Home | About IPJ | Editorial board | Ahead of print | Current Issue | Archives | Instructions | Contact us |   Login 
Industrial Psychiatry Journal
Search Articles   
    
Advanced search   
 

ORIGINAL ARTICLE
Year :   |  Volume :   |  Issue :   |  Page : Previous Article  Table of Contents   Next Article  

Development and validation of an instrument for the assessment of internet use in the Indian context


1 Department of Psychology, PSG College of Arts and Science, Coimbatore, Tamil Nadu, India
2 Department of Clinical Psychology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
3 Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
4 Department of Bio-Statistics, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

Date of Submission19-Jan-2021
Date of Acceptance20-May-2021
Date of Web Publication22-Sep-2022

Correspondence Address:
Thamilselvan Palanichamy,
Department of Psychology, PSG College of Arts and Science, Coimbatore - 641 014, Tamil Nadu
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ipj.ipj_14_21

   Abstract 


Background: Internet usage is increasing in the young population. Over 560 million internet users, India is the second-largest online market globally, which necessitates the development of an Internet use assessment tool in the Indian context. Methods: Samples of 560 individuals aged from 16 to 40 years participated. Data were coded in excel sheet for STATA 14.0 analysis evolved to item response theory. Cronbach's alpha was used to determine the internal consistency; concurrent validity was performed using the “Internet Addiction Test.” Descriptive statistics, analysis of variance, confirmatory factor analysis, and Pearson's correlation were also determined. Results: The developed instrument has the Cronbach's alpha reliability value (25 items) of 0.893, which indicates high internal consistency and has a concurrent value of 0.800. Factor analysis of 23 items revealed that the variance of 64.481 revealed all the items loaded in the rotated component matrix. The final 18 items got the item discrimination index was >1.0 with three-factor structure. Conclusion: The developed reliable and valid instrument can be used for identifying the patterns of internet usage across various settings (research, educational, mental health/clinical, and workplace).

Keywords: Assessment, internet usage, reliability, validity



How to cite this URL:
Palanichamy T, Sharma MK, Chandra PS, Kandavel T. Development and validation of an instrument for the assessment of internet use in the Indian context. Ind Psychiatry J [Epub ahead of print] [cited 2022 Nov 29]. Available from: https://www.industrialpsychiatry.org/preprintarticle.asp?id=356695




   Introduction Top


The Internet is a worldwide system of interconnected computer networks which has gone through various fast technological improvements. Fast technological improvements have increased the accessibility and use of Internet in all age groups tremendously since the past decade which has caused many individuals, especially adolescents to be affected by Internet addiction.[1] India had 560 million Internet subscribers in 2018, second only to China. Indians download more applications in 2018 than any country except China and spend more time on social media (an average of 17 h a week) than social media users in China and United States.[2]

The Internet, a term used to represent “Internetwork,” began in the 1950s and 1960s with computers development. The Internet has been used for academic, business, entertainment, and administrative purposes in the contemporary world. In 2015, around 26% of the Indian population has used the Internet which indicates an increase significantly compared with preceding years. In terms of gender, males (71%) were seen to have higher internet usage when compared to women (29%) in India.[3] There are 8.5% of Internet users in India. Mumbai with 3.24 million users holds first place and Delhi with 2.66 million users holds 2nd place in terms of the number of Internet users with the age group of 16 years to 45 years.[4] The Internet users are males, whereas in the United States, 87% of the teenagers' range between age group of 12 to 17 years use the Internet as compared to 73% in 2000 and 66% of Internet users in other Western and European countries.[5]

Internet overuse manifests as addiction when individuals display compulsive forms of behavior (along with craving and loss of control) for use of Internet, “its interference in his/her normal functioning and their relationships with family, friends, loved ones, social isolation, marital discord, academic failure, job termination, and excessive financial debt.”[6] Due to “excessive use of Internet,” disruption in marriages, financial and relationship difficulties (i.e.,: Sexual/romantic, parent–child, and friendships) were seen to occur subsequently.[7] Physical problems (cardiopulmonary-related deaths and other health-related issues) and game-related murder was reported about excessive internet use at the workplace.[8],[9],[10] Internet overuse is also related to several psychiatric conditions such as “depression (40%–43%), anxiety disorders (44%), obsessive-compulsive disorders (43%), and antisocial behaviors (45%).” 4.1% have an addiction to Internet in a survey carried out in Bangalore, and the need was felt to a development tool for the assessment of internet use in Indian Context, 24% with problematic internet use have psychiatric distress[11] and 5% have addictive use of social networking sites.[12] Amongst adolescents, addictive use was present for gaming (19.5%), Mobile Phone (15.5%), Face book use is higher in 13-15 years of age group.[13] Evidence is there for addictive use, so there is a need to assess the pattern of use in the Indian context. Based on this information, there is a need to evolve a strategy to screen Internet use and its problematic manifestations in the community and evolve the psychosocial management.

Several assessments have been established by various researchers from all over the world which includes “Internet Addiction Diagnostic Questionnaire;”[8] “Internet Addiction Test;”[7] Chen Internet Addiction Scale;”[14] “Compulsive Internet Use Scale;”[15] “Generalized Problematic Internet Use Scale;”[16] “Internet Addiction Proneness Scale;”[17] “The Problematic Internet Entertainment Use Scale;”[18] “Questionnaire on Internet-related experiences;”[19] “Compulsive Internet Use Scale (CIUS);”[15] “Problematic Internet use scale (PIU);”[16] “Excessive Internet Use Questionnaire;”[20] “Problematic Internet Use Questionnaire (PIUQ);”[21] “Chinese Internet Addiction Inventory (CIAI),”[22] and “International prevalence rates for Internet addiction.”[23]

Nevertheless, there are some limitations to existing tools. This includes “Chen Internet Addiction Scale” only validated in Greek and Chinese; “Compulsive Internet Use Scale” has no cut-off and does not consist of the assessment of tolerance.[15] “Internet Addiction Proneness Scale;” “DSM 52 Scale of Internet Use,” and “The Problematic Internet Use Questionnaire” consist of 20, 52, and 20 items, respectively; but not all items relevant for addiction classification;[17],[21],[24] “The Problematic Intent Entertainment Use Scale” has no cut off scores;[18] “Questionnaire on Internet Related Experiences,” “Compulsive Internet Use Scale,” and “Internet Consequence Scale” does not use recognized diagnostic criteria;[15],[19],[25] and “Assessment of Computer and Internet Addiction Scale” lacks time criterion.[26] These instruments or scales established in different samples have not been accepted extensively. All these tests developed in Western countries, though are not validated in India.

There is no empirically validated tool in the Indian context. Hence, it is vital to develop the instrument for internet use assessment in the Indian context to screen or prove the “usage patterns of Internet use.” In this context, this study attempted to “develop an instrument for assessing the usage of the Internet” in the Indian context.


   Methods Top


Aim

The study aimed to develop the instrument for the assessment of internet use in “Indian Context.”

Objectives

  • To develop the instrument and generate domains for the “instrument for the assessment of Internet use”
  • To establish the reliability and validity and other psychometric properties “instrument for the assessment of Internet use.”


Design and sampling

Five hundred and sixty subjects were recruited from the Schools, Colleges, IT/Software Companies, Government, and Private sectors based in Bangalore. The stratified random sampling method was used to the recruitment of subjects in the age group of “16 years to 40 years (16-20, 21-25, 26-30, 31-35, and 36-40).” The sample included both the male and females, having minimum education of 8th standard to comprehend the English to respond to the instrument and using Internet for the minimum period of one year. Participants with the presence of any health problem which interferes in taking the administered tools were excluded.

Item generation

The investigator developed the schedule of “semi-structured interview to elicit opinion about the items. 10 experts (Clinical Psychologists, Psychiatric Social workers, and Addiction related professionals) opinions were elicited in terms of content appropriateness and typological errors. After obtaining the expert opinion, six focus group discussion (FGD) was conducted to generate the instrument items. Each FGD's had Clinical Psychologists, Social Workers, Psychiatrists, Substance-related professionals, Software professionals, College students, Teachers, Homemakers, and other professionals like Nursing faculty, Physiotherapist, and Occupational therapist. 69 questions were given to 5 or 6 experts in a group of 6 FGD's to generate appropriate items for the instrument for the assessment of internet use and an administered semi-structured interviews to improvise the items.

Potential items were generated to assess the internet use by the investigator. For the instrument development, a total of 39 questions were developed and it was presented in a semi-structured format to elicit as much information as possible on various aspects of the place of accessing the internet, ranking the greatest to least watching sites, duration of watching internet activity, the usefulness of the internet, preferable time to use the internet, use of internet in academics, habits, number of attempts to try to stop, familial issues due to using of internet use, significances occur if not able to access Internet as well as using Internet and associated psychosocial problems. Initially, 147 items were generated through the analysis of the review of the literature.

The preliminary testing was done using the “Internet Addiction Test (Young, 1998)”. Secenty-five subjects were screened for the presence of mild to severe internet addiction using the Internet Addiction Test. They were taken from the community (i.e., college students, staff or professionals from the Government and Private sector and working in Cybercafé, those who are working on the internet for at least a minimum period of one year). Among these 75 subjects, 50 subjects met the criteria of mild (41) Moderate (09), and severe (0) internet addiction. The (n = 50) subjects answers were elicited for “socio-demographic data sheet, Semi-Structured Interview” (20 Questions) and the instrument for the assessment of Internet use items (24 items). Subsequently, the item refinement was done to modify the order of items, check the typological errors, and select two reversal items (I do not like to be alone whenever I use Internet and I feel good about myself due to my internet use). In the analysis of Semi--structured Interview and Instrument responses for the Assessment of Internet Use, the instrument items increased to 50 from 24.

Instrument validation

During this phase, expert rating solicited for content relevance, typological errors, instructional procedure, and addition and deletion of the items also taken into the account. Each item was rated with a rating scale from 0 to 10 “(0 = Most undesirable to 10 = Most desirable)”. Based on the Expert suggestions, “the items, which got the least rating”, were modified. Finally, 28 items were retained for Item Reduction Analysis.

The developed instrument for the Assessment of Internet Use (28 items) was administered on 100 subjects in the age range from16 years to 40 years. An attempt was made to select the representative sample taken from various age groups (16-20, 21-25, 26-30, 31-35, and 36-40). The data obtained from the community sample was exposed to the item analysis of the instrument items. It was examined for age and education group. Descriptive statistics, Cronbach's alpha, and factor analysis were done using SPSS. After analysis, subject matter expert rating was done to increase the effectiveness of the developed instrument.

Based on Factor Analysis, Frequencies, and Percentages, the lowest value of the items had been deleted. Finally, the instrument for the Internet use assessment comprised 18 items with 4-point rating scale (never, sometimes, occasionally, and always). Test-retest reliability was established on using 50 subjects (16-20, 21-25, 26-30, 31-35 and 36-40).

Data analysis

Data were coded in excel sheet for StataCorp. Stata Statistical Software: Release 14.[27] College Station, TX: StataCorpLP evolved to item response theory. “Descriptive statistics such as mean, standard deviation, frequency, percentages” were used to examine the “sociodemographic information.” The “correlation between Internet addiction test and developed instrument” for the assessment of Internet use analyzed using Pearson's correlation coefficient. The difference between sociodemographic groups regarding internet overuse instruments was analyzed using Analysis of variance for continuous variables; factor analysis, concurrent validity, and internal consistency reliability were established for the developed instrument.


   Results Top


Internet usage

In an initial assessment, the majority of the participants were in the 21 – 25 years (36%) of age group, male (76%), studied under graduation (54%), had 2 siblings (42%), born as a 1st child (40%), unmarried (76%), Hindu religion (70%), nuclear family (74%), professionals (72%), urban background (92%), the mother tongue of Kannada (34%), and an income of 21000 to 50000 (36%). The usage of the internet was high at home (50%) and the workplace (50%).

The Semi-Structured Interview Schedule findings revealed the majority of the subjects were using the internet by the smartphone (50%); using the internet for 12 h (20%); 30% were using the Internet for 3 h/day (32%); using Wi-Fi connection for 12 h (20%); using 12 h of paid connection for Internet (66%); using easy recharge (78%); 60% spent 51 rupees to 100 rupees minimum for using Internet and 34% spent 301 – 500 rupees maximum for Internet usage.

Majority of the subjects were using the internet for accessing entertainment programs (56%) followed by social networking sites (40%), entertainment (56%), personal use (42%), and academic-related sites (46%) and sometimes watched pornography/restricted sites (24%).

Thirty percent of subjects in the age group of 12-15 years using social networking sites (Emailing/Orkut/Facebook/Google talk); online gaming (36%); 16-20 years using search engines: Google/Yahoo/MSN (38%); 21-25 years using youtube (30%) and pornography/restricted sites (30%); 26-30 years using and Google Earth/Map (40%) use net news groups (38%), and Academic-related sites (e. g., library, online journal) (26%); 31 – 35 years using Internet forum (e.g. hobby, culture) (48%) and online gambling (6%); 36 – 40 using the personal site (e.g. blog) (56%) and online shopping (34%). Predominantly, subjects aged from 12 to 15 years were using Google Earth/Map.

The result shows that, majority of the subjects first preferences for “social networking sites” (surfing, chat, Facebook, Email, etc.) (64%), 2nd preference for “entertainment” (games, news, watching videos, etc.) (20%), 3rd preference for “personal usage” (online shopping/banking/blog, etc.) (14%) and not given priority for academic-related activities.

Among the participants, it also found that 60% of the respondents were using the Internet while talking through mobile phone; 70% of the respondents watching television while using Internet; 62% of the respondents using Internet during mealtime; 40% of the respondents checked mail soon after getting up; 72% of the respondents checked mail just before going to bed and 54% of the respondents checked mail both soon after getting up and just before going to bed.

Then results found that the amount of spent time in other daily activities which include family time (44%), relaxation (38%)/academic activities (36%) followed by socialization (22%). The participants experienced emotional distress when internet access was not available, which including anger, restlessness/irritability, loneliness, anxiety, sadness, impatience, boredom, happiness, and relaxed. The positive relationship was seen for internet use and health issues (headache/eye strain/disturbed sleep, etc).

Content validation

The present study followed Edward's methods,[28] and generated items were rated from the Edwards criteria for the item analysis. For evaluating the responses, the mean, standard deviation, and variance were performed. The study involved 15 experts for content validation and the experts were asked to see the appropriateness and typological errors. Content relevance and content representation were commonly evaluated by the subject matter expert ratings, which could be usually involved in content validation,[29] whereas it is linked to criterion-related validity.[30] The present study had taken the expert suggestions for all the 50 items. Then analyzed qualitatively for all the items in terms of repetitions/similar contents, modifications, and typological errors were taken into account. After solicitation expert review for 50 items using Edward's criteria, 28 items were retained.

Internal consistency and test-retest reliability

For the developed 28 instrument items, the Cronbach's Alpha value range from 0.863 to 0.881, which indicated good internal consistency, and the overall Cronbach's alpha value of 28 items, was 0.872. Three items were deleted due to the higher value than overall Cronbach's alpha. Hence, 25 items were retained. The 25 items had a Cronbach's alpha reliability value of 0.893.

Factor analysis was performed for 25 items; it led to the elimination of 2 items due to low factor loading. It led to the retention of 23 items. For the 23 items subject matter expert rating was conducted with the help of 10 experts. 10 experts (6 Professionals from the Centre for Addiction Medicine and 4 from Clinical Psychology) were asked to rank or rate the effectiveness using an advanced established scale range from 1 (least effective) to 4 (most effective) for 23 items. The average ratings across the expert's judgement were established. Two items were deleted due to get the rating of the lowest value than other items. Finally, 21 items were retained.

The developed 21 items with 4-point rating scale like Never, Sometimes, Often and Always (Based on the FGD, most of the test have 5-point scale to quitting neutralizing responses selected 4-point scale) was administered on 50 samples for test-re-test reliability. Retesting was carried out after one month. The correlation analysis was performed. The mean and standard deviation of the test was 21.32 ± 11.052 and the retest was 23.16 ± 10.839 with corresponding r value was 0.902**, significant at <0.01 level revealed that the developed instrument for the assessment of internet use had high test-retest reliability.

The [Table 1] showed most of the items were loaded in factor 1 except item numbers 14 (loaded in factor 2); item numbers 1 and 12 (loaded in factor 4). Hence, after eliminating the item number 1, 12, and 14, 18 items were retained.
Table 1: Factor loadings (pattern matrix) and unique variances of an instrument for the assessment of internet use (all the items)

Click here to view


[Table 2] shows the principal component factors in the factor analysis of 18 items, after eliminating the item numbers 1, 12 and 14. Three factors were retained, and the 3rd-factor value of 1.00499 with the cumulative percent of 0.5211 indicated adequate variance.
Table 2: Factor analysis with 18 items

Click here to view


[Table 3] shows the Principal Component Factors of the final 18 items. There were 3 factors retained, and Eigenvalues such as 3.92519 (factor 1), 3.05495 (factor 2) and 2.39933 (factor 3) with the cumulative percent of 0.5211. The Chi-square (153) value was 2392.41 at P < 0.001 probability.
Table 3: Principal component factors of final 18 items

Click here to view


[Table 4] displays the “Rotated Factor Loadings (Pattern matrix) and Unique Variances” of final 18 items. The item numbers 2, 3, 4, 5, 6, 9, 10, 15, 16, and 17 were loaded in the first factor; item numbers 6, 17, 18, 19, 20 and 21 in the second factor and 3, 7, 8, 11 and 13 were loaded in the third factor. The item numbers 6 and 17 were loaded in the first, and second factor and the item number 3 was loaded in the first and third factor.
Table 4: Factor loadings (pattern matrix) and unique variances of final 18 items

Click here to view


[Table 5] shows the Factor Rotation Matrix of the final 18 items. All three factors were “correlated with each other.”
Table 5: Factor rotation matrix of final 18 items

Click here to view


The “mean and standard deviation value” of 18.46 ± 11.345 for the instrument for the assessment for Internet use and 29.51 ± 19.632 for the Internet Addiction test, the correlation value was 0.800** with the P value of <0.001 level which indicated that the developed test has high concurrent validity [Table 6].
Table 6: Correlation between implicit association test and developed instrument for the assessment of Internet use (n=360) (final 18 items)

Click here to view



   Discussion Top


The present study evolved India's first instrument for the “Assessment of Internet use.” The questions were framed based on a literature search about internet use. Semi-structured interview was used to get depth information about the particular topic, which has been accomplished more flexibly by the researcher's probes.[31]

There were 6 FGDs conducted to generate the instrument items through 20 semi-structured questions. 147 items were generated in the first 3 FGD's, then 69 items were shortlisted in the 4th and 5th FGD's, subsequently, 24 appropriate items had been finalized in the 6th FGD. On analysis of the contents was based on the researcher's interactional experience with the focus group discussants.[32] The present study identified the three components, namely Internet usage patterns, Internet overuse/addiction, and dysfunctions or consequences of internet use.

Preliminary testing for Item generation was carried out with the 50 subjects, and they met the criteria of mild (82%), moderate (18%), and severe (0%) internet addiction (Administered Internet Addiction Test). After that, sociodemographic data, semi-structured interview (20 Questions), and the instrument for the assessment of internet use items (24 items) were administered. A recent study found that 70.5% were normal users, 23% were mild addicted, 6% were moderately addicted and 0.5% were severely addicted among adolescents.[33] Both the developed instrument for the assessment of internet use and internet addiction test, the range was the same, mostly similar standard deviation with adequate variance.

The demographic characteristics revealed that most of the subjects aged range from 21 to 25 years of age group had consistent with the statistical findings that most of the internet users were 18-29 years (98%).[3] Recent literature had a consistent finding that most internet users were males than females.[34],[35],[36],[37],[38] Postgraduation and belonged to urban background had more usage of Internet, which is corroborated with the findings of,[39] which showed 97% and 98%, respectively, since 2018. The data were collected from Bangalore, Karnataka so the majority of the samples' mother tongue was Kannada. The age, gender, income, and education were the key factors of Internet access.[40]

Majority of the instrument items had got the response of 'sometimes' in all the phases. According to statistical properties, any test items were reduced in terms of two ways such as (1) if the correlation value is <0.30 and (2) delete the items which show the higher Cronbach's alpha than overall Cronbach's alpha,[41] other items could be retained. Taking this as a 206 central point, the developed instrument for assessing Internet use, the items reduced based on Cronbach's Alpha. Out of 21 items, three items (1, 12, and 14) were deleted, having higher than Cronbach's alpha value. After deletion, all the retained items got the correlation value of above 0.5 with the high internal consistency (0.9130). According to the graded response model, previously deleted three items also had scored below 0.6 indicated poor item discrimination. All the items were independent with contributed single factor versus three factor and all the items were correlated.

The present study analyzing all the 21 items through factor analysis (principal component matrix), described 4 factors with the acceptable range of cumulative percent (0.5281) subsequently, the factor loadings (Pattern matrix) was done, revealed that most of the instrument items were loaded in a single factor except the item number 14 [loaded in factor 1), 1 and 12 (loaded in factor 2) which got already higher Cronbach's alpha than overall Cronbach's alpha as shown in [Table 3].

According to orthogonal varimax (Kaiser Off) rotation, 4 factors were retained, but there was no difference seen in the 4th factor. The pattern matrix revealed that the instrument items were not segregated equally in the distributing factors and got 4 factors with low, and negative values. Henceforth, the item numbers 1, 12, and 14 were not loaded in any of the factors and got low discrimination based on the graded response model, after the deletion of the 3 items, 18 items in the instrument were retained and used for exploratory factor analysis.

According to Orthogonal Varimax – Kaiser off Rotation, all the items were loaded only in the 1st factor. However, principal component factors revealed that, 18 items got 3 factorial structure (factor 1 = 3.92519, factor 2 = 3.05495, and factor 3 = 2.39933) with adequate variance (0.5211) and all the three factors were correlated each other.

On factor loadings (pattern matrix) revealed that 10 items were loaded in factor 1, 6 items were loaded in factor 2, and 5 factors were loaded in the 3rd factor and the item numbers 6 and 17 were loaded in the first and second factor and the item number 3 was loaded in the first and third factor.

For identifying concurrent validity, the correlation was performed for Internet addiction test and developed “instrument for the assessment of Internet use.” Based on the mean value both the tests have got normal or recreational use. The correlation value was 0.800 indicated high concurrent validity. On analysis of the literature revealed that, most of the Internet addiction related tests got multidimensional factor structures. Complexity of the factors assessed from these tests differs broadly from one[42] to seven factors[43] due to various reasons for Internet addiction's miscellaneous factor structures. Determining the correct factor structure of Internet addiction accomplishing a compromise definition which has been argued as a critical step, this would regulate the dimensions of the concept and generating the items and also various tests had different kinds of measurements comprised from “8-36 items seen in the literature that seems to be measuring the paradigm. There was a dispute that the factor analytic methods and choice used in evolving these tests have a direct effect on the structure attained and confirmatory factor analysis were used in some research studies to confirm the factor structures.”[44] There was an obvious discrepancy of several researches associated to the “factor structure and was not always a result of the dissimilar tests” used in contrast, different factor structures were used in some studies.[45]

The present study found 3-factor structures of the 18 items. It was corroborated with 3 factor structure (Withdrawal and Social problems, Time management and Performance and Reality Substitute) of Internet addiction test in other studies,[46],[47] whereas 6 factor structure in Internet-related problem scale includes “salience, negative effects, mood enhancement, productivity, loss of control and lack of information.”[48] Whereas, Italian version of the Internet addiction test reported 6 factor structure of the Internet addiction test.[49] There was a presence of inter-factor correlations implied the possibility of a unidimensional test and implied use of total score for assessing Internet addiction[50] even though few studies could not be found such correlations.[46],[50],[51] There was difficulty establishing the reliability and validity for Internet addiction-related tests, implied better assessment was a pre-condition for developing knowledge in Internet addiction.[52]

Finally, the present study found the 3-factor structure with the 18 items such as Craving/Loss of control/Withdrawal, (2) Consequences, and (3) Compulsion/Coping. The scoring method formed based on 4-point rating scale (never = 0, sometimes = 1, often = 2 and always = 4), overall score divided by three because there were 3 categories were evolved such as recreational use of Internet (0–18), excessive use of Internet (19–36), and dysfunctional use of Internet (37–54) [Table 7].
Table 7: Scoring for the instrument for the assessment of Internet use (final 18 items)

Click here to view



   Conclusion Top


The “instrument for the assessment of Internet use” in this study is brief, simple, and naturally valid for the Indian population (16–40 years). The instrument comprises 18 items with 3-factor structures. Sociodemographic factors and usage patterns of the Internet emerged as a significant determinant manipulating Internet usage, especially age, gender, and education. The instrument has satisfactory psychometric properties. This instrument can identify the patterns of Internet usage across various settings (research, educational, mental health/clinical, and workplace). The obtained scores can be used for psychosocial interventions for the “promotion of healthy use of technology.”

Acknowledgment

The author (Dr. Thamilselvan. P) would like to thank the University Grant Commission (UGC), New Delhi, India, for providing financial support to work as a Junior Research Fellow (JRF) at the Department of Clinical Psychology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India.

Financial support and sponsorship

University Grant Commission (UGC) fund.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Maheshwari SK, Preksha S. Internet addiction: A growing concern in India. Indian J Psychiatr Nurs 2018;15:61.  Back to cited text no. 1
    
2.
Madgakar A, Kshirsagar A, Gupta R, Manyika J, Bahi K, et al. Digital India: Technology to Transform a Connected Nation. McKinsey Global Institute; 2019.  Back to cited text no. 2
    
3.
Dixon S. Social Media – Statistics & Facts. (June 21, 2022). Social Research Media & User-Generated Content. Social Media usage worldwide, Statista; 2022.  Back to cited text no. 3
    
4.
Keelery S. Statista: Demographics & Use, Internet usage in India – statistics & facts. 2021. Availabler from: https://www.statista.com/topics/2157/internet-usage-in-india/#topicHeader__wrapper. [Last Accessed on 2021 Aug 02].  Back to cited text no. 4
    
5.
Lenhart A, Madden M, Macgill AR, Manager P, Smith A. Teens and Social Media Gains a Greater Foothold in Teen Life as they Embrace the Conversational Nature of Interactive Online Media. Pew Internet and American Life Project; 2007. p. 1-44. Available from: https://www.pewinternet.org/wp-content/uploads/sites/9/media/Files/Reports/2007/PIP_Teens_Social_Media_Final.pdf.pdf. [Last accessed on 2017 Jul 18].  Back to cited text no. 5
    
6.
Padwa H, Cunningham J. Addiction: A Reference Encyclopedia. ABC-CLIO; 2010.  Back to cited text no. 6
    
7.
Young KS. Internet addiction: The emergence of a new clinical disorder. Cyber Psychol Behav 1998;1:237-44.  Back to cited text no. 7
    
8.
Young K. Internet addiction: The emergence of a new clinical disorder. [Consult. em outubro, novembro e dezembro de 2010]. Cyber Psychol Behavior 2009;1:237-44.  Back to cited text no. 8
    
9.
Ong SH, Tan YR. Internet addiction in young people. Ann Acad Med Singapore 2014;43:378-82.  Back to cited text no. 9
    
10.
Ahn DH. Korean Policy on Treatment and Rehabilitation for Adolescents' Internet Addiction. In 2007 International Symposium on the Counseling and Treatment of Youth Internet Addiction. Vol. 49. Seoul, Korea: National Youth Commission; 2007.  Back to cited text no. 10
    
11.
Barthakur M, Sharma MK. Problematic internet use and mental health problems. Asian J Psychiatry 2012;5:279-80.  Back to cited text no. 11
    
12.
Sharma MK, Rao GN, Benegal V, Thennarasu K, Thomas D. Technology addiction survey: An emerging concern for raising awareness and promotion of healthy use of technology. Indian J Psychol Med 2017;39:495-9.  Back to cited text no. 12
[PUBMED]  [Full text]  
13.
Rajanna SH, Sharma MK, Palanichamy TS. Exploration of technology use pattern among teenagers and its relationship with psychological variables. ASEAN J Psychiatry 2016;17:1.  Back to cited text no. 13
    
14.
Chen SH, Weng LJ, Su YJ, Wu HM, Yang PF. Development of a Chinese internet addiction scale and its psychometric study. Chin J Psychol 2003;45:279-4.  Back to cited text no. 14
    
15.
Meerkerk GJ, Van Den Eijnden RJ, Vermulst AA, Garretsen HF. The compulsive internet use scale (CIUS): Some psychometric properties. Cyberpsychol Behav 2009;12:1-6.  Back to cited text no. 15
    
16.
Caplan SE. Theory and measurement of generalized problematic internet use: A two-step approach. Comput Hum Behav 2010;26:1089-97.  Back to cited text no. 16
    
17.
Kim DI, Chung YJ, Lee EA, Kim DM, Cho YM. Development of internet addiction proneness scale-short form (KS scale). Korea J Couns 2008;9:1703-22.  Back to cited text no. 17
    
18.
Lopez-Fernandez O, Freixa-Blanxart M, Honrubia-Serrano ML. The problematic internet entertainment use scale for adolescents: prevalence of problem internet use in Spanish high school students. Cyber Psychol Behav Soc Network 2013;16:108-18.  Back to cited text no. 18
    
19.
Fargues MB, Lusar AC, Jordania CG, Sánchez XC. Vaidation of two brief scales for Internet addiction and mobile phone problem use. Psicothema 2009;21:480-5.  Back to cited text no. 19
    
20.
Mythily S, Qiu S, Winslow M. Prevalence and correlates of excessive Internet use among youth in Singapore. Ann Acad Med Singapore 2008;37:9.  Back to cited text no. 20
    
21.
Thatcher A, Goolam S. Development and psychometric properties of the Problematic Internet Use Questionnaire. S Afr J Psychol 2005;35:793-809.  Back to cited text no. 21
    
22.
Huang Z, Wang M, Qian M, Zhong J, Tao R. Chinese internet addiction inventory: Developing a measure of problematic internet use for Chinese college students. Cyberpsychol Behav 2007;10:805-12.  Back to cited text no. 22
    
23.
Weinstein A, Lejoyeux M. Internet addiction or excessive internet use. Am J Drug Alcohol Abuse 2010;36:277-83.  Back to cited text no. 23
    
24.
Xu J, Shen LX, Yan CH, Hu H, Yang F, Wang L, et al. Personal characteristics related to the risk of adolescent internet addiction: A survey in Shanghai, China. BMC Public Health 2012;12:1106.  Back to cited text no. 24
    
25.
Beutel ME, Klein EM, Aufenanger S, Brähler E, Dreier M, Müller KW, et al. Procrastination, distress and life satisfaction across the age range – A German representative community study. PLoS One 2016;11:e0148054.  Back to cited text no. 25
    
26.
Wölfling K, Beutel ME, Müller KW. Construction of a standardized clinical interview to assess internet addiction: First findings regarding the usefulness of AICA-C. J Addict Res Ther 2012;6:003.  Back to cited text no. 26
    
27.
StataCorp. Stata Statistical Software: Release 14. College Station, TX: StataCorpLP.  Back to cited text no. 27
    
28.
Edwards JR. Multidimensional constructs in organizational behaviour research: An integrative analytical framework. Organ Res Methods 2001;4:144-92.  Back to cited text no. 28
    
29.
Stelly DJ, Goldstein HW, McPhail SM. Application of content validation methods to broader constructs. In: Alternative Validation Strategies: Developing New and Leveraging Existing Validity Evidence. Vol. 60. San Francisc, CA: Jossey-Bass; 2007. p. 252-316.  Back to cited text no. 29
    
30.
Seo J, MacEntee M, Brondani M. The use of subject matter experts in validating an oral health-related quality of life measure in Korean. Health Qual Life Outcomes 2015;13:138.  Back to cited text no. 30
    
31.
Rubin HJ, Rubin IS. Qualitative Interviewing. In: Oaks T, editor. Qualitative Interviewing: The Art of Hearing Data. 2nd ed. CA: Sage; 2005.  Back to cited text no. 31
    
32.
Creswell JW, Miller DL. Determining Validity in Qualitative Inquiry: Theory into Practice. Vol. 39. 2000. p. 124-30. Available from: https://doi.org/10.1207/s15430421tip3903_2. [Last accessed on 2010 Jun 24].  Back to cited text no. 32
    
33.
Kayastha B, Gurung A, Chawal R. A descriptive study to assess the level of internet addiction among adolescents: A case study of high schools in Mangalore. J Child Adolesc Behav 2018;6:378.  Back to cited text no. 33
    
34.
Cardak M. Psychological well-being and internet addiction among university students. Turk Online J Educ Technol 2013;12:134-41.  Back to cited text no. 34
    
35.
Lin MP, Ko HC, Wu JY. Prevalence and psychosocial risk factors associated with Internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol Behav Soc Network 2011;14:741-6.  Back to cited text no. 35
    
36.
Wu X, Chen X, Han J, Meng H, Luo J, Nydegger L, et al. Prevalence and factors of addictive Internet use among adolescents in Wuhan, China: Interactions of parental relationship with age and hyperactivity-impulsivity. PLoS One 2013;8:e61782.  Back to cited text no. 36
    
37.
Cao F, Su L. Internet addiction among Chinese adolescents: Prevalence and psychological features. Child Care Health Dev 2007;33:275-81.  Back to cited text no. 37
    
38.
Carli V, Durkee T, Wasserman D, Hadlaczky G, Despalins R, Kramarz E, et al. The association between pathological internet use and comorbid psychopathology: A systematic review. Psychopathology 2013;46:1-3.  Back to cited text no. 38
    
39.
Smith A, Anderson M. Social media use in 2018. Pew Res Cent 2018;1:1-4. Available from: https://www.pewinternet.org/2018/03/01/social-media-use-in-2018/. [Last accessed on 2018 Mar 01].  Back to cited text no. 39
    
40.
Zhou R, Fong PS, Tan P. Internet use and its impact on engagement in leisure activities in China. PloS One 2014;9:e89598.  Back to cited text no. 40
    
41.
Gliem AJ, Gliem RR. Calculating, Interpreting and Reporting Cronbach's Alpha Reliability Coefficient for Likert-Types Scales. Refereed Paper: 2003 Midwest Research to Practice Conference in Adult, Continuing, and Community Education. Available from: https://scholarworks.iupui.edu/bitstream/handle/1805/344/gliem+&+gliem.pdf?sequence=1. [Last accessed on 2017 Aug 28].  Back to cited text no. 41
    
42.
Siomos KE, Dafouli ED, Braimiotis DA, Mouzas OD, Angelopoulos NV. Internet addiction among Greek adolescent students. Cyber Psychol Behav 2008;11:653-7.  Back to cited text no. 42
    
43.
Caplan S, Williams D, Yee N. Problematic internet use and psychosocial well-being among MMO players. Comput Hum Behav 2009;25:1312-9.  Back to cited text no. 43
    
44.
van Prooijen JW, van der Kloot WA. Confirmatory analysis of exploratively obtained factor structures. Educ Psychol Meas 2001;61:777-92.  Back to cited text no. 44
    
45.
Young KS, Rodgers R. The relationship between depression and internet addiction. Cyberpsychol Behav 1998;1:25-8.  Back to cited text no. 45
    
46.
Chang MK, Man Law SP. Factor structure for Young's internet addiction test: A confirmatory study. Comput Hum Behav 2008;24:2597-619.  Back to cited text no. 46
    
47.
Pui S, Law M, Chang K. Factor Structure for the Internet Addiction Test: A Confirmatory Approach. Hong Kong: International DSI/Asia and Pacific DSI; 2007.  Back to cited text no. 47
    
48.
Widyanto L, Griffiths M. An empirical study of problematic internet use and self-esteem. Int J Cyber Behav Psychol Learn 2011;1:13-24.  Back to cited text no. 48
    
49.
Ferraro G, Caci B, D'Amico A, Blasi MD. Internet addiction disorder: An Italian study. Cyber Psychol Behav 2007;10:170-5.  Back to cited text no. 49
    
50.
Samaha AA, Fawaz M, El Yahfoufi N, Gebbawi M, Abdallah H, Baydoun SA, et al. Assessing the psychometric properties of the Internet Addiction Test (IAT) among Lebanese college students. Front Public Health 2018;6:365.  Back to cited text no. 50
    
51.
Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, Seo JS, et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: A questionnaire survey. Int J Nurs Stud 2006;43:185-92.  Back to cited text no. 51
    
52.
Widyanto L, Griffiths M. “Internet addiction”: A critical review. Int J Ment Health Addict 2006a;4:31-51.  Back to cited text no. 52
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]



 

Top
Previous Article   Next Article
 
  Search
 
  
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
    Abstract
   Introduction
   Methods
   Results
   Discussion
   Conclusion
    References
    Article Tables

 Article Access Statistics
    Viewed3378    
    PDF Downloaded23    

Recommend this journal