Digital Identity Scale: A Validity and Reliability Study

photo: Kristína Kontínová Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 Sevgi Kavut Digital Identity Scale: A Validity and Reliability Study DOI: https://doi.org/10.34135/mlar-24-01-10 ABSTRACT The purpose of this study is to develop a reliable and valid scale for measuring digital identity definitions, digital personalities, self-presentation strategies, communication styles in digital contexts, and digital identity perceptions of digital environment users over the age of 18. This study is significant since it is the first scale study on the subject of digital identity in Turkish literature, filling a gap in the field and creating a unique, reliable and valid scale. The process of developing this scale consists of many phases. First, in-depth interviews were conducted with 18 people and an item pool was created by reviewing literature and data obtained. Item numbers were reduced according to the view of experts. Expert opinions were obtained using the Lawshe method. Afterwards, the pilot study was applied to 278 participants for construct validity. Following this, the main study was conducted with 511 participants. After the data was collected, exploratory and confirmatory factor analyses were carried out. The scale’s validity and reliability was tested and approved. The result of exploratory and confirmatory factor analysis was a scale with 28 items and 3 factors. These findings are deemed important so that this scale can be considered a reliable and valid measurement tool. KEY WORDS Digital Identity. Digital Identity Scale. New Communication Technologies. Reliability. Scale Development. Validity. Studies page 174 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 1 Introduction Digital identity is a relatively new yet growingly significant topic. The implications of digital identities on people’s lives, behaviour, and how they present themselves in digital contexts with impression management and personality traits are predicted to remain unexplored until a measurement method is established. Thus, the purpose of this study is to develop a unique, reliable and valid scale for measuring digital identity definitions, digital personalities, selfpresentation strategies, communication styles in digital contexts, and digital identity perceptions of digital environment users over the age of 18 in Turkey. This study is significant since it is the first scale study on the subject of digital identity in Turkish literature, filling a gap in the field and creating a unique and legitimate scale. In the meantime, through individual digital identity accounts and usages, this study depicts digital identity definitions, digital personalities, self-presentation strategies, communication styles in digital contexts, and digital identity perceptions of users over the age of 18. The main problem in developing this scale is the deficiency of measurement tools in Turkish literature and the inadequacy of current digital identity scales in international literature. Thus, this study has targeted developing a valid and reliable measurement tool which reveals digital identity and sub-dimensions of digital identity. Examining articles, theses, dissertations, scales, and other research in the national and international literature reveals that studies on digital identity are undertaken on a restricted and homogeneous population, such as university students, younger people, and members of specific occupational groups. The existing scales have a weak correlation to reveal digital identity definitions, digital identity presentations and digital identity perspectives of digital users. Thus, this study has targeted various sociodemographic groups, including those with digital environment users over the age of 18, based on factors including age, income, gender, and education. It is believed that this scale will address a need in the field and advance the fields of sociology, psychology, and communication in particular. It is noted that there is not much research on the topic of digital identity. Nonetheless, it is acknowledged that this subject is significant and has progressively become vital from a national and worldwide perspective. This study aims to contribute to new studies in digital identity, digital presentation, digital personality, digital reputation, digital impression, digital communication topics. 2 Literature Review The nature of human communication and the internet, which contains an incredibly large amount of data at a profoundly varying rate, both allow for greatly reduced communication costs and simultaneously incredible distances, as well as coverage of all other media content (Reed, 2019). Every major advancement in technology alters humans. The emergence of some aspects of the modern digital world predates the era of smartphones and personal computers. However, the measurement and rate of change in our lives have unexpectedly grown due to accessibility, the usage of mobile phones, and the rapid growth of internet advances (Lemma, 2017). Digital identities facilitate individuals’ ability to exhibit themselves to various audiences and to acquire distinct personas across various social media channels. Within the virtual digital environment of cyberspace, these identities’ distinct artificiality is fully present. Because of this special characteristic, the relationship between digital IDs and their corresponding offline IDs is unclear (Brown, 2016). Digital identity is defined by Sullivan and Stalla-Bourdillon as “it is an identity constituted from saved information and transmitted on digital forms” (2015, p. 268). Digital identities can be also described as a composition of technology and identity, individuals who present identity in the digital world. Everywhere people go, their identities are marked Studies page 175 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 with information about them. These marks may be classified as information on social media, name, phone number, creating a full profile covered by the image, commenting on a forum, filling out a form, and maintaining a blog (Ayed & Ghernaouti-Helie, 2011). At the same time, digital identity is a concept that is appropriate to the digital world. This trend has a link that raises privacy concerns about people’s real-world identities and their identities in the virtual world, as people engage in more activities in the virtual world (Pato, 2003). Digital identity is defined by Phiri et al. (2011) as the formation of personality traits, attitudes, and preferences of individuals who can receive personalized services that can exist online on mobile devices, in workplaces, and in many other areas. A digital identity is a social identity that is formed online through the creation of online profiles. On networking sites, self-offering is displayed in a social setting. Social media profiles on the internet are crucial for identity authorization (Bozkurt & Tu, 2016). Digital identities are now an important part of the identity management system due to the growth of the internet and the expansion of online services. A presentation of all available user data in online contexts is called a digital ID (Phiri et al., 2011). According to Rodrigues, a digital identity is one that has been formed by relationships, computer technology, and digital interactions like the Internet and digital media. Account names, domain names, artificial intelligence, biometric data, digital certificates, digital pictures, digital/electronic signatures, e-portfolios, geotagging, globally unique identifiers, ID cards or symbols, mobile IDs, passwords, personal data, IP addresses, personal profiles, smart cards, reputation, etc. can all be sampled, according to Rodrigues, who stated that there are various ways in which digital IDs can be viewed (Rodrigues, 2011). Nowadays, identities may be constituted as analogue (document), digitized (scanning) or digital (digital footprint) (Kavut, 2021a). Furthermore, as seen in the figure 1, Kavut (2021a), digital identity modules are classified into three stages as digital identity, digital impressions and digital mark or digital footprint. We can say that personal digital identities constitute digital impression via social media platforms and this process creates digital marks. FIGURE 1: Digital identity module Source: Kavut (2021a, p. 33) Goffman’s writings help to develop theories for comprehending the connection between identity and social networks. The dramaturgical approach of Goffman offers an appropriate framework for the development of identity. Proposed individualism, self-narratives, and cultural practices of connectedness are more generally positioned in parallel with Goffman’s work on social networks. Goffman observed the unique components of performance as well as Studies page 176 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 deliberate self-presentation in various circumstances (Cover, 2016). Goffman clarified that daily existence is a type of performance and used a theatrical metaphor to describe people’s attitudes and behaviour (Wood & Smith, 2005). The historical roots of the concepts of personality and personality traits are based on Trendelenberg, known for his work between 1870 and 1910 (Paranjpe, 2002). Although the concepts of person and personality are used synonymously, they have different meanings. The concept of the person suggests individual differences in personality while expressing people’s rights and duties (Paranjpe, 2002). Digital identities are a fairly new concept derived from the applications of individuals developed in online environments. The digital identities of individuals are in pieces and therefore consist of a combination of several services or networks (Costa & Torres, 2011). Digital identity creation can provide young people with the opportunity to showcase themselves, if utilized with caution. Finding an identity that governs one’s experiences and life and enables one to become an individual is the primary goal in life, according to Frosh, a psychoanalytic and identity theorist. Identity is expressed in this way as the middleman (Frosh, 2010, in Balick, 2014). Self-presentation is distinct from other behaviours because effective communication relies heavily on genuine or perfect responses. Self-presentations are abnormal ways for people to convey their sentiments, realities, and/ or self-beliefs as well as their interpersonal outcomes and impacts (Schlenker, 2012). With a multitude of digital experiences, a digital identity structure has arisen. A digital identity can be defined as an enumeration of one’s online self-definitions. Building a second self that works with their second life is how people construct their digital identities (Sohier & Brée, 2019). Digital identity is the method of self-presentation that people exhibit online, both in a personal and professional context (Ahlquist, 2016). According to Kavut (2021b), digital identity refers to an identity type that encompasses cultural capital, individual profiles, social media records, and person sharing in digital contexts. Put differently, digital ID, or biometric identity, is a type of identification that simultaneously verifies encoded identities in social and content networks and incorporates the characteristics of intelligent identity technologies like blockchain and apps based on additional data (Feher, 2019). Simultaneously, digital identity is known as a digital presentation of publicly available data on people or organizations. Obtaining permissions can be used for a variety of purposes, such as verifying identity claims. Digital credentials comprise not only the individual’s qualifying information, such as social security number and passport number, but also biometric information, such as footprint characteristics (Bertino et al., 2009). Consequently, when the term “digital identity” is used, it is understood to refer to a concept that encompasses not only how individuals present themselves on various online platforms, but also a more comprehensive structure that includes things like membership and biometric traits, individual preferences, attitudes, and behaviours related to name, surname, social media, and other online transactions. Schmidt and Cohen mentioned the importance of digital identities in their book The New Digital Age – Reshaping the Future of People, Nations, and Institutions, suggesting that the world’s virtual (online) population will exceed the world’s population within the next decade. They emphasized that while online identities rarely overshadow people’s physical selves, in the future, individuals’ identities in everyday life will become more recognizable than virtual activities and relationships (Schmidt & Cohen, 2013). Their digital IDs are physical, such as fingerprints and DNA; passwords, such as driver’s licenses; Khan, who divides this into four sections: behavioural identity such as electronic and online shopping, such as social media accounts, stated that the electronic and behavioural identities he defines dynamically can develop in line with the habits of individuals, while the legal and physical identities he defines as static are unique and irrevocable (Khan, 2018). Digital identities are an important topic today. It has developed around two macro areas: presentation and reputation. The presentation dimension is the way people display their behaviour in online environments, how to participate and interact in these shared areas, and what persona or self they assume as part of their digital presence. In the reputation dimension, Studies page 177 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 the focus is on what others think of the individual. While reputation is independent of people’s online presence, it is socially dependent. That’s why digital identity management is important because it can affect people’s activities both face-to-face and online (Costa & Torres, 2011). Identities will be the most valuable resource for citizens in the future and they will mostly exist online. They claimed that people’s experiences on the internet can start even before they are born, that people’s life experiences can become frozen over time and eventually surface where they are visible to all, and that businesses can develop new techniques to control information (Schmidt and Cohen, 2013). Focusing on the benefits of digital identities, Aiello et al. (1998) explained that digital IDs also meet the needs of many areas such as online shopping, transactions between businesses, online banking, verification of codes, internal identities when necessary for government, private or business use of the Internet. Al-Mahmood et al. (2018) explained in their work on digital identities and online reputation that The Reputation Economy’s author Michael Fertik stated that online reputation is more important than money or power. This explanation is a case in point of the economic value and importance of digital identity. 3 Methodology 3.1 Aim and Method The purpose of this study is to develop a reliable and valid scale for measuring digital identity definitions, digital personalities, self-presentation strategies, communication styles in digital contexts, and digital identity perceptions of digital environment users over the age of 18 in Turkey. This study was conducted with the field research method. This study used an online Google Form survey as its survey method. The correlational survey model served as the study’s research model. 3.2 Sample The study’s target population comprises Turkish consumers and creators of digital environments. The study’s sample consists of adult Turkish citizens over the age of eighteen who use digital environments. 4 Scale Development Process There are numerous steps involved in the development of this scale. First, a pool of items was produced by reviewing both domestic and foreign literature, and 18 people were interviewed in-depth. In line with the data obtained, the item pool was created and 252 scale items were prepared in the light of the collected information. The scale items are regulated and sent to an expert for assessment. Expert opinions were obtained using the Lawshe approach. There are three academicians and two communication specialists from Turkey among the five experts. The number of articles in the draft questionnaire form was reduced to 55, after evaluation by the experts. Based on the fact that the number of articles is 5 times greater, the pilot research study was aimed to be applied to 275 people, and the online questionnaire prepared through Google Forms was applied to 278 participants. Pretest (pilot) survey studies are the only way to pre-evaluate questions that pose problems for respondents and interviewers. Therefore, both basic textbooks and prerequisites for experienced researchers are explained as mandatory (Presser et al., 2004). Pre-evaluation (pilot) applications prepared for the evaluation of surveys are recommended to be carried out by Studies page 178 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 the interview method (Altunışık, 2008). For this reason, the in-depth interview method was used in the initial stages of designing the Digital Identity Scale to create open-ended and sentence completion questions utilizing the preliminary research form and questions. Pretest studies usually means testing surveys to screen and identify possible problems for a small sample group of 15 to 30. Even the best surveys can be improved by preliminary studies, the general rule on this issue is not to start fieldwork without a comprehensive preliminary. All features of surveys, including survey content, words, order of questions, forms, the difficulty of questions, and guidelines, should be tested (Malhotra, 2006, p.195). Reynolds et al. (1993) emphasized in the literature that the number of pretest samples should be small, that this number can be compared between 5-10 and 50-100, and that the sample is small but large enough to cover the target audience. The scale’s composition was determined to have 28 items and 3 factors, following the completion of the pilot study and factor analysis. In the initial study, 511 participants were contacted via email addresses and social media platforms, and an online survey form created using Google Forms was distributed to them. The 5-way Likert scale was used to rate the items. Next to each of the items five choices were placed. These choices were arranged and scored as (1) Strongly Disagree, (2) Disagree, (3) Neutral, (4) Agree, and (5) Strongly Agree. 5 Findings 5.1 Reliability Analysis De Vaus (2002) explained that a reliable measurement gives similar results if the test is repeated. The most commonly used test reliability index in reliability analysis is Cronbach alpha (α) (Ryan, 2013). Table 1 displays the reliability coefficients of the digital identity scale based on data from the pilot research. Scale Dimensions Number of Items Guttman Cronbach (α) Describing Digital Identity 11 .890 .944 The Need for Digital Identity, Personality, and Digital Identity Presentation 9 .905 .918 Communication, Impression, and Reputation Management in Digital Environments 8 .945 .957 Total 28 .910 TABLE 1: The coefficients of reliability analysis Source: own processing, 2024 Büyüköztürk (2005) explained that the reliability coefficients of scales 0.70 and above are generally considered sufficient for test scores. The results of the reliability analysis demonstrate the scale’s overall and sub-dimension-wide reliability. Table 1 clearly shows that the scale’s reliability research yielded a height sufficient for all reliability coefficients with an alpha value of .910 Cronbach. The Cronbach alpha internal consistency value of scale shows that the reliability coefficient of Describing Digital Identity dimension was found to be α: .944, The Need for Digital Identity, Personality, and Digital Identity Presentation dimension was found to be α: .918, and Communication, Impression And Reputation Management in Digital Environments dimension was found to be α: .957. These results were obtained by looking at the reliability multiples of the sub-dimensions of the scale. Upon closer inspection, it became evident that the scale had a high internal consistency rate. It was found that alpha values for all sub-dimensions are greater than 0.70 and therefore the scale has sufficient reliability. Studies page 179 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 5.2 Validity Analysis According to De Vaus (2002), the validity measurement has been defined as realizing the measurement of things wanted or intended to be measured. Factor analysis has mostly been used to support the validity of the Digital Identity Scale. After determining the KMO and Bartlett values, factor analysis is used in conjunction with the varimax rotation procedure and the maximum likelihood approach. Lawley invented the maximum likelihood method in 1940 to handle factor exclusion. The maximum likelihood method has calculated sample values for each factor loading by calculating loadings that maximize sample suitability of the surveyed correlation matrix (Tabachnick & Fidell, 2014). Maximum likelihood (ML) is the most commonly used prediction method (Harrington, 2009). The recognized statistical method known as factor analysis excludes the dimension reduction technique in particular, as well as the latent structure of a set of variables. Factor analysis is classified into two types: exploratory and confirmatory factor analysis (Ryan, 2013). 5.3 Exploratory Factor Analysis An exploratory factor analysis was performed on 278 participants in total. 55 items in total are included in the first stage of factor analysis. The appropriateness of the factor analysis of the data was assessed using Bartlett’s test (sig=.000) and KMO (.823) The analysis yielded a KMO and Bartlett’s test value of over 80, indicating significance. A first-factor analysis was conducted on fifty-five items. 55 items are grouped under twelve factors, and the eigenvalue is large starting at one. The variation of these twelve components concerning the scale is 67.79 percent. Twelve components specified items have common variances (communalities) ranging from 0.275 to 0.889. Due to the low factor loading values in the factor commonality and under different factors, items constructed for measurement were selected to be gradually removed from the scale. Consequently, items 1, 2, 4, 5, 8, 11, 16, 18, 20, 22, 25, 27, 29, 31, 33, 34, 36, 38, 39, 40, 42, 45, 48, 49, 52, 54, and 55 were removed from the scale. This decision was taken after considering expert opinions and literature support. The second stage of factor analysis is applied after these transactions. Three sub-dimensions were found to be collected using a 28-item scale in the final form, and it was found that the distribution of sub-dimensions was consistent. The Kaiser-Meyer-Olkin and Bartlett test was used to assess the suitability of the factor analysis of the data in the exploratory factor analysis. Kaiser-Meyer-Olkin Measure of Sampling Adequacy Chi-square Bartlett’s Test of Sphericity .858 7045.037 Df 378 Sig. .000 TABLE 2: KMO and Bartlett’s test values of digital identity scale Source: own processing, 2024 Table 2 indicates that Bartlett’s Sphericity test score of 0.00 (p<0.05) and the Kaiser-MeyerOlkin value of .858 are significant. This value shows that the available data are excellent for factor analysis. In other words, KMO and Bartlett’s values are appropriate for factor analysis of the data. It is found that in the second stage of factor analysis, KMO values are higher than in the first. Studies page 180 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 FIGURE 2: Digital identity scale scree plot Source: own processing, 2024 The scree plot shows that the chart curve tends to decrease after three factors. Thus, it was determined that three should be the scale factor number. In the second stage, the scale was collected under three factors, with an eigenvalue greater than 1, when factor common variances (communalities) tables and scree plots were analysed. The total variance value of Digital Identity Scale is 67.84%. It is found to vary between 0.493 and 0.880 in terms of common variances (communalities) of three sub-dimensions that are defined alongside items. Factors Initial Eigenvalues Total Variance Cumulative % % Total Factor Loads Total Variance Cumulative % % Rotation Sums of Square Loads Total Variance % Cumulative % 1 8.776 31.341 31.341 8.053 28.761 28.761 6.784 24.229 24.229 2 5.316 18.987 50.328 5.007 17.883 46.644 6.040 21.571 45.800 3 4.904 17.514 67.842 4.906 17.521 64.165 5.142 18.364 64.165 TABLE 3: Eigenvalues and variance of digital identity scale Source: own processing, 2024 Table 3 displays the item factor loads. A review of the component matrix table reveals that these 28 items are included in the first, second, and third-factor load values, and that factor load values are 594 and above. This finding suggests that the scale has a generic element. The first component, λ1=8.776, describes 31.34% of the total variance, the second factor, λ2=5.316, 18.98%, and the third factor, λ3=4.904, describes 17.51% of the total variance, according to the eigenvalue statistics of the factor analysis conducted in the second stage. Studies page 181 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 Factors Items 1 1. Digital identity is the identity that creates the person one wants to be in the virtual environment and reflects it to others as if it were real. .875 2. Digital identity is the identity by which people reflect their way of life in digital media such as profile photos, identity details, political-social posts made, and people being followed. .854 3. Digital identity is an intermediary identity for individuals to find people who resemble them as a mirror that allows them to see themselves. .832 4. Digital ID is the identity that contains the presentation of all the information that we have created online as a tool that helps communicate when used correctly. .821 5. Digital ID, when used correctly, is an identity that strengthens the face-to-face environment by hosting content that cannot be asked or criticized. .812 6. Digital ID is a kind of personal marketing identity that people show to people they know or do not know. .796 7. Digital ID is the identity of many companies for advertising purposes to reach their user profiles to understand in which direction perceptions are heading. .784 8. I think digital ID and virtual ID are the same things. .742 2 3 9. Digital identity is an identity in which real-life reputation is digitized and digitalized. .676 10. Digital identity, like the footprint of an individual, is the identity that can be monitored at any time on social media, job applications, credit usage, online purchases, digital banking, and choosing friends. .655 11. Digital ID is the identity that protects individual data against data leaks and cyberattacks and provides online security. .618 12. Digital IDs are an identity that everyone needs, which are necessary for all personality structures. .852 13. Social people need a digital identity as much as introverted people. .836 14. If the presented digital identity is far from reality, the person does not like themself and presents their personality differently in the digital environment. .808 15. People who have a sense of curiosity and want to be noticed by other people need a digital identity. .796 16. People need digital identities because of their desire to make them like themselves. .761 17. In digital environments, individuals talk about their positive personality traits with the idea that reality has no chance of being investigated. .743 18. Identity formation in digital environments takes advantage of good impressions and personality and thanks to this, people present themselves better. .673 19. The life of everyone appears perfect and smooth in digital environments. .638 20. People need digital identities to earn money in this area by catching on. .594 21. I think that communication with words or emojis alone is not healthy without hearing the voice of the other person, without seeing their face. .905 22. I think that communication in digital environments is a false and insincere form of communication. .895 23. I think that communication in digital environments, along with face-to-face communication environments, can be an effective tool. .875 24. With digital identity, I think that people try to make themselves think that they are living a life that others will aspire to, by making them look much more successful, rich, and beautiful than they are. .859 25. I think a special effort should be made on social media to have a good reputation via digital identity. .836 26. I think that in digital environments people will not care when the assessment of others about themselves is negative. .834 27. I think it is important how one sees oneself in digital environments. .824 28. I think it can be dangerous to have close ties with other people in digital environments. .781 TABLE 4: Rotated factor matrix of digital identity scale Source: own processing, 2024 Studies page 182 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 The component matrix’s findings demonstrate that the Digital Identity Scale, which has three sub-dimensions, is a valid and reliable measuring instrument. Describing Digital Identity dimension makes up 11 items. The Need for Digital Identity, Personality, and Digital Identity Presentation dimension makes up 9 items. Communication, Impression and Reputation Management in Digital Environments dimension makes up 8 items. Internal consistency and reliability of the scale were calculated for each factor value, which ranged from 594 to 905, as well as for the total score of 910. Eleven items make up the first factor: 3, 6, 7, 9, 10, 12, 13, 14, 15, 17, 19; nine items make up the second factor: 21, 23, 24, 26, 28, 30, 32, 35, 37; and eight items make up the third factor: 41, 43, 44, 46, 47, 50, 51, 53. By analysing the scale items and items in the factors after the factor analysis, the researcher created the scale factor names: Describing Digital Identity is the first factor, The Need For Digital Identity, Personality, and Digital Identity Presentation is the second factor, and Communication, Impression, and Reputation Management in Digital Environments is the third factor. 5.4 Confirmatory Factor Analysis The confirmatory factor analysis stage of the scale was carried out, following the exploratory factor analysis. The confirmatory factor analysis has been satisfied with the AMOS program. Confirmatory factor analysis (CFA) is a research method that uses structural equation modelling to show correlations between latent and observable variables (Çapık, 2014). Gürbüz (2019), acceptable values of path analysis model indices explain in Table 5. A goodness of fit index for the level I and level II path analysis models and the acceptance criteria of fit indices have been explained in Table 5. While evaluating the results of the confirmatory factor analysis in table, it is evaluated by considering indices such as χ2, CMIN (χ2)/DF, NFI, TLI, IFI, CFI, GFI, AGFI, RMSEA, RMR, SRMR, AIC, CAIC and ECVI. Model Fit Index Model Result (Level I) Model Result (Level II) Acceptable Value Harmony X2 Test .000 .000 p>.05 Acceptable CMIN (X2 )/ df 2.101 2.139 <5 Acceptable NFI .915 .913 >.90 Acceptable TLI .940 .938 >.90 Acceptable IFI .954 .952 >.90 Acceptable CFI .953 .951 >.90 Acceptable RMSEA .063 .064 <0.80 Acceptable GFI .870 .870 >.90 Acceptable AGFI .822 .822 >.90 Acceptable <0.80 RMR .083 .079 AIC 841.977 853.318 CAIC 1351.015 1353.101 ECVI 3.040 3.081 Acceptable The smaller value between the two models Acceptable Acceptable Acceptable TABLE 5: Results I and II goodness of fit index path model Source: Gürbüz (2019, p. 34) The digital identification scale’s first-level goodness of fit index results was analysed in Table 5, revealing a chi-square of 621.977, a degree of freedom (df) of 296 (p=.000), and a chi-square / df=2.101. The scale’s comparative fit index (CFI) score was 953; the goodness of fit index (GFI) value was 870; the TLI value was 940; the IFI value was 954; and the RMSEA Studies page 183 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 value was 063. Based on the analysis of model comparative fit indices, it was determined that ECVI 3.040, AIC 841.977, and CAIC 1351.015 had values that were smaller and both saturated compared to independent models. Based on the findings, the goodness of fit indices are often observed to be quite good (Bayram, 2013; Meydan & Şeşen, 2015; Karagöz, 2016). After looking at Table 5’s results for the digital identity scale’s second level goodness of fit indices, the chi-square measured 637.318 degrees of freedom (df) was 298 (p=.000), and chi-square / df=2.139 was discovered. There was a CFI of 951; an RMSEA (Root Mean Square Error of Approximation) of 064; a GFI of 870; and an RMR of 079. The values of the model comparative fit indexes are AIC 853.318, CAIC 1353.101, and ECVI 3.081. These findings demonstrate that the scale is smaller than the independent model and also saturated. It was discovered after the results of the analysis were reviewed that the fit indices are fairly good (Bayram, 2013; Meydan & Şeşen, 2015; Karagöz, 2016). The chi-square model, which allows for the assessment of the precise model convenience of the sample to model, is the most widely used model fit index (Harrington, 2009). The range of the chi-square’s degree of freedom is measured in chi-square tests and ranges smaller than five are recognized as the goodness of fit index (Erkorkmaz et al., 2013). It is seen that supply exceeds goodness fit and acceptable fit index with values of level I 2.101 and level 2 2.139. It is accepted that values of CFI and TLI have between .90 and .95 as the goodness model fit index (Brown, 2015). The CFI values in the first level are 1.953 and 2.951, while the TLI values in the first and second levels are .940 and .951, respectively .938 are markers of good fit. A good fit is indicated by a GFI (Goodness of the Fit index) value of .90 or higher, while satisfactory compliance is indicated by a value of .85 or above (Karagöz, 2019). An explanation of the scale’s permissible GFI .870 compliance value is provided. Overall, all indices of goodness of fit indicate good model fit. The validity and reliability of the 28-items, 3-sub-dimensions of Digital Identity Scale have thus been validated by the results of exploratory and confirmatory factor analyses. Kaiser-Meyer-Olkin Sampling Adequacy .893 Chi-square 4819.911 Df 378 Sig. .000 Bartlett’s Test of Sphericity Cronbach Alpha .894 TABLE 6: Test-retest reliability of digital identity scale Source: own processing, 2024 The KMO value, which was 858 in the pilot study conducted with 278 participants, grew to 893 in the main study, which was produced with 511 participants, according to an analysis of the KMO and Cronbach Alpha data relevant to the main study. The pilot study’s Cronbach alpha value was 910, whereas the main study’s score was 894. The results show that the Digital Identity Scale is a legitimate and reliable measurement tool. 6 Conclusion The Digital Identity Scale revealed digital identity definitions, digital personalities, selfpresentation strategies, communication styles in digital contexts, and digital identity perceptions of digital environment users over the age of 18 in Turkey. The main problem in developing this scale is the deficiency of measurement tools in Turkish literature and the inadequacy of current digital identity scales international literature. Thus, this study targeted developing a valid and Studies page 184 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 reliable measurement tool. Examining articles, theses, dissertations, scales, and other research in the national and international literature reveals that studies on digital identity are undertaken on a restricted and homogeneous population, such as university students, younger people, and members of specific occupational groups. The existing scales have a weak correlation to reveal digital identity definitions, digital identity presentations and digital identity perspectives of digital users. Therefore, this study targeted various sociodemographic groups, including those with digital environment users over the age of 18, based on factors including age, income, gender, and education. Scales and measurement tools on digital identity and digital identity types in the literature were examined. Upon reviewing the literature, both domestically and internationally, it became apparent that the present scales typically have an indirect connection to the topic of digital identification. A sub-dimension known as Virtual Appreciation was discovered in the subdimensions of the “Virtual Identity Scale” created by Kardaş (2017) when the measurement instruments for assessing Digital Identity in Turkey were examined. The Social Approval Needs scale created by Karaşar and Öğülmüş (2016) has been found to include a Positive Impression Release sub-dimension. Demir (2011) created the Turkish adaptation of the Identity Functions Scale created by Serafini and Adams (2009). There are 22 compounds on the initial iteration of Serafini and Adams’ scale. It has 15 items in Demir’s study on Turkish adaption. Sohier and Brée (2017) developed a Digital Identity Scale with four sub-dimensions: Digital Self, Virtual Reputation, Social Inhibition Elimination, and Self-Search, after analysing the measuring tools discovered in the international literature. Serafini and Adams (2009) developed the Identity Functions Scale, also referred to as Structure, which included questions regarding identity and self. The Identity Views Scale (AIQ-IV), created by Cheek and Briggs (2013), has five subdimensions: Relational Identity, Individual Identity, Social Identity, Collective Identity, and Special Items. The social identity and relational identity sub-dimensions were utilized. This study examined the scale materials of the researchers stated, and it is included in literature. It was appropriate to construct a scale as the Digital Identity Scale is not included in Turkish literature. There is a major shortage when there is no measurement device in the relevant subject. It is a unique, valid, and reliable scale developed within the framework of scientific research. This scale consists of 28 items and 3 sub-dimensions. The first sub-dimension of the scale is titled Describing Digital Identity and consists of 11 items which reveal digital identity definitions of individuals. The Need for Digital Identity, Personality, and Digital Identity Presentation are the titles of the second sub-dimension of the scale, which consists of 9 items regarding people’s demands for digital identities, digital identities’ presentation, and personality attributes. Communication, Impression, and Reputation Management in Digital Settings is, with 8 items, the third sub-dimension of the scale that deals with communication patterns that are composed in digital settings and controlling impressions and online reputation with digital identities. The scale can be tested on several sample groups or with various factors connected to digital identification, such as those under the age of eighteen, certain age and educational groups, college students, and personnel in the communication and information sectors. The outcomes may vary if various variables and subjects from the Digital Identity Scale are examined. The scale can be a contribution to the field in this way. It is thought that the scales that are currently in use only provide a small area of study for digital identification, after reviewing the research. It is believed that this scale will address a need in the field and advance the fields of sociology, psychology, and communication in particular. The overall results indicate that this scale has appropriate validity and reliability. Having said that, emerging and enabling technologies like blockchain, big data, AI, and the internet of things are linked to digital identity. Digital identity research has become more important as a result of recent developments in communication technologies including artificial intelligence, big data, blockchain, and other developments. It is recommended that researchers can develop Studies page 185 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 valid, reliable and update scales regarding enabling technologies and digital identity parallel to current technological advancements. Besides, it is suggested that future researches should replicate this study on larger samples such as different cultures, sociodemographic groups or other countries. Trying different survey and data collection methods is suggested for future researches. Bibliography Ahlquist, J. (2016). The digital identity of student affairs professionals. New Directions for Student Services, 2016(155), 29-46. https://doi.org/10.1002/ss.20181 Aiello, W., Lodha, S., & Ostrovsky, R. (1998). Fast digital identity revocation. In Krawczyk, H. (Ed.), Advances in cryptology – CRYPTO’98: Proceedings of 18th annual international cryptology conference (pp. 137-152). Springer. https://doi.org/10.1007/BFb0055725 Al-Mahmood, R., Corbin, J., Balavijendran, L., & Ondracek, C. (2018). Digital identity and e-reputation: Showcasing an adaptive eLearning module to develop students’ digital literacies. In Campbell, M., Willems, J., Adachi, Ch., Macfarlane, S., Ngo, L., O’Donnell, M., Palmer, S., Riddell, L., Story, I., Suri, H., & J. Tai (Eds.), Open Oceans: Learning without borders, Proceedings ASCILITE 2018 (pp. 25-34). Deakin University. Altunışık, R. (2008). Anketlerde veri kalitesinin iyileştirilmesi için öntest pilot test yöntemleri [Pre-test pilot test methods for improving data quality in questionnaires]. Pazarlama Ve Pazarlama Araştırmaları Dergisi, 1(2), 1-17. Ayed, G. B., & Ghernaouti-Helie, S. (2011). Digital identity management within networked information systems: From vertical silos view into horizontal user-supremacy processes management. In Barolli, L., Xhafa, F., & M. Takizawa (Eds.), 14th international conference on network-based information systems (pp. 98-103). IEEE. https://doi.org/10.1109/ NBiS.2011.24 Balick, A. (2014). The psychodynamics of social networking. Karnac Books. Bayram, N. (2013). Yapısal eşitlik modellemesine giriş [Introduction to structural equation modeling]. Ezgi Publishers. Bertino, E., Paci, F., & Shang, N. (2009). Keynote 2: Digital identity protection – concepts and issues. In 2009 international conference on availability, reliability and security (pp. lxix-lxxviii). IEEE. https://doi.org/10.1109/ares.2009.176 Bozkurt, A., & Tu, C.-H. (2016). Digital identity formation: Socially being real and present on digital networks. Educational Media International, 53(3), 153-167. https://doi.org/10.1080 /09523987.2016.1236885 Brown, P. G. (2016). College student, social media, digital identities and the digitalized self [Doctoral dissertation]. Boston College. https://dlib.bc.edu/islandora/object/bc-ir:105053 Büyüköztürk, Ş. (2005). Sosyal bilimler için veri analizi el kitabı istatistik, araştırma deseni SPSS uygulamaları ve yorum [Data analysis handbook for social sciences statistics, research design SPSS applications and interpretation] (35th ed.). Pegem Akademi Publishing. Çapık, C. (2014). Geçerlik ve güvenirlik çalışmalarında doğrulayıcı faktör analizinin kullanımı [Use of confirmatory factor analysis in validity and reliability studies]. Anadolu Hemşirelik ve Sağlık Bilimleri Dergisi, 17(3), 196-205. Costa, C., & Torres, R. (2011). To be or not to be, the importance of digital identity in the networked society. Educação, Formação & Tecnologias, 4(extra), 47-53. http://eft.educom. pt/index.php/eft/article/view/97 Cover, R. (2016). Digital identities creating and communicating the online self. Academic Press. De Vaus, D. (2002). Surveys in social research (5th ed.). Routledge. Demir, İ. (2011). Function of identity scale: Turkish validity and reliability. Kuram ve Uygulamada Eğitim Bilimleri, 11(2), 579-586. Studies page 186 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 Erkorkmaz, Ü., Etikan, İ., Demir, O., Özdamar, K., & Sanisoğlu, S. Y. (2013). Doğrulayıcı faktör analizi ve uyum indeksleri [Confirmatory factor analysis and fit indices review]. Turkiye Klinikleri Journal of Medical Sciences, 33(1), 210-223. https://doi.org/10.5336/medsci.2011-26747 Feher, K. (2019). Digital identity and the online self: Footprint strategies – an exploratory and comparative research study. Journal of Information Science, 47(2), 192-205. https://doi. org/10.1177/0165551519879702 Gürbüz, S. (2019). AMOS ile yapısal eşitlik modellemesi [Structural equation modelling with AMOS]. Seçkin Publishers. Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press. Cheek, J. M., & Briggs, S. R. (2013). Aspects of identify questionnaire (AIQ-IV). Measurement instrument database for the social science. https://www.studocu.com/in/document/ alagappa-university/art-education-psychology/aspects-of-identify-questionnaire/42681175 Karagöz, Y. (2016). SPSS ve AMOS 23 uygulamaları: İstatistiksel analizler [SPSS and AMOS 23 applied: Statistical analysis]. Nobel Academic Publishing. Karagöz, Y. (2019). SPSS – AMOS – META uygulamalı: İstatistiksel analizler [SPSS – AMOS – META applied: Statistical analysis] (2nd ed.). Nobel Academic Publishing. Karaşar, B., & Öğülmüş, S. (2016). Sosyal onay ihtiyacı ölçeği: Geçerlik ve güvenirlik analizi modellemesi [The need for social approval scale: Reliability and validity analysis modelling]. Ege Eğitim Dergisi, 17(1), 84-104. https://doi.org/10.12984/eed.38607 Kardaş, S. (2017). Sanal kimlik ve spiritüel iyi oluşun üniversite öğrencilerinin narsistik eğilimlerini yordayıcılığı [Virtual identity and spiritual well-being predict narcissistic tendencies of university students] [Doctoral dissertation]. Marmara University. https://acikbilim.yok.gov. tr/handle/20.500.12812/474355 Kavut, S. (2021a). Kişilerarası iletişim bağlamında dijital kimlik algısı ve izlenim yönetimi üzerine bir araştırma [A research on digital identity perception and impression management in the context of interpersonal communication] [Doctoral dissertation]. Istanbul University. http:// nek.istanbul.edu.tr:4444/ekos/TEZ/ET002330.pdf Kavut, S. (2021b). Digital identities in the context of blockchain and artificial intelligence. Selçuk Communication Journal, 14(2), 529-548. https://doi.org/10.18094/josc.865641 Khan, F. (2018, September 7). The new age of digital identity its challenges. http://www. datadriveninvestor.com/2018/09/07/the-new-age-of-digital-identity-its-challenges Lemma, A. (2017). The digital age on the couch-psychoanalytic practice and new media. Routledge. Malhotra, N. K. (2006). Questionnaire design and scale development. In Grover, R., & M. Vriens (Eds.), The handbook of marketing research: Uses, misuses, and future advances (pp. 176202). Sage Publications. Meydan, C., & Şeşen, H. (2015). Yapısal eşitlik modellemesi: AMOS uygulamaları [Structural equation modelling: AMOS applications] (2nd ed.). Detay Publishers. Paranjpe, C. A. (2002). Self and identity in modern psychology and indian thought. Kluwer Academic Publishers. Pato, J. C. (2003). Identity management: Setting context. Kluwer Academic. https://web.archive. org/web/20220124093831/https://www.hpl.hp.com/techreports/2003/HPL-2003-72.pdf Phiri, J., Zhao, T.-J., & Agbinya, J. I. (2011). Biometrics, device metrics and pseudo metrics in a multifactor authentication with artificial intelligence. In 7th international conference on broadband communications and biomedical applications (pp. 157-162). IEEE. https://doi. org/10.1109/ib2com.2011.6217912 Phiri, J., Zhao, T.-J., Zhu, C. H., & Mbale, J. (2011). Using artificial intelligence techniques to implement a multifactor authentication system. International Journal of Computational Intelligence Systems, 4(4), 420-430. Studies page 187 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1), 109-130. https://doi.org/10.1093/poq/nfh008 Reed, T. V. (2019). Digitized lives-culture, power and social change in the internet era (2nd ed.). Routledge. Reynolds, N., Diamantopoulos, A., & Schlegelmilch, B. (1993). Pre-testing in questionnaire design: A review of the literature and suggestions for further research. Market Research Society Journal, 35(2), 1-11. https://doi.org/10.1177/147078539303500202 Rodrigues, R. E. (2011). Revisiting the legal regulation of digital identity in the light of global implementation and local difference [Doctoral dissertation]. The University of Edinburgh. https://era.ed.ac.uk/bitstream/handle/1842/8942/Rodrigues2012. pdf?sequence=2&isAllowed=y Ryan, T. P. (2013). Sample size determination and power. John Wiley & Sons. https://doi. org/10.1002/9781118439241 Serafini, E. T., & Adams, R. G. (2009). The function of identity: Scale construction and validation. Identity: An International Journal of Theory and Research, 2(4), 361-389 https://doi. org/10.1207/s1532706xid0204_05 Schlenker, R. B. (2012). Self-presentation. In Leary, M. R., & J. P. Tangney (Eds.), Handbook of self and identity (2nd ed.) (pp. 542-571). The Guilford Press. Schmidt, E., & Cohen, J. (2013). The new digital age: Reshaping the future of people, nations and business. Hachette UK. Sohier, R., & Brée, J. (2017). Proposal of a digital identity scale. In Rossi, P. (Ed.), Marketing at the confluence between entertainment and analytics (pp. 1219-1231). Springer. https:// doi.org/10.1007/978-3-319-47331-4_235 Sohier, R., & Brée, J. (2019). Towards adolescents’ digital identity profiles: A comparison between quantitative and qualitative analysis. In Rossi, P., & N. Krey (Eds.), Finding new ways to engage and satisfy global customers (AMSWMC 2018) (pp. 503-516). Springer. https://doi. org/10.1007/978-3-030-02568-7_140 Sullivan, C., & Stalla-Bourdillon, S. (2015). Digital identity and French personality rights – a way forward in recognizing and protecting an individual’s rights in his/her digital identity. Computer Law and Security Review: The International Journal of Technology Law and Practice, 31(2), 268-279. https://doi.org/10.1016/j.clsr.2015.01.002 Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.) Pearson. Wood, F. A., & Smith, J. M. (2005). Online communication linking technology, identity, and culture (2nd ed.). Lawrence Erlbaum Associates Publishers. Studies page 188 Media Literacy and Academic Research | Vol. 7, No. 1, June 2024 Author Assoc. Prof. Sevgi Kavut University of Kocaeli in Türkiye /Turkey Faculty of Communication Kabaoglu, Baki Komsuoglu Boulevard, No: 515, Umuttepe 41001, Kocaeli TURKEY [email protected]; [email protected] ORCID ID: 0000-0002-0253-3906 Sevgi Kavut is an Associate Professor at the Department of Advertising at the Kocaeli University, Faculty of Communication, Turkey. She graduated from Istanbul University Faculty of Communication, Journalism Department, and Public Relations and Publicity Department. She received her MA degree in Public Relations and Publicity from Marmara University and her Ph.D. degree in Public Relations and Publicity from Istanbul University. Her main research areas are communication studies, digital identity, interpersonal communication, communication psychology, new communication technologies, social media, personality and impression management. Studies page 189