Selçuk iletiŞİM

Instant Messaging Use Among University Students (81-96)
91
Table 4 Hierarchical regression analysis about predictors of IM using frequency (motivations of
IM using, IM using behaviors, internet using behaviors, and demographic variables)
Model 1
Model 2
Time Spent on Instant
Messaging Daily
Time Spent on Each In-
stant Messaging Session
Predictors
β
r
β
r
Block 1 : Motivations of IM Using
Interpersonal Interaction and Utility
.081*
.252***
.010
.120**
Convenience
.047
.206***
.114*
.220***
Relaxation/Entertainment
.019
.304***
.075
.235***
Information Seeking
.049
.165***
.023
.090*
Adjusted R² (%)
.10
.06
F
15.9***
9.5***
Block 2: IM Using Behaviors
Time of the IM using
.015
.164***
.028
.077*
Classmates
a
-.162***
-.131**
-.113*
-.068
Ordinary friends
a
-.062
.036
-.137**
-.076*
Family members
a
-.065
-.105**
-.094*
-.044
Number of the session in one day
.150***
.371***
-.220***
-.075*
IM Services (MSN)
Number of saved friends in IM service
-.075*
-.078*
-.016
-.014
Number of saved friends in IM account
.086*
.224***
.140**
.199***
Adjusted R² (%)
.24
.11
F
15.6***
6.7***
Block 3: Internet Using Behaviors
At home
b
-.003
.126**
.014
.060
From school
b
.010
-.034
.004
-002
Home and school
b
-.051
.004
.039
.033
From dormitory
b
.011
.071
.026
.041
E-Mail and IM
c
-.042
.031
-.040
.046
Surf the web
c
-.029
-.054
-.079
-.062
Providing helping materials for homework
c
-.004
-.070
.014
-.021
For being my hobby
c
.070
.245***
.033
.089*
Time of the internet usage in one day
.463***
.583***
.325***
.317***
Adjusted R² (%)
.42
.20
F
19.6***
7.2***
Block 4: Demographic Variables
Amount of spending monthly
-.031
.097*
-.098*
-.074*
Second class
d
.091**
.126**
.068
.099*
Third class
d
.004
.053
-.092*
-.082*
Gender (male)
-.100**
-.082*
-.096*
-.110***
Adjusted R² (%)
.43
.21
F
19.3***
7.2***
Final Adjusted R² (%)
.44
.21
Final F
19.0***
7.1***

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Selçuk İletişim, 5, 3, 2008
92
a
Which groups do you chat more frequently thorough IM services? (Dummy coded)
b
Which place do you  mostly connect to internet? (Dummy coded)
c
Which purpose do you use internet? (Dummy coded)
d
Which class are you attending? (Dummy coded)
***
p< .001,
**
p< .01,
*
p< .05
The second group includes these variables:
“how long have you been using IM?”, “Which
groups do you chat more frequently thorough
IM services?” (this variable was transformed as
dummy variable), “how many sessions do you
connect in an ordinary day?”, “which IM ser-
vice do you use most frequently?” (this vari-
able were transformed as dummy variable),
“how many friends do you have in your IM
service list?
The third independent variables group includes
these variables: “from which place do you
mostly connect to the internet?” (this variable
was transformed as dummy variable), “for
which purpose do you use the internet?” (this
variable was transformed as dummy variable),
“how many hours (and minutes) do you use
internet in one day?”.
The last and fourth group includes demo-
graphic variables which were “which class are
you attending?”, “amount of spending
monthly”, and “gender of respondents”.
Table 4 showed that independent variables
which consisted of motivations of IM using
explained 10 percent (F = 15.9, p< .001) vari-
ance of IM using frequency. Results indicated
that the motivation of “interpersonal interaction
and utility” (β = .081, p< .05) was the only one
that significantly and positively contributed to
IM using frequency. This result indicated that
respondents who have this motivation more
frequently use IM. Also analysis revealed that
all motivations which were interpersonal inter-
action and utility (r = .25, p< .001), conven-
ience (r = .20, p< .001), relaxa-
tion/entertainment (r = .30, p< .001), and in-
formation seeking (r = .16, p< .001) positively
and significantly correlated with IM using
frequency. Results indicated that the highest
correlation with IM frequency was relaxa-
tion/entertainment while the lowest correlation
with IM frequency was information seeking.
The second independent variables group which
were the IM using behaviors explained (with
first group) 24 percent (F = 15.6, p< .001)
variance of IM using frequency. According to
results classmates (β = -.162, p< .001), and
MSN Messenger (β = -.075, p< .05) signifi-
cantly and negatively contributed to IM using
frequency. These results showed that respon-
dents who chatted mostly with girl/boy friend
more frequently use IM than those who chatted
mostly with classmates (because group of
girl/boy friend was taken as reference). This
result is very normal because students can talk
with classmates easily by face to face in class-
rooms. The other result is Yahoo Messenger
users more frequently use IM than MSN Mes-
senger users (because MSN was taken as refer-
ence). Also analysis indicated that classmates
(r = -.13, p< .01), family members (r = -.10, p<
.01), and MSN Messenger (r = -.07, p< .05)
significantly and negatively correlated with IM
frequency. On the other hand variables of
number of the session in one day (β = .150, p<
.001), and number of saved friends in IM ac-
count (β = .086, p< .05) positively and signifi-
cantly predicted IM frequency. Also these
variables positively and significantly correlated
with  IM  frequency  (r = .37, p< .001; r  =  .22,
p< .001). Additionally time of the IM using
positively and significantly correlated with IM
frequency (r = .16, p< .001). This result
showed  that  older  IM  users  use  more  fre-
quently IM than new IM users.
Table 4 showed that the third independent
variables group of internet using behaviors
explained (with first two groups) 42 percent (F
= 19.6, p< .001) variance of IM using fre-
quency. Results indicated that “time of the
internet usage in one day” (β = .463, p< .001)
was the only variable that significantly and
positively contributed to IM using frequency.
Also this predictor significantly and positively
correlated with IM frequency (r = .58, p<
.001). Correlation analysis revealed that vari-
ables  of  “at  home”  (r = .12, p< .01) and “for

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