Objective Our Approach Methods Contact Information ...

Positive and Negative Online Gaming “Involvement”: An Ethnographic Approach to Measurement & Assessment Jeffrey G. Snodgrassa, HJ Francois Dengah IId, Andrew P. Bagwella, Max Van Oostenburge, Michael G. Lacy b , Noah Benedicta, Cheri J. Smarr-Fostera, & Justin M. Patry c Department of: Anthropologya , Sociologyb, & Journalism & Media Communication c - Colorado State University – Fort Collins, CO of Sociology, Social Work, & Anthropology - Utah State University - Logan, UT - e Pacific States Marine Fishery Council & NOAA – Portland, OR

dDepartment

Analysis & Results

Objective Employ ethnographic methods more attentive to insider gamer perspectives to develop a culturally-sensitive scale measure to assess internet gaming “disorder” or “addiction.”

Our Approach Researchers propose “internet gaming disorder” as characterized by excessive or poorly controlled behaviors, preoccupations, and urges regarding online gaming that lead to distress or impairment (Pontes and Griffiths 2015). They suggest that distressful patterns of internet use, like other behavioral addictions, can be usefully classified with alcohol and drug use disorders, as they share common characteristics related to salience, mood modification, tolerance, withdrawal, conflict, and relapse (Block 2008). Our approach has led us to take a more balanced insiders’ approach to online gaming by observing the full range of both positive and negative experiences (Snodgrass et al. 2012). This has led us in the present study to treat so-called online “addiction” as a fusion of a more neutral intensive gaming involvement with high negative and low positive play consequences , which is but one of many other possible online gaming outcomes.

Methods We began fall 2014 with several months of participantobservation research in the massively multiplayer online roleplaying game (MMORPG) Guild Wars 2 (Figure 1), which included observations and unstructured interviews inside in-game associations of like-minded players termed guilds . This was followed by semi-structured interviews (N=20) using the McGill Illness Narrative Interview (MINI) (Groleau, Young, and Kirmayer 2006) modified to better elicit insider gamer understandings of both positive and negative play experiences. The interviews were coded and analyzed with the software MAXQDA, which helped us isolate important components of online gaming involvement, the insider language of each component, and how each component was associated with positive and negative gaming experiences. This analysis led us to a series of propositions about online gaming involvement, as well as positive and negative consequences of online gaming, which we treated as a cultural frame or model (Bennardo and De Munck 2014). The model statements were placed on an online survey, which we distributed in our own play networks as well as on Reddit gaming forums, receiving ~700 responses. We analyzed responses to these statements with cultural consensus to explore how much gamers have a shared frame regarding such consequences (Romney, Weller, and Batchelder 1986).

Figure 1: Our Virtual Research Team in Guilds Wars 2

I. Analysis of our MINI interviews revealed an overall positive and highly social online gaming experience for our respondents, which contradicts in many ways the “lonely gamer” stereotype popularized in U.S. media (Schiano et al. 2014), as shown in Table 1. It should be noted that we oversampled problem play respondents, in order to better grasp that experience, thus inflating those frequencies below. More typically, problem players are estimated at around 5% of the online gaming population (Pontes et al. 2014).

Table 1: +/- Gamer Experiences (via MINI N=20). Respondent Attributes

Frequencies

In a relationship

50%

Regularly plays socially

95%

Plays with offline social network

95%

Regularly plays solo

50%

When plays alone, enjoys it?

80%

Is lonely

25%

Exhibits problem play

35%

Gaming helps with loneliness

65%

Gaming adds to loneliness

20%

II. Further structured analysis in MAXQDA of our MINI interview data yielded five key online gaming involvement themes: involvement (time, energy, effort), engagement (emotion, enjoyment, passion), immersion (attention grabbing, lost in the game), achievement (advancement, winning), and social (involved/engaged with others, community, friendship). Likewise, we grouped key positive/negative consequence into: psychological, physical, behavioral, social, and achievement , which we show in more detail below in Table 2. There, positive and negative gaming experiences are relatively balanced, with psychological being the most common positive experience, social the most frequent negative consequence. It should be noted that negative experiences such as frustration are not in themselves bad, as they can reflect in-game challenge that leads ultimately to satisfying play.

Positive Consequences

Table 2: +/- Gamer Experiences (via MINI N=20). Code Negative Code Frequency Frequency Consequences

Psychological

423 (47.5%)

Psychological

234 (25.2%)

Physical

36 (4%)

Physical

60 (6.5%)

Behavioral

87 (9.8%)

Behavioral

199 (21.4%)

Social

209 (23.5%)

Social

319 (34.3%)

Achievement

135 (15.2%)

Achievement

118 (12.7%)

890 (100%)

930 (100%)

III. We developed three survey items for each of our five involvement themes (15 total) and 21 positive and 21 negative consequences items (in each case, 3 psych , 3 phys , 6 beh , 6 soc, 3 ach ), for a total of 57 items. Respondents were asked if they agreed or disagreed that each item captured something important and even typical about intensively involved online gamers. Some sample items include: Q8: Get so involved in their play that they lose track of time. [Involvement / Immersion] Q17: Find that online gaming help relieve frustrations and improve their mood. [+ Cons/ Psych] Q53: Find that playing online games leads to conflicts with friends and family. [- Cons/ Soc]

Discussion

Cultural consensus analysis of dichotomized responses (T/F or Agree/Disagree) showed shared insider gamer consensus on the meaning of intensive online involvement and potential positive and negative online consequences (Eigenvalue ratio: 6.5; Average competency: .445), with the answer key typically being “YES/AGREE” for the involvement and positive benefits items, “NO/DISAGREE” for the negative consequences items. As a more intuitive alternative to the eigenvalue ratio, we also measured the fit of the cultural consensus model using use the new summary statistic developed by Lacy and Snodgrass (Lacy and Snodgrass 2015), for which P CCU = 0.49. This indicates that cultural consensus model provides a good summary of the data; this value shows that predicting individuals’ responses based on their competence scores with the answer key allows prediction of the response wtih 49% less error than an a priori guessing model. (This is roughly analogous though by no means identical to obtaining an R2 of 0.49 in a regression analysis.) IV. Subsequent linear regression analysis showed some variation in respondents’ competence scores according to their main online games played, as shown below in Table 3. Average competence scores: MOBAs (“massive online battle arenas” like League of Legends , N=121): .405 MMORPGs (such as World of Warcraft or Guild Wars 2, N=428): .45 Both MOBAs and MMORPGs (N=63): .481

Table 3: Regression Analysis of Cultural Competence (1st Factor Loadings) on Main Game Type. Cultural Competence (1st Factor Loadings) * p