presumably,
people who initiate discussions are different from those who
happen to join
a conversation because of proximity. Previous investigations
have found that
people who initiate conversations are more active in their media
use and are
more likely to control the topic of conversation (Himielboim,
Gleve, &
Smith, 2009). Understanding these differences could be useful
for future
investigations.

In addition, we did not account for the type of discussions that
occurred.
It would be useful to understand the nature of these discussions
with
in-group and out-group members. As discussed earlier, a variety
of different
types of discussion can occur. Moreover, Korostelina (2007)
suggested that
in situations where people’s views differ on politics, they are
more likely to
engage in arguments about politics; however, when people have
a similar
view, they are more likely to engage in a casual conversation.
Presumably,
there are differences between casual conversations about
politics and argu-
ments about politics. The nature of the questions we asked did
not explore
this area. The differences, at least in terms of frequency,
between casual
conversations and arguments could have influenced the results
of this study.
Finally, where there were several different background
characteristics we
did include, there are numerous psychographic factors in
addition to these
that may provide interesting insight into political discussion and
should
be considered in future research. For instance, we did not
include a measure

of introversion. Previous scholars have found that introverts, or
those who
are described as more reserved, quiet, or shy, are less likely to
socialize than
extroverts (McCrae & Costa, 1987). In this light, it would be
relevant to
know if the participants who engaged in political discussion
were more
likely to be introverted or extroverted. In addition, we could
have measured
the participants’ political self-efficacy or confidence in their
political beliefs.
Presumably, those who are more confident in their beliefs may
engage in
political discussion more than those with less confidence.
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INTRODUCTIONTHE ROLE OF DISCUSSION IN
POLITICSUSES AND
GRATIFICATIONSDemographicsPolitical Opinion
LeadershipPolitical Social IdentityPolitical Content
AffinityMotives for Using Media for Political InformationMedia
UseElaborationPolitical DiscussionRESEARCH QUESTIONS
AND HYPOTHESESMETHODSampleMeasuresPolitical Social
IdentityPolitical Opinion LeadershipPolitical Content
AffinityMotivesRESULTSPolitical Discussion with Political in-
Group MembersPolitical Discussion with Political Out-Group
MembersDISCUSSIONPersonal CharacteristicsMedia
UseLimitationsREFERENCES
Distribution
Today, companies must decide whether to sell their products
directly to their customers via the Internet or to use more
traditional methods of distribution.

Respond to the following:
· Do you think the high-end designer apparel brands, such as
Gucci, Chanel, or Prada, sell their goods direct to consumers
through the Internet? Give reasons for your answer.
· What, if any, practices of corporate social responsibility
(CSR) do they exhibit? Is there a factor of showing it in any
online marketing strategy?
Write in 300–500 words.
By Saturday July 8, 2017,
Investigating grit and its relations with college students’
self-regulated learning and academic achievement
Christopher A. Wolters & Maryam Hussain
Received: 9 May 2014 /Accepted: 4 November 2014 /Published
online: 18 November 2014
# Springer Science+Business Media New York 2014
Abstract We investigated grit and its relations with students’
self-regulated learning (SRL)
and academic achievement. An ethnically diverse sample of 213
college students completed an
online self-report survey that included the Grit Short scale
(Duckworth and Quinn Journal of
Personality Assessment, 91(2), 166–174, 2009), seven
indicators of SRL and their past and
present academic achievement. Results indicated that one aspect
of grit, perseverance of effort,
was a consistent and adaptive predictor for all indicators of SRL

including value, self-efficacy,
cognitive, metacognitive, motivational, time and study
environment management strategies,
and procrastination. A second aspect of grit, consistency of
interest, was associated only with
the latter two facets of SRL. Perseverance of effort predicted
achievement before, but not after,
accounting for SRL; hence, students’ engagement in SRL may
serve as a mediating pathway
through which this aspect of grit is associated with improved
academic outcomes. In contrast,
consistency of interest showed no relation to achievement.
Implications of the findings for
additional research and instruction are discussed.
Keywords Grit . Self-regulated learning . Motivation .
Strategies . Procrastination .
Achievement . Postsecondary
Over the past 25 years, self-regulated learning (SRL) has
emerged as a major framework used
to understand, evaluate, and improve students’ functioning
within academic contexts (Schunk
and Zimmerman 2008). Individual differences characterized by
many as more stable or trait-
like, including personality, intelligence, and achievement
motives, also have been investigated
repeatedly as an important influence on students’ academic
functioning (Diseth, and
Kobbeltvedt 2010; Komarraju et al. 2009; Richardson et al.
2012). Studies integrating these
two perspectives indicate that accounting for both SRL and
more stable individual differences
may provide for a better understanding of students’ academic
performance than either one does

alone (Bidjerano and Dai 2007; De Feyter et al. 2012; Eilam et
al. 2009; Richardson and
Metacognition Learning (2015) 10:293–311
DOI 10.1007/s11409-014-9128-9
C. A. Wolters (*)
Department of Educational Studies, Dennis Learning Center,
250 Younkin, The Ohio State University,
Columbus, OH 43201-2333, USA
e-mail: [email protected]
M. Hussain
Department of Educational Psychology, University of Houston,
Houston, TX 77204, USA
Abraham 2009). In the present study, we advance this area of
research by investigating the
relation of grit, SRL and academic achievement. Although grit
is considered distinct from
other personal traits and has proven useful for explaining
academic outcomes (Duckworth and
Quinn 2009; Maddi et al. 2012; Strayhorn 2013), the relation of
grit and SRL has not yet been
investigated. The present study addresses this gap by exploring
whether grit can be used to
predict seven indicators of college students’ engagement in
SRL, and whether grit and SRL
together can be used to understand students’ academic
achievement.
Grit
Grit is defined as a person’s trait-level perseverance and
passion for long-term goals

(Duckworth et al. 2007). As such, grit has been conceived as a
stable characteristic or
disposition of the individual that, similar to traditional
personality traits, influences his/her
attitudes and behavior across diverse contexts (Duckworth and
Quinn 2009; Kleiman et al.
2013; Reed et al. 2013). Compared to their less gritty peers,
individuals with higher levels of
grit are expected to exhibit greater persistence in the pursuit of
their goals despite setbacks,
distractions, or other forms of interference. Within educational
contexts, grit is portrayed as a
potentially important influence on outcomes such as students’
engagement, achievement level,
retention and probability of graduation (Duckworth and Quinn
2009; Maddi et al. 2012;
Strayhorn 2013).
Although still quite limited, the research examining grit
indicates that it can be measured
reliably and that it is empirically distinct from other trait-like
individual differences. In
particular, Duckworth and colleagues (2007) developed an
initial self-report measure of grit
and provided some evidence that it was different than
traditional personality constructs such as
conscientiousness. Although analyses with adults suggested that
it consisted of two related
dimensions, these researchers examined grit using a single 12-
item scale. Based on samples
from several distinct populations, these researchers showed that
this broad indicator of grit was
related positively to educational attainment, college grades,
self-control, retention within a
rigorous military training program, and adolescents’
performance in a competitive national

spelling bee.
Adding to this research, Duckworth and Quinn (2009)
developed a shorter 8-item self-
report survey based on a subset of the original grit items. Re-
evaluation of data from the
participants in Duckworth et al. (2007) with this smaller set of
items as well as an additional
sample of adults indicated that grit was best modeled as two
first-order latent factors that also
loaded on to one second-order latent factor. One of the first-
order factors, titled consistency of
interest, reflected participants’ reported tendency to adhere to
particular goals over longer
periods of time. The second first-order latent factor, termed
perseverance of effort, represented
participants’ reported tendency to sustain the time and energy
necessary for accomplishing
long term tasks even in the face of distractions. Despite the
distinction between these factors,
Duckworth and Quinn utilized a single 8-item indicator of grit
in several additional studies
with adolescents, military cadets and adults. Overall,
Duckworth and Quinn replicated many of
the earlier findings from Duckworth et al. (2007) including the
independence of grit from
aspects of personality, and its positive relation to academic
success, retention within a rigorous
military training program, and ranking in a national spelling
bee. They also found that the
single short measure of grit was relatively stable across the span
of one year among
adolescents.
Recent contributions from other researchers tend to confirm the
view of grit as a potentially

important influence on aspects of individual’s persistence and
performance, including within
294 C.A. Wolters, M. Hussain
academic contexts. Maddi et al. (2012), for instance, found that
a singular indicator of grit was
a strong predictor of retention and performance in sample of
military cadets. Higher levels of a
general measure of grit have also been linked to increased
intensity of exercise (Reed et al.
2013) and reduced suicide ideation (Kleiman et al. 2013). In a
sample of high school students,
MacCann and Roberts (2010) found that both dimensions of
grit, but especially the persever-
ance of effort, were positively correlated with life satisfaction,
multiple aspects of conscien-
tiousness and teacher’s rating of social behavior, but not to
grades or academic readiness.
Finally, Strayhorn (2013) found that an overall indicator of grit
was a positive predictor of self-
reported grades among African-American males attending a
university with a predominantly
White student population. In this study, grit was a stronger
predictor of college grades than
high school GPA and other standardized college entrance
exams.
Summing across this work, grit appears to represent a compound
personal characteristic
that is associated with students’ tendency to be successful
within achievement contexts. The
evidence linking grit specifically to students’ academic
achievement, however, is still very

limited and somewhat inconsistent. For instance, the two studies
that examined the relation of
grit with students’ course grades produced conflicting results
(MacCann and Roberts 2010;
Strayhorn 2013). Further, the pathways through which this
impact may be realized have not
yet been examined. In particular, the possibility that grit is
associated with increased engage-
ment in SRL has not been investigated.
Self-regulated learning
SRL is understood generally to be the process through which
students take an active,
purposeful role in managing motivational, cognitive and
behavioral aspects of their own
learning (Pintrich 2004; Zimmerman 2000). This management is
accomplished through
students’ engagement of various sub-processes that include goal
setting, activation of relevant
prior knowledge, progress monitoring, engagement and
regulation of learning strategies, and
reflection (Pintrich 2004; Winne and Hadwin 1998). Across
these various sub-processes, two
consistently critical components of SRL reflect students’
motivation and their engagement and
use of different cognitive and regulatory strategies.
Motivation
SRL is an effortful process that involves the activation and use
of substantial cognitive and
metacognitive resources. Hence, motivation is viewed as an
important facet of what it takes to
engage in SRL (Pintrich and Zusho 2002; Winne and Hadwin
2008; Zimmerman and Schunk

2008). The forms of motivation that have been incorporated into
models of SRL are diverse
with no clear hegemony (Schunk and Zimmerman 2008). For
instance, models of SRL have
highlighted the roles of goal setting, achievement goals,
interest, value, perceived self-efficacy
and attributions (Pintrich and Zusho 2002; Winne and Hadwin
2008). The focus in this study is
on two aspects of achievement motivation, perceived self-
efficacy and value, which consis-
tently have been used within models of SRL (Pintrich and Zusho
2002; Zimmerman 2000).
Self-efficacy, defined as students’ beliefs about whether they
are capable of successfully
reaching particular learning goals, has been intricately linked to
their goal setting, choice
and effort within academic contexts (Linnenbrink and Pintrich
2003; Pajares 1996;
Zimmerman 2000). As well, students who report higher levels
of self-efficacy have been
found to exhibit increased use of the cognitive and
metacognitive strategies central to SRL.
Even when examined outside the framework of SRL, self-
efficacy has proven to be an
Grit and self-regulated learning 295
important predictor of success, retention, and performance
within college student populations
(Richardson et al. 2012; Robbins et al. 2004).
Students’ value for the material they are learning is also a key
form of motivation within
many models of SRL (Pintrich and Zusho 2007; Wigfield and

Cambria 2010). Value is a multi-
dimensional construct that can incorporate students’ personal
interest, importance, usefulness,
and aspects of situational interest (Wigfield and Eccles 2000).
In general, students who
perceive the materials or skills they are learning as useful,
interesting, important or enjoyable
are more likely to engage the regulatory strategies necessary for
SRL and evidence higher
academic achievement (Pintrich and Zusho 2007; Wigfield and
Cambria 2010).
Strategy use
From its inception, the activation and effective use of strategies
that promote learning,
understanding and achievement has been a hallmark of what it
means to engage in SRL
(Pintrich 2004; Winne and Hadwin 1998; Zimmerman 2000).
Although cognitive and
metacognitive learning strategies have been examined most
commonly, researchers recognize
that other types of strategies are also critical to successful SRL
(Pintrich 2004; Wolters 2003a).
Consistent with this view, we assess four distinct types of
strategies that reflect adaptive
aspects of SRL including cognitive, metacognitive,
motivational, and time and study environ-
ment management.
Generally, cognitive strategies are understood to include
different tactics that students use to
facilitate the encoding and storage of the material they are
supposed to learn (Weinstein et al.
2011). For instance, cognitive strategies include efforts to
rehearse, elaborate, or organize

material one is trying to learn. Closely linked to these,
metacognitive strategies include
students’ efforts to plan, select, monitor, evaluate and, when
necessary, modify or regulate
their use of learning strategies (Pintrich et al. 2000).
Motivational regulation strategies
represent students’ efforts to control their own level of
motivation or motivational processing
(Wolters 2003a). This type of strategy, including self-
consequating, self-talk about goals, and
making learning tasks more enjoyable, are thought to be
particularly important when students
are facing obstacles to their continued engagement and effort on
academic tasks (Wolters 1998,
2003a). Time management and control of the learning
environment also represent important
self-regulatory skills (Pintrich 2004; Zimmerman et al. 1994).
These more behavioral strate-
gies reflect students’ efforts to control when and where they
study including the use of
planners, to-do lists, and well-organized study spaces.
Overall, there is compelling evidence that the use of SRL
strategies is associated positively
with students’ achievement within academic contexts. The
evidence for this connection is
strongest for students’ use of cognitive and metacognitive
strategies (Pintrich and Zusho
2007). Research also shows that college students have a
repertoire of motivational regulation
strategies and that use of these strategies is associated with
increased effort, persistence, and
academic performance (Schwinger et al. 2009; Wolters 1998).
Although not always conducted
using a SRL framework, there is also ample research showing
that students who report greater

time management tend to get better grades (Claessens et al.
2007; Kitsantas et al. 2008; Macan
et al. 1990).
Procrastination
We investigate procrastination as a reflection of students’
ability to effectively self-regulate
their learning. Procrastination can be defined as students’ delay
of tasks or decisions that are
necessary and will eventually be completed (Steel 2007).
Although studied through many
theoretical perspectives, procrastination repeatedly has been
portrayed as a failure of effective
self-regulation (Steel 2007; Wolters 2003b). Consistent with
this view, procrastination by
students within academic settings has been associated with
lowered academic performance
(Schouwenburg et al. 2004). Procrastination also has been
linked to maladaptive outcomes
such as reduced sense of well-being, depression, stress and
fatigue (Schraw et al. 2007; Steel
2007). At the same time, studies have consistently found that
procrastination is prevalent
among college student populations with some estimates
indicating that over 75 % of students
reporting that they procrastinate regularly (Ferrari et al. 2007;
Schraw, et al. 2007; Steel 2007).
Grit and self-regulated learning

According to Pintrich (2004), one assumption consistent within
most models of SRL is that
SRL processes serve as a mediator between students’ personal
and background characteristics
and their performance within particular contexts. Dispositions,
personality traits, or other stable
individual differences such as grit are, therefore, commonly
viewed as precursors or potential
influences on the attitudes, beliefs, cognitive processes, and
behaviors that embody SRL
(Bidjerano and Dai 2007; Eilam et al. 2009; Komarraju et al.
2009). In line with this
assumption, one would expect that aspects of grit could be used
to explain college students’
motivation and use of strategies emblematic of SRL. Up to this
point, however, no published
empirical research has investigated these theoretical links
directly. Duckworth et al. (2010) did
find that grit was a positive predictor of the deliberate and more
effortful forms of practice
reported by contestants preparing for a national spelling bee.
This study, however, did not
assess any of the specific strategies used during the practice
time and was focused on early
adolescents engaged in an extra-curricular activity. The overall
goal of the present study was to
address this major gap in prior research by examining the
relations between aspects of grit and
college students’ SRL as represented by measures of their
value, self-efficacy, reported use of
four types of learning strategies, and procrastination.
Despite the lack of studies examining grit and SRL directly,
research investigating similar
trait-like individual differences supports the need to investigate
these relations. Based on the

five-factor model of personality, for instance, greater
conscientiousness among college stu-
dents tends to be associated with higher levels of academic
motivation and especially self-
efficacy or perceived competence (De Feyter et al. 2012;
Komarraju et al. 2009; Richardson
and Abraham 2009). As well, college students who are more
conscientious also tend to report
increased use of some learning strategies typical of SRL
(Bidjerano and Dai 2007). Given its
association with attributes such as diligence, dependability,
organization, punctuality, careful-
ness and self-control, conscientiousness also has been linked to
lower levels of procrastination
(vanEerde 2004). Although not found in every case (Trautwein
et al. 2009), several studies
have concluded that the influence of conscientiousness and
other personality traits on students’
achievement is mediated by their motivation and use of
regulatory strategies (Bidjerano and
Dai 2007; De Feyter et al. 2012; Eilam et al. 2009; Richardson
and Abraham 2009).
Achievement motives represent another type of trait or stable
disposition that has been used
to understand students’ academic functioning (Bartels et al.
2010; Diseth and Martinsen 2003).
Motives linked to wanting to achieve success and to avoid
failure both have been used to
explain more context specific aspects of students’ motivation,
especially their achievement
goals (Bartels et al. 2010; Chen et al. 2009; Conroy and Elliot
2004; Elliot and Church 1997;
Elliot and McGregor 2001; Elliot and Murayama 2008; Michou
et al. 2013). Likewise,
approach motives have been associated positively with self-

reported engagement and the use
of deep, metacognitive or other adaptive types of strategies
emblematic of SRL (Bartels and
Magun-Jackson 2009; Bartels et al. 2010; Diseth and
Kobbeltvedt 2010; Diseth and Martinsen
2003; Michou et al. 2013). In contrast, fear of failure or other
avoidance motives have been
associated with performance-avoidance goals and test anxiety,
along with decreased use of
adaptive strategies (Bartels et al. 2010), increased use of
surface strategies (Diseth and
Kobbeltvedt 2010; Diseth and Martinsen 2003), and increased
levels of self-handicapping,
procrastination, and other indicators of poor self-regulation
(Chen et al. 2009). Finally, studies
also show that the relations of achievement motives to students’
academic performance may be
mediated by more situational forms of motivation and
engagement (Diseth and Kobbeltvedt
2010; Diseth and Martinsen 2003; Elliot and Church 1997;
Elliot and Murayama 2008).
Research questions
In sum, individual differences similar to grit have been linked
to several indicators of students’
SRL such as their motivation, use of learning strategies, and
procrastination. Research investi-
gating these relations with regard to grit, however, is absent.
We address this gap in prior work via
four related research questions. One, is grit related to college

students’ value and self-efficacy?
Given its basic conceptual definition as well as past research
with conscientiousness and
achievement motives, we expected that grit would be associated
positively with both of these
adaptive motivational beliefs and attitudes. Two, is grit related
to more strategic aspects of
students’ SRL including their use of different regulatory
strategies and level of academic
procrastination? We expected that grit would be a positive
predictor of students’ use of regulatory
strategies and negatively associated with their self-reported
procrastination. Three, is grit related
to college students’ academic achievement? Although the
evidence of this connection is not
entirely consistent, we anticipated that grittier students would
tend to get better grades. Finally, do
the motivational and strategic aspects of SRL mediate the
relation between grit and students’
academic performance? Given findings with other types of
individual differences, we expected
that aspects of SRL may also mediate any relation between grit
and students’ grades.
Method
Participants
The 213 participants for this study came from a large and
diverse public university. The
students were primarily female (n=188, 88 %), and self-reported
their race/ethnicity as
African-American (n=45, 21 %), Asian/Pacific Islander (n=53,
25 %), Hispanic (n=62,
29 %), White (n=43, 20 %), or Other (n=10, 5 %). The academic
rank reported by the

participants included freshman (n=28, 13 %), sophomore (n=51,
24 %), junior (n=74, 35 %),
and seniors or post-baccalaureate (n=60, 28 %).
Procedures
Participants were recruited through a subject pool associated
with multiple undergraduate
psychology and educational psychology courses. Students in
these courses had access to an
electronic list with short descriptions for the studies that could
be used to satisfy requirements for
participation in research. Students who elected to volunteer for
the present study clicked on a link
that took them first to a consent document and, if approved, to
the actual survey. Surveys were
completed during the final two weeks of the autumn semester
before the final exam period.
Measures
The primary instrument was an online self-report survey with a
total of 136 items. Only data
from 78 items related to demographics, grit, achievement
motivation, strategy use, procrasti-
nation and academic performance were used in the present
study. Other than the demographics
and performance sections, all items used Likert-styled items
with a response scale ranging
from 1 (not at all true of me) to 5 (very true of me).
Grit Participants completed the eight-item Grit Short scale

(Duckworth and Quinn 2009). In
line with Duckworth and Quinn, a confirmatory factor analysis
with the current sample
indicated that a measurement model with two first-order latent
factors fit the data well (χ2
(19, N=213) =20.04, p=.392; RMSEA=.02 (90 % confidence
interval [CI]=.00, .06),
CFI=.997). In contrast, alternative models that reflected all
items in one general factor (χ2
(20, N=213) =125.756, p<.001; RMSEA=.16 (90 % CI=.13,
.19), CFI=.645) or that
included a second order general factor (χ2 (36, N=213) =333.81,
p<.001; RMSEA=.20
(90 % CI=.18, .22), CFI=.000) did not fit the data well. Given
these findings and consistent
with prior work with this scale (Duckworth and Quinn 2009),
we constructed separate scales
representing the two first order factors based on the mean of the
relevant items. Perseverance
of effort reflected students’ self-reported tendency to sustain
the time and energy necessary for
accomplishing long-term tasks. Consistency of interest
represented students’ self-reported
tendency to stick with particular goals over longer periods of
time. Items for these scales
included “I finish whatever I begin.” (Perseverance of effort),
and “I often set a goal but later
choose to pursue a different one” (Consistency of effort, reverse
coded). Coefficient alphas for
each of the scales, as well as all those described below are
presented in Table 1.
Motivation Motivation was measured using 11 items modified to
assess students’ beliefs and

attitudes regarding school or learning in general rather than a
specific course (Pintrich et al.
1993; Wolters 2003b). Six items represented value, or the extent
that students perceived
coursework as useful, interesting, and important. Five items
reflected self-efficacy, or how
confident students felt about their ability to learn and
successfully complete their coursework.
Strategy use and procrastination Using modified items from
Pintrich et al. (1993) and Wolters
(2003b; Wolters and Benzon 2013) to refer to learning or
coursework in general, four types of
self-regulatory strategies were assessed. Cognitive strategies
measured participants’ reported
use of rehearsal, elaboration and organization strategies to
complete academic tasks (9 items).
Metacognitive strategies measured participants’ reported use of
techniques for planning,
monitoring, and managing their learning strategies (9 items).
Time and study environment
management reflected the extent to which students believed they
used effective strategies for
academic scheduling and regulating where they studied (8
items). Motivational strategies
measured participants’ reported use of strategies for managing
their level of motivation and
their effort at particular academic tasks (14 items). Finally,
procrastination was assessed by
adapting the 12 items from the Pure Procrastination Scale (Steel
2010) to refer to academic
contexts. These items measured students’ reported tendency to
delay making decisions, begin
tasks, or miss deadlines for their academic work. As shown in
Table 1, all of the motivation,
strategy use, and procrastination scales had moderate to high

levels of internal consistency.
Academic performance Prior achievement was based on a single
item on which students
reported their cumulative high school grade point average
(HSGPA). Although not ideal, self-
reported grade point average is a widely used measure of
academic performance and has
T
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shown a high correlation with actual grade point average
(Caskie et al. 2014; Kuncel et al.
2005). Simple mean imputations were used to replace the
missing observations for nine
students who did not respond or indicated that they did not
know their HSGPA. The eight
response options for this item were based on 0.25 GPA
increments from 8 (4.00–3.76) to 1
(2.00 or below). Current achievement was based on three
separate items on which students
reported the number of courses they were currently taking in
which they expected to earn 1) an
A, 2) a B or C, and 3) a D or lower. Responses to these items
were combined to create one five-
level variable with higher scores representing better expected
grades in the courses they were
taking the semester the survey was completed. Students with the
top score (i.e., a 5) reported
that they expected to receive an “A” in all of their courses.

Students with scores of 4 and lower
reported that they were expecting an increasingly larger number
of B and C, or D and lower
grades.
Results
Results are divided into two sections. First, we present
descriptive information and bivariate
correlations among the major variables. Second, we discuss
findings from three sets of
multiple regressions.
Descriptives and correlations
The means and standard deviations for the grit, SRL and
achievement variables are presented
in Table 1. The mean for perseverance of effort appeared
somewhat higher (M=3.5) when
compared to the mean for consistency of interest (M=2.84).
Consistent with previous studies
using similar scales with college populations (Wolters 2003b),
all of the means for the SRL
variables fell near the middle of the response scale.
The bivariate correlations among the grit, SRL and achievement
measures are also pre-
sented in Table 1. Most noteworthy among these results is the
low correlation between the two
aspects of grit, as well as the distinctive pattern of relations
each had with the SRL and
achievement measures. Perseverance of effort was positively
correlated with both value and
self-efficacy, as well as with each of the four types of self-
regulatory strategies. In contrast,
consistency of interest was correlated only to time and study

environment management. Both
aspects of grit were correlated negatively with procrastination;
only perseverance of effort was
correlated positively with current achievement (see Table 1).
Multiple regressions predicting SRL and achievement
Predicting students’ motivational beliefs We examined the
relations of perseverance of effort
and consistency of interest to students’ value and self-efficacy
in two separate multiple
regressions. In addition to the two aspects of grit, prior
achievement, sex, and age were
included as control variables. Results from these analyses are
presented in Table 2. Overall,
the model accounted for about 16 % of the variance in value,
F(5, 205) =8.04, p<.001, and
about 19 % of the variance in self-efficacy, F(5, 205) =9.89,
p<.001. Perseverance of effort
was a significant individual positive predictor for both value,
β=.34, t(210) =5.20, p<.001,
95 % CI [.23, .51] and self-efficacy, β=.37, t(210) =5.78,
p<.001, 95 % CI [.25, .52].
Together, these findings indicate that students who tended to
report that they sustained their
engagement and did not give up on achieving long-term goals
also reported, on average,
increased value for their schoolwork, and greater confidence
that they could successfully learn
the material. In contrast, consistency of interest failed to
emerge as significant individual

predictor for either aspect of motivation (see Table 2).
Predicting students’ use of strategies and procrastination Next,
we conducted a set of five
multiple regressions in which the two aspects of grit, the two
indicators of motivation, and the
same control variables were used to predict the four strategy
measures and procrastination.
These regressions were completed in two steps; with the grit
and control variables entered
together in a first block, followed by the two motivational
variables in a second block. As
presented in Table 3, the set of variables entered in the first
block explained a significant
amount of variance in cognitive strategies, R2=.21, F(5, 205)
=10.71, p<.001, metacognitive
strategies, R2=.26, F(5, 205) =14.21, p<.001, motivational
strategies, R2=.26, F(5, 205)
=14.61, p<.001, time and study environment management
strategies, R2=.35, F(5, 205)
=21.93, p<.001, and procrastination, R2=.36, F(5, 205) =22.57,
p<.001. In this first block,
perseverance of effort was the only significant predictor of
cognitive strategies, β=.43, t(210)
=6.77, p<.001, 95 % CI [.29, .53], metacognitive strategies,
β=.50, t(210) =7.98, p<.001,
95 % CI [.37, .61], and motivational strategies, β=.51, t(210)
=8.32, p<.001, 95 % CI [.41,
.67]. Both perseverance of effort and consistency of interest
predicted time and study envi-
ronment management strategies, β=.52, t(210) =8.89, p<.001, 95
% CI [.40, .63], β=.22,
t(210) =3.75, p<.001, 95 % CI [.13, .42], and procrastination,
β=−.42, t(210)=−7.38, p<.001,
95 % CI [−.68, −.39], β=−.32, t(210)=−5.47, p<.001, 95 % CI
[−.69, −.32].

Adding the two motivational variables in the second step
significantly increased the amount
of variance explained for cognitive strategies, ΔR2=.11, F(7,
203) =13.73, p<.001,
metacognitive strategies, ΔR2=.10, F(7, 203) =15.80, p<.001,
motivational strategies,
ΔR2=.12, F(7, 203) =18.29, p<.001, and time and study
environment management strategies,
ΔR2=.05, F(7, 203) =19.51, p<.001. The amount of variance
explained did not significantly
change for procrastination, ΔR2=.01, F(7, 203) =16.58, p=.255,
therefore, individual results
for the motivation predictors and this dependent variable are not
presented or discussed further.
In the second step, value was a positive predictor for cognitive
strategies, β=.20, t(210)
=2.76, p<.01, 95 % CI [.05, .30], metacognitive strategies,
β=.22, t(210) =3.18, p<.01, 95 %
CI [.08, .34], motivational strategies, β=.32, t(210) =4.68,
p<.001, 95 % CI [.18, .44], and
time and study environment management strategies, β=.26,
t(210) =3.79, p<.001, 95 % CI
[.11, .36]. Self-efficacy, in contrast, was a positive predictor
only for cognitive strategies,
β=.24, t(210) =3.22, p<.01, 95 % CI [.08, .35] and
metacognitive strategies, β=.17, t(210)
=2.41, p<.05, 95 % CI [.03, .30].
Table 2 Results of regression analyses predicting value and self-
efficacy
Predictor Value Self-efficacy
B SE B β B SE B β

Age .02 .01 .12 .02 .01 .15*
Female −.26 .16 −.11 −.14 .15 −.06
Prior achievement .03 .02 .09 .04 .02 .11
Perseverance of effort .37 .07 .34*** .39 .07 .37***
Consistency of interest −.01 .09 −.01 −.02 .09 −.02
R2 .16*** .19***
Note: N=211, * p<.05, *** p<.001
Even when accounting for the two motivational beliefs, the
pattern of relations for the two
aspects of grit remained the same (see Table 3). Perseverance of
effort was a positive
individual predictor for cognitive strategies, β=.28, t(210)
=4.26, p<.001, 95 % CI [.14,
.39]; metacognitive strategies, β=.36, t(210) =5.56, p<.001, 95
% CI [.23, .48]; motivational
Table 3 Results of regression analyses predicting strategies for
self-regulated learning and procrastination
Step 1 Step 2 Step 1 Step 2
Predictor B SE B β B SE B β B SE B β B SE B β
Cognitive strategies Metacognitive strategies
Age .00 .01 .01 −.01 .01 −.05 .00 .01 .01 −.01 .01 −.04

Female −.10 .14 −.05 −.02 .13 −.01 .10 .14 .04 .17 .13 .08
Prior achievement .02 .02 .07 .01 .02 .03 .02 .02 .05 .01 .02 .01
Perseverance of
effort
.41 .06 .43*** .26 .06 .28*** .49 .06 .50*** .35 .06 .36***
Consistency of
interest
.04 .08 .03 .04 .07 .04 .03 .08 .02 .03 .07 .03
Value – .18 .06 .20** – .21 .07 .22**
Self-efficacy – .22 .07 .24** – .17 .07 .17*
R2 .21*** .32*** .26*** .35***
ΔR2 .11 .09
Motivational strategies Time & study environment management
strategies
Age −.01 .01 −.07 −.02 .01 −.12* .00 .01 .00 .00 .01 −.03
Female −.09 .14 −.04 .01 .13 .00 .04 .13 .02 .10 .12 .05
Prior achievement .00 .02 .01 −.01 .02 −.03 .03 .02 .08 .02 .02
.05
Perseverance of
effort
.54 .07 .51*** .38 .07 .36*** .52 .06 .52*** .43 .06 .26***
Consistency of
interest

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