ENGL 1302 Article Analysis Worksheet

Instructions: Use this chart to help you map how each of your peer reviewed journal articles creates and disseminates knowledge
about your research topic to the scholarly community. Make sure to include proper citations with specific information that you quote
or paraphrase from your sources in each category row.
Article Background Exhibits Arguments Method / Theory
Citation What materials / sources are What materials are providing What sources / which scholars What materials / sources are
Last Name providing background specific examples / data in this are providing context for the providing methods of research
Last Name information / facts in this article? article? List those materials scholarly conversation / known to ground the argument or
and Last List those materials and provide and provide quoted evidence as researched conclusions / provide a theoretical lens for
Name quoted evidence as examples examples next to those debates in the field of study in the analysis / reasoning in this
Last Name, et next to those materials: materials: this article? List those sources article? List those materials
al. and provide quoted evidence as and provide quoted evidence as
examples next to those examples next to those
references: materials:
Abuhamdah Establish “smartphone Referred to students’ responses To justify the prevalence of “Cross-sectional online
and Nasser addiction” as a global concern, to questions that they created mental distress (88.7%) and survey study” using
particularly in college students, for their online survey: “62.9% severe psychological stress “convenience sampling” (2).
because of the prevalence of reported that ‘their mental (59.1%) according to K10
use: cite Demirci, et al.; abilities have been negatively scale, authors cite 5 studies Scales used (translated into
Al-Shobaili and Al-Yousefi; affected by the use of that highlight how shame Arabic):
Dikec and Kebapçı; smartphones,’ and 67.0% associated with poor mental Kessler Psychological Distress
Mosalanejad, et al.; Kumar, et al. reported that ‘using health explains the high Scale (K10) (2)
(2). smartphones has affected your incidence of “severe mental Smartphone addiction Scale
sleep and made it harder to fall and psychological disorders” (SAS) (2)
Establishes controversy over asleep’ …57.1% reported that in young adults and college
classifying excess phone Used “binary logistic
‘they feel that everything students in their area: Alslman
overuse as an addiction: using regression” to determine
requires effort and fatigue, and et al.; Naser et al.; Rayan and
Panova and Carbonell (2). potential causes of “severe
they do not want to do any Jaradat; Dalky, HF; Downs and
psychological distress and
activity that requires effort’” Eisenberg (5).
Define smartphone addiction smartphone addiction” (3)
(3)
and state its characteristics, but Argue that 56.7% of
note the lack of concrete Determined that data had
“88.7% showed mental participants were addicted to
standards to identify Gaussian distribution
disorder state to different their devices since
smartphone addiction: uses (“normally distributed”) by
degree of severity according to smartphones are necessary to
 ENGL 1302 Article Analysis Worksheet

De-Sola Gutiérrez, et al.; El their score on Kessler the lifestyle of many and a using a “histogram and
Keshky, et al.; and Elhai, et al. psychological distress scale source of entertainment and skewness and kurtosis
(2). score… 59.1% had a connection: cites 2 sources: measures” (3).
psychological distress score of Kaya et al. and Gundogmus et
Cites sources that demonstrate 30 and above” (3) al. (5). Recruited Jordanian university
the influence of smartphone use students to participate through
on physical, social, and mental SAS: “56.7% had a Defend their stance that social media apps (2).
health and academics: smartphone addiction score of females are more likely to
Dermirci, et al.; Choi, et al.; 30 and above” and “[t]he most express their psychological “Inclusion Criteria…:
Abdulmannan, et al.; Kim and commonly agreed upon distress symptoms in survey university students who
Kim; Nasr Esfahani, et al.; statement was that they are studies: uses Dachew et al. resided in Jordan, were at least
Horwood and Anglim; Kuss and using their smartphone longer 18 years old, and were enrolled
Griffiths. than they had intended Conclude that mental distress in any level of study” (2)
(64.3%)” (3-4) and mobile phone addiction
are both anticipated by the Exclusion Criteria: students
“87.2% female and 48.1% in same factors: Al-Barashdi, H. “under 18… or unable to read
their first year of study” (3) or comprehend Arabic”
Other Data Collected:
Demographics: “Age, gender,
marital status, study level,
faculty, and monthly income”
(2).
Created their own question set
regarding smartphone use,
sleeping habits, and motivation
(2).
Chen, et al. List the negative influence of SAS-SV: 29.8% of Argue that, contrary to other “Cross-sectional study” using
smartphone overuse on overall undergraduates were studies, smartphone simple random sampling that
mental, social, and physical smartphone-addicted, and addiction was not significantly observed students in Chinese
health and outlook on one’s prevalence of smartphone more prevalent in one gender medical university (2).
academics and profession: addiction was 1% higher in than the other: uses Kwon, et
Clayton, et al.; Cheever, et al.; male participants (3). al. to support and uses “Descriptive analysis…,
Cazzulino, et al.; 2 studies by Demirci, et al.; De-Sola univariate analysis by
 ENGL 1302 Article Analysis Worksheet

Demirci, et al.; Kim, J, et al.; “Significant factors Gutierrez, et al.; chi-square tests, and
Kwon, et al.; Block, JJ; Thomee, associated with smartphone Tavakolizadeh, et al.; and Lee multivariate analysis by
et al.; Mohammadbeigi, et al. addiction:” et al. to contrast (7). binary logistic regression” (3)
(1-2). Males: “smartphone games,”
sleep quality scores, and Found differences in reasons Scales used:
States commonly-assessed anxiety scores (3) for device use between males Smartphone Addiction Scale
characteristics of “smartphone Females: “multimedia… [and] and females: uses Roberts, et Short Version (SAS-SV) (3)
addiction” and compares them social networking [apps],” al. and De-Sola Gutierrez to Self-Rating Anxiety Scale in
to internet addiction: uses 2 sleep quality scores, and support (7). Chinese (4)
studies by Lin, et al.; Foerster, et anxiety and depression scale Center for Epidemiologic
al.; Kwon, et al.; Block, JJ (2) Argue that there is a Studies Depression Scale
scores (3)
correlation between (CES-D) (4)
Establish that college students 28.0% of male participants and smartphone overuse and Pittsburgh Sleep Quality Index
and young adults are at-risk 22.8% of female participants characteristics of “depression, (4)
groups in terms of phone were depression positive (4) anxiety, and poor sleep
addiction: uses Haverila, M; Tao, quality,” but the direction of Exclusion Criteria: “fail[ing] to
et al.; and Long, et al. 12.8% of male participants and the relationship is unclear and complete the questionnaires in
6.8% of female participants still debated: uses Kim, J, et its entirety”
were “anxiety positive” (4) al.; Demirci, et al.; Thomee, S.,
et al.; Lemola, et al.; van den Other Data Collected:
32.9% of male participants and Eihnden, et al.; and Yen, et al. Demographic: “gender, age,
38.0% of female participants (7) academic year, residence
were considered to have “poor (urban/rural), and monthly cost
sleep quality” (4) of living” (2)
Reasons for device use,
“51.7% [of participants] were self-estimated screen time,
female” (3) monthly cost of smartphone,
“age at which they obtained
their first smartphone…, [and]
frequency of replacement of
their smartphones” (2)
 ENGL 1302 Article Analysis Worksheet

Ercengiz, et Establishes the prevalence of “The direct effect between Argue that a developing a “Cross-sectional study” (11).
al. smartphone use in college differentiation of self and sense of self dependent on
students: use Mannion; Savci, et nomophobia was significant, smartphones results in “Attachment theory” used to
al.; and Yildirim (1). [but] the direct effect between smartphone anxiety: uses 2 suggest the relationship
emotion management skills studies from Walsh, et al.; between capacity for
Defines “nomophobia,” its and nomophobia was not” (8). King, et al.; Yildrim and independence and emotional
characteristics, and the “Intolerance of uncertainty… Correia; Bowen; Gilbert; and regulation and social
controversy surrounding its and differentiation of self… Smith (9-10) interaction and coping with
treatment and correlation with provided a statistically distress (5)
“smartphone addiction:” uses significant contribution to the Argues that anxious “Extended self theory”
King, et al.; Yildirim and model. However, the individuals do not cope with demonstrates that connections
Correia; Lin, et al.; Sahin, et al.; interaction did not contribute stress effectively, which makes between oneself and their
and Bragazzi and Del Puente (2). statistically to the model” that them prone to developing device increases their
evaluated whether these smartphone anxiety: uses susceptibility to smartphone
Notes that smartphone King, et al.; Demirci, et al.; anxiety (10)
variables encourage
dependence results in an Ghasempour and Uses “Uncertainty
smartphone anxiety (8).
interconnectedness between Mahmoodi-Aghdam; Cai, et orientation theory” to prove
self-concept and phone use: The correlation between the al.; Abbate-Dage, et al.; and that individuals who cannot
uses Smith; Mannetti, et al.; and inability to cope with Yuksel (10-11) cope with unpredictability are
2 studies from Walsh, et al. unpredictability and the more likely to develop
(2-3). ability to process and cope Argues that being able to smartphone anxiety (10)
with emotions “were regulate thoughts and feelings
Establishes that mentally ill was not important in
statistically significant” when Scales used:
individuals use phones to cope preventing nomophobia unless Differentiation of Self
tested for their individual
with their emotions when an individual has a weak
“moderating effect” on Inventory-Revised
stressed or faced with sense of self: uses King, et al;
smartphone anxiety (9). Emotions Management Skills
unpredictability: uses King, et Ghasempour and Scale
al.; Demirci, et al; Ghasempour Mahmoodi-Aghdam (11) Intolerance of Uncertainty
and Mahmoodi-Aghdam, et al.;
Scale
Davoudi, et al.; Mennin, et al.;
Nomophobia Questionnaire
Roemer and Orsillo; and Watson
and Greenberg (4) Used 2 mediation and 2
moderation analyses
 ENGL 1302 Article Analysis Worksheet

Inclusion Criteria: “used

smartphones for the past year”
(7)

Hashemi, et Establishes prominence of “Statistically significant Argues that feeling anxious “Descriptive-analytical
al. smartphone use and its negative meaning between faculty… and stressed is related to cross-sectional study” with
impacts on social and [and] average score of stress” symptoms of depression, combination of stratified and
psychological health: uses (3). which encourages more clustered random sampling
Elhai, et al.; Thomee, et al.; smartphone use: uses Oqbaee, (2).
Zhao, et al.; Bianchi and “There was a statistically et al.; Jun, S.; PsychCentral
Phillips; Repacholi, MH; significant meaning between (5-6) “One-way ANOVA test and
Sadeghian, E; Shaw and Black; faculty… and GPA… in terms independent t-test” to
Azuki; Billieux, et al.; of the average score of Claims that people who are evaluate the correlation of
anxiety” (5) more smartphone dependent variables (3).
Establishes that correlation have more anxiety: uses Hawi “Pearson’s correlation
between poor social health and “The results also showed that and Samaha, et al. (7) coefficient test… evaluating
smartphone use: uses Azuki, T; there was a statistically the linear relationship” of
Billieux, et al.; Wash, et al.; significant meaning between Argues that characteristics variables (3)
Hawi and Samaha; and Sapacz, faculty… in terms of the observed in the DASS-21
et al. (2) average score of depression” make smartphone Scales used:
(5). dependence more likely: uses Depression Anxiety and Stress
Tas; Kil, et al.; Volunis, et al.; Scale-21 (DASS-21) (3)
“Pearson correlation and Zanjani, et al. (7) Cell-Phone Overuse Scale
coefficient analysis:” (COS) (3)
“Strong correlation… between Other Data Collected:
[COS] score and Stress score” “Gender, age, marital status,
(5) faculty, major, GPA…”(3)

“By increasing in every 25

units of cell phone overuse
score, the student’s stress
score increases about 1
unit…[B]y increasing in each
 ENGL 1302 Article Analysis Worksheet

unit of cell phone overuse

score, the student’s anxiety
score increases by about 0.031
units” (5).
“72.2% of [participants] were
exclusively male” (3).

Kuru and Establish that there is a strong Correlation analysis: Argue that characteristics of “Cross-sectional study” using
Çelenk relationship between increases in “A positive correlation was mental rigidity make students “Convenience sampling”
phone use and poor sleep and found between [symptoms of more susceptible to developing (160, 163).
symptoms of emotional depression] and [anxiety an addiction: goes outside of
distress: uses Thomee, et al.; symptoms]” (161). smartphone addiction and Scales used:
Bickham, et al.; Demirci, et al; “...the path from [anxiety mentions studies of other Acceptance and Action
Elhai, et al. (160) scores] to [mental rigidity addictions: uses Brem, et al.; Questionnaire-II (AAQ-II)
score] was positive and Garcia-Oliva, et al.; Chou, et (160)
Establish that mental rigidity is statistically significant…, and al.; Crosby and Twohig; Beck Depression Inventory
correlated with psychological [t]he total effect [Anxiety] had Gifford, et al.; Amount, et al.; (BDI) (160)
distress: uses Masuda, et al.; on [smartphone addiction] and Hayes, et al. (162) Beck Anxiety Inventory (BAI)
Ruiz; Ruiz, et al.; and Kato, T was positive and significant” (160)
(161). Argues that mental rigidity Smartphone Addiction Scale
more deeply affects Short-Version (SAS-SV) (160)
Bootstrapping Analysis: depression than anxiety
“Indicated a significant because of rumination (not 2 mediation analyses:
[indirect effect] to exist from evaluated): uses Eisma, et al. One observing impact of
[anxiety scores] on “psychological inflexibility”
Claims that mental rigidity on the correlation between
[smartphone addiction
can elucidates poor depression and phone
scores] through [psychological
psychological health: uses addiction severity, and another
inflexibility]” (161).
Spinhoven, et al. and Masuda observing how “PI” influences
63.6% of participants were and Tully (162) the correlation between anxiety
female (161) symptoms and smartphone
addiction severity (160)
 ENGL 1302 Article Analysis Worksheet

Bootstrap analysis used to

verify the model created to
discuss “PI” (161)

Liu, et al. Establishes excessive phone use FMI scores varied between the Argues that encouraging Aim: Assess whether a 30
as a global issue, while control and experimental students’ conscious awareness minute implementation of a
highlighting the pervasiveness of groups prior to their 30-minute reduces their boundless phone form of cognitive-behavioral
smartphone access in Chinese audio-listening sessions (6) usage: uses Hayes, A.F.; therapy directly or indirectly
university students: uses Ding Zsadanyi, et al.; Birtwell, et al. reduces excessive cell phone
and Li; Davey and Davey; Sohn, “Further simple effect analysis (9-10) use (3)
et al.; and Smetaniuk, P (2). indicated that the problematic
smartphone use level of Argues that short behavioral Design: “2x2 Mixed Factorial”
Establishes the physical, mindfulness group was therapy increases one’s ability (5)
mental, and social health risks significantly lower in to limit impulsivity: uses
that accompany smartphone post-intervention, as compared Anderson, et al.; Friese, et al.; Statistical Analysis: “Bivariate
overuse: uses Seo, et al.; Park, et to that of pre-intervention”: the Verhaeghen, P; Twohig and correlation,” “Two-way
al.; and Kim, et al. (2) means of students phone Levin (9) repeated-measures analyses of
dependence was lower after variance” (5-6)
Establishes how increasing CBT (7) Argues that self-discipline
university students’ acceptance explains the inverse Controlled experiment (5)
and awareness of their “Further simple effect analysis relationship between (control/experimental groups);
environment and feelings may indicated that the self-control awareness and phone usage: includes pre- and post-tests
reduce smartphone use: uses De level of mindfulness group was uses Diamond; 2 studies from
Scales Used (in Chinese):
la Fuente-Anuncibay, et al.; significantly higher in Brand, et al; and Verhaeghen
Freiburg Mindfulness
Khoury, et al.; Janssen, et al; post-intervention, as compared (10).
Inventory (FMI) (4)
Elhai, et al.; Yang, et al.; and to that of pre-intervention”: the
Mobile Phone Addiction
Lan, et al. (2). means of participants’
Tendency Scale (MPATS) (4)
awareness post-CBT was
Self-control Scale (SCS) (4-5)
 ENGL 1302 Article Analysis Worksheet

greater than their pre-CBT

Establishes Hofmann’s mean (7) Theories used:
self-control model and its Hofmann’s Self Control Model,
relation to the study: uses “SCS change had a significant which indicates that self
Duckworth and Kern; Kim and predictive effect on restraint works against
Kang; and Hofmann, et al. (3) post-intervention MPATS”: intrusive inclinations; used to
Higher differences between infer that increasing
initial self-control scores and mindfulness will increase
SCS scores after experiencing “self-control,” which will
“mindfulness training” or reduce phone dependence (3)
listening to a news story
influenced phone dependence Other Data Collected:
scores after implementation of “Age, gender, meditation
CBT or listening to news experience” (5)
stories (7).

Mohamed, et Establishes the characteristics “22.78% reported mild social Argue that social anxiety is Aim: Discover the
al. and negative effects associated phobia…, 21.85% had common among college pervasiveness of, and potential
with social anxiety: uses Mäki moderate social phobia…, students, but also link between, social anxiety
(1) students with severe social acknowledges research that and phone dependence in
phobia… constituted 16.3%, does not support their results: female university students (7)
Establish that people can and those with very severe uses Ragheb, et al; Rabie, et
develop addictions to their social phobia… constituted al.; Elhadad, et al.; Al-Hazmi, “Cross-sectional study” (2)
technology and phones, and lists 10.93% of the sample” (4). et al.; Alkhalifah, et al.; and
their characteristics: uses Inclusion Criteria: Female
Cahyaning, Suryaningrum to
Cheever, et al. and Griffiths (1) “Using Pearson correlation to university student within the
support (7)
assess correlation between ages of 18-25 who has a
Indicates that the behaviors of social phobia and smartphone Between both fields of study, personal smartphone from
individuals who experience addiction, there is:” students in fields that require various areas of study (2)
 ENGL 1302 Article Analysis Worksheet

social anxiety and other A strong, positive correlation hands-on training experienced
psychological issues may in “academic studying group lower levels of phone Exclusion Criteria: has a
encourage smartphone (r=0.609)” and a moderate, addiction and social anxiety mental health diagnosis
addiction: uses 2 studies from positive correlation “in the than students in fields that do besides social phobia (2)
Elhai, et al.; Lee and Stapinski; practical group (r=0.559), with not include hands-on training:
Statistical analysis:
Leyfer, et al.; Bernroider and statistically significant uses El-Sayed Desouky and
“T-test,” “chi square test,”
Wong (1-2) difference between social Abu-Zaid and Patel and Puri to
“ANOVA,” and “Pearson’s
phobia and smartphone support and Okasha to contrast
Establishes the damaging correlation analysis” (2)
addiction scores in both groups (5, 8).
physical, mental, and social (P < 0.001 for each)” (6). Scales used (in Arabic):
health effects of excessive “[A] moderate positive relation Argues that the characteristics
Smartphone Addiction Scale
phone use and phone addiction: between social phobia and of social anxiety may make
(3)
uses Ghosh; Olsen, et al.; Yang, smartphone addiction them more susceptible to
Social Phobia Inventory (3)
et al.; Lee, et al; Soni, et al.; (r = 0.590) with a statistically becoming dependent on their
Alhassan, et al. (2, 7) significant difference smartphone: uses El-Sayed Other Data Collected:
(P < 0.001)” (6) Desouky D and Abu-Zaid; Structured Clinical Interview
Buyukbayraktar; Çelik and for DSM-IV axis disorder I to
Konan; Turgeman, et al; assess whether participants
Inokentii and Korniienko; have a mental disorder besides
Weinstein, et al.; Wolniewicz, social anxiety (3)
et al.; Enez, et al.; Brand, et al.;
Elhai, et al. (8).

Squires, et al. Defines “problematic “[S]ignificant positive Argues that, as a person Aim: broaden the study of
smartphone use” and emphasizes associations were identified exhibits more characteristics of “emotion regulation” and
the negative physical, mental, between psychological distress depression, stress, and anxiety, hypothesize its relation to
and social health effects as well and problematic smartphone the more likely they are to use characteristics of
as the academic ramifications of use, c = .110, t (192) = 4.24, p their phone in excess: uses 2 psychological distress and
PSU: uses Kwon, et al.; Elhai, et < .001; between psychological
 ENGL 1302 Article Analysis Worksheet

al.; Vahedi and Saiphoo; Bian distress and emotion studies by Elhai, et al. and “problematic smartphone
and Leung; Mahapatra; Darcin, dysregulation, a = .411, t (192) Vahedi and Saiphoo (1292). use” (1289)
et al.; Herrero, et al.; Demirci, et = 15.50, p < .001; and between
al.; Thomee, et al.; Samaha and emotion dysregulation and Argues that, as a person Study Type: cross-sectional
Hawi ; Lepp, et al.; Clayton, et problematic smartphone use exhibits more characteristics of (1295)
al; Paek; AlAbdulwahab, et al.; after controlling for depression, stress, and anxiety,
they will have less control over Statistical Analysis:
Panova and Carbonell; and Petry, psychological distress, b =
how they handle their Bivariate correlation analysis
et al. (1285) .230, t (192) = 3.30, p = .001.”
emotions: uses Gross; Abdi (1291)
(1291).
Establishes the controversy and Pak; Bardeen, et al.; Bootstrapped mediation
surrounding whether to call “[A] bootstrapped mediation Hallion, et al.; Kirwan, et al.; analysis (1291)
smartphone overuse and analysis with 5000 iterations Manuel and Wade; Mazaheri;
Scales used:
dependence and “addiction:” revealed that the correlation Pepping, et al. (1292)
Depression Anxiety and Stress
uses Aljomaa, et al.; Bian and between psychological distress
Argues that those who have Scale (DASS-21) (1290)
Leung; Chiu; Darcin, et al.; and problematic smartphone
Demirci, et al.; Haug, et al.; use was atemporally mediated less control over their emotions Difficulties in Emotion
will use their phones Regulation Scale-18
Herrero, et al.; Körmendi, et al.; by emotion dysregulation
excessively: uses Yildiz, et al. (DERS-18) (1290)
Liang and Leung; Lin, et al.; when gender and age were
and 4 studies by Elhai, et al. Smartphone Addiction
Mahapatra; Noë, et al.; Samaha included as covariates” (1291).
(1292-3) Scale-Short Version (SAS-SV)
and Hawi; Griffiths; Petry, et al.;
(1290-1)
Sussman, et al. American
Argues that increases in
Psychological Association; Models/Theories used:
characteristics of depression,
World Health Organization; Griffiths Addiction Model and
stress, and anxiety result in a
Kardefelt-Winther et al.; Panova Billieux Impulse Pathway;
lower ability to regulate one’s
and Carbonel (1285-6) Brand’s Person-Affect-
emotions, which increases
smartphone dependence: uses Cognition-Execution model;
Establishes the definition of
Billieux; Kardefelt-Winther; Kardefelt-Winther’s
“emotion dysregulation” and the
implications it is associated with: and Brand
 ENGL 1302 Article Analysis Worksheet

uses Aldao, et al; (Gratz and compensatory internet use

Roemer; Gross; Carpenter and theory (CIUT) (1287, 1293-4)
Timothy; Griffiths; and Billieux
(1287)

Discusses the inconclusive

results of studies that have
analyzed participants’ ability to
regulate their emotions: uses
Yildiz, et al.; Philips and Power;
Gul, et al; Firat, et al; Gratz and
Roemer; Gross and John; cites
multiple studies by Elhai, et al.
(1288)

Yadav, et al. Establishes differences between “80% were using their Argues that there is Aim: evaluate medical
mobile phones and smartphones for more than 2 prominence of smartphone students’ smartphone
smartphones, as well as the years, while 60% were having addiction-positive college dependence and analyze
widespread nature of daily usage of more than 3 students: uses Gupta, et al.; patterns in their reasons for
smartphones: uses Zheng and Ni; hours” (37). Kanmani, et al. (38) use (37)
Balakrishnan and Raj; and Daily
Mail “67% are inclined to use [their Found that amount of time Study type: cross-sectional
phones] without any reason spent on smart device did not
Establishes the negative mental, and 48% get stressed in its increase or decrease risk of Inclusion criteria: university
social, and physical health absence; however, 39% addiction: uses Daily Mail; students with access to a
effects and academic confessed that they are not able Bartwal and Nath; and personal smartphone (37)
ramifications that result from to reduce its usage even after Dasgupta, et al. to contrast
Scale used:
phone overuse: uses Mali, D; lots of efforts” (37-8). these results (38)
Nomophobia Questionnaire
Khan, MM; Walsh, et al.;
(NMP-Q) (37)
 ENGL 1302 Article Analysis Worksheet

Jamson, et al.; Ebesu, et al.; Collected data regarding how

Ling, R.; Baron, et al. “63% had constant desire to often and when students use
Auckerman, W; Massimini, M, use smartphone and 54% got their smartphones, apps used,
and M. Peterson; and Brown, et scared when phone gets and their interactions with their
al. (37-8) discharged and 60% gets device and its apps (37)
annoyed when not able to use
smartphone” (38)

Medical students use their

phone most when they are
bored…(93%), alone (89%),
and traveling (78%) (37)

Demonstrates data collected

from NMP-Q and phone use
questions through multiple
charts and graphs (40-1)
Aim: establish whether a
Yang, et al. Establishes how widespread “[P]erceived social support Argues that those with weaker correlation exists between
mobile phones are and notes the significantly and negatively support systems are more Study Type: cross-sectional
pros and cons associated with predicted college students’ susceptible to smartphone (10)
them: uses Lepp, et al.; You, et mobile phone addiction (β = dependence: uses Zhao, et al. Model created: “moderating
al.; and Ding, et al. (1) −0.17, t = −5.19, p < 0.001)” (8). mediation model” (5)
Statistical Analysis:
Establishes the ongoing debate “After including depressive Argues that the assumed
“Common Method Bias Test,”
regarding whether scholars in the symptoms as a mediator strength of one’s support
“Moderated Mediation Model
field should consider the variable, perceived social systems is inversely correlated
Test,” and “bootstrap test” (5)
characteristics associated with support significantly with indications of depression;
Scales used:
excessive technology use, such negatively predicted as symptoms of depression
as smartphones, should be depressive symptoms (β = increase, so does smartphone
 ENGL 1302 Article Analysis Worksheet

considered a result of an −0.46, t = −15.44, p < 0.001), dependence: uses Wu, et al.; Perceived Social Support Scale
addiction to one’s phone: uses and depressive symptoms Wei, et al.; Elhai, et al.; (4)
Yanya and Xavier; Lin, et al.; significantly positively Agnew, R; Tang, et al.; Kim, Mobile Phone Addiction Index
Salehan and Negahban; Lapierre, predicted mobile phone J.H.; Jun, S.; and Mitchell and (MPAI) (4)
et al.; Goswami and Singh (1-2) addiction (β = 0.45, t = 12.72, Phillips (8) Depression Anxiety Stress
p < 0.001)” (5-6). Scale (DASS-21) (5)
Establishes the prevalence of Justifies their finding that Self-Compassion Scale-Short
excessive phone use in “[W]hen self-compassion was self-compassion is relevant to Form (SCS-SF) (5)
university students and notes that added as a moderator, the whether one’s support system Theories Used:
this behavior puts them at risk interaction terms of perceived leads to depression, and, thus, “Cognitive-behavioral theory”
for worsened physical and social support and phone addiction: uses Muris, et (3)
mental health and poor self-compassion significantly al; Tang, et al.; Cohen and “Compensatory internet use
scholastic success: uses positively predicted depressive McKay (9) theory (CIUT)” (8)
Lapierre, et al.; Peng, et al.; and symptoms (β = 0.08, t = 3.25, “compensatory theory” (8)
Samaha and Hawi ( p < 0.01)” (6). “General strain theory” (8)
“Stress buffer hypothesis” (9)
“Simple slope analysis
revealed that perceived social
support significantly
negatively predicted
depressive symptoms when
self-compassion was low (M −
1SD) (simple slope = −0.42, t
= −10.85, p < 0.001), while
when self-compassion was
high (M + 1SD), the
association between perceived
social support and depressive
symptoms was attenuated
ENGL 1302 Article Analysis Worksheet

(simple slope = −0.26, t =

−6.72, p < 0.001),” (6)

“Simple slope analysis

revealed that perceived social
support was significantly
positively correlated with
mobile phone addiction when
self-compassion was low (M −
1SD) (simple slope = 0.17, t =
3.49, p < 0.01). In contrast,
when self-compassion was
high (M + 1SD), this
association became
nonsignificant (simple slope =
−0.01, t = −0.27, p = 0.79)”
(6).