Tabletop Role-playing Games (TRPG) and Group Coherence
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1 Tabletop Role-playing Games (TRPG) and Group Coherence Av: Martin Danielsson Handledare: Fatima Jonsson Södertörns högskola | Institutionen för naturvetenskap, miljö och teknik Kandidatuppsats, 15 hp Medieteknik | HT2020‒VT2021
Transcript of Tabletop Role-playing Games (TRPG) and Group Coherence
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Tabletop Role-playing Games (TRPG) and Group Coherence
Av: Martin Danielsson Handledare: Fatima Jonsson Södertörns högskola | Institutionen för naturvetenskap, miljö och teknik Kandidatuppsats, 15 hp Medieteknik | HT2020‒VT2021
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Rollspel och gruppkoherens En enkät- och intervjustudie
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Abstract Tabletop role-playing games (TRPGs), also known as Pen-and-Paper role-playing games
(PnP-RPGs), are games that can be described as a mix between board games and improvi-
sational theatre. Each player takes on the role of a single character, while the game’s leader
(GM or DM) is in charge of both simulating the game world, acting out all remaining
characters and facilitating the game rules. The games are often played in campaigns,
continual stories told over several gameplay sessions. This thesis investigates whether
players’ types/characteristics and preferences affect the enjoyment of play, and in particu-
lar whether being coherent with the rest of the role-play group in these respects affects the
enjoyment of TRPG play. The research questions were addressed primarily by a survey
among TRPG players that received 1,982 completed questionnaires. These were then
analysed by correlation and factor analyses in order to find connections between responses.
The thesis work shows that the length and depth of previous experiences have almost no
correlation to the level of enjoyment. Some aspects of the game itself contributed to the
enjoyment, but a clearer factor was that of group coherence – if the player perceives the
group to have the same or similar stances as him/herself on game preferences.
Keywords: tabletop role-playing game, pen-and-paper role-playing game, group
coherence, game enjoyment, satisfaction
Abstrakt Bordsrollspel (TRPGs), även känt som penna-och-papper-rollspel (PnP-RPGs), är en typ av
spel som kan beskrivas som en blandning mellan brädspel och improvisationsteater. Varje
spelare antar rollen av en karaktär, medan spelledaren (GM eller DM) ansvarar för simu-
leringen av spelvärlden, agerandet av resterande karaktärer samt ansvar för regelhållningen.
Denna uppsats undersöker om spelartyper (egenskaper) och deras preferenser påverkar
spelglädjen, och i synnerhet huruvida det är viktigt att ha samma uppfattning som den övriga
gruppen i dessa frågor (gruppkoherens). Forskningsfrågorna besvarades huvudsakligen
genom en enkätundersökning bland TRPG-spelare som gav 1982 komplett ifyllda enkätsvar.
Dessa analyserades sedan med korrelations- och faktoranalyser för att hitta kopplingar
mellan svaren. Uppsatsen visar att längden och djupet av tidigare spelupplevelser nästan inte
har något samband med nivån av spelglädje. Några aspekter av själva spelet bidrog däremot
till spelglädjen, och den tydligaste faktorn var gruppens koherens – om spelaren uppfattar att
gruppen har samma eller liknande ståndpunkter som sig själv när det gäller spelpreferenser.
Nyckelord: bordsrollspel, penna- och papper-rollspel, gruppkoherens, spelglädje, nöjdhet
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Table of Contents
Abstract .......................................................................................................... 3
Abstrakt .......................................................................................................... 3
Table of Contents ........................................................................................... 4
Introduction .................................................................................................... 5
Related Research ........................................................................................... 6
Frame Analysis .................................................................................................... 6
Player Typology ................................................................................................... 7
Purpose and Research Question ................................................................ 10
Research Design and Method ..................................................................... 11
Quantitative Survey ........................................................................................... 12
Qualitative Interviews ........................................................................................ 20
Data Collection ............................................................................................. 21
Data Analysis ............................................................................................... 23
Interviews ........................................................................................................... 34
Results .......................................................................................................... 38
Discussion .................................................................................................... 39
Conclusions ................................................................................................. 41
References.................................................................................................... 43
Appendix A ................................................................................................... 45
Appendix B ................................................................................................... 46
Appendix C ................................................................................................... 47
Appendix D ................................................................................................... 53
Appendix E ................................................................................................... 55
Appendix F ................................................................................................... 56
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Introduction
Tabletop role-playing games, also known as pen-and-paper role-playing games,
shortened as either TRPG or PnP-RPG, is a unique medium of games that effectively
plays like a mix between its combat-simulating wargaming roots (Mizer, 2015, Ch. 1),
and the freeform role-play of its later off-shoot, Live-action role-play (LARP)
(Rognli, 2008). It is a style of game in which the participants live out their characters’
lives through speech acts or (in earlier forms, still in play) letters (or nowadays
emails). The participants mentally embody the actions of their fictive characters in
accordance with both their predetermined character attributes and by acts allowed
according to an often rather formal set of rules. Additionally, there is often an element
of chance and probability involved, allowing outcomes to be partially decided by dice
rolls. Under these rules, the players can improvise, akin to improvisational theatre but
lacking the body-acting part, whilst being governed by a stricter set of rules. An
important aspect of the play is not only to act but also to make decisions. The
decisions made by the players as their alter egos, together with the GM (see below),
determine the path the game takes.
The combination of decision and game rules govern the unfolding of a co-created
story. Almost all role-play groups have a game master (GM) that creates and
maintains the scenario in which the players act out their characters.1 The GM is in
control of the fictive world while the other players act out and make decisions
according to how they interpret what their fictive characters should do. The GM
controls the play by consulting the rules and/or making his/her own decisions on
consequences of acts and decisions, and then feeding back the results to the players.
A range of academic theories has been developed (Torner, 2018), attempting to
explain phenomena encountered in tabletop and pen-and-paper role-playing games.
The next section will discuss related research relevant to the thesis work.
1 In the survey in this thesis, only 5 respondents out of 1,982 stated that their group did not have a GM.
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Related Research
The related research that is relevant for this work can be divided into two parts. Frame
analysis deals with the context in which a play unfolds while player typology rather
looks at the players themselves and tries to categorise them into different player
categories. Both lines of research are interesting to explore for this thesis.
Frame Analysis
Much of the early studies on TRPGs have been ethnographic in nature (Cover, 2010,
Introduction chapter) and focused on conducting frame analysis on role-players
(White, 2020, Ch. 4), which interprets role-players as jugglers of different frames of
reality during play. In one of the approach’s most recent iterations, the frames listed
are: the diegetic character frame, the constative frame, the narrative frame, the player
frame, and finally the “primary framework”, which is reality as we know it (Mizer,
2015, Ch. 1). Meanwhile, Waskul and Lust equate their model of the “person-player-
persona trinity" to frames, saying that TRPG play is done by people with social roles,
who take on additional roles as both players and player characters and have to
navigate between these. They also argue that the frames of role-play might not be so
different from the variety of social roles we inhabit in real life (Waskul and Lust,
2004). While the exact definition of each frame varies between authors, the use of
frame analysis overall is common (White, 2020, Ch. 4). However, Mizer (2015, Ch.
1) and Cover (2010, Ch. 9) criticize frame analysis for being unable to capture the
finer nuances of the role-play experience, suggesting a more phenomenological
approach, which Mizer commits to in his phenomenologically coloured ethnographic
exploration of the worlds formed during TRPG play (2015, Ch. 1).
Ron Edward’s Forge Theory (White, 2020, Ch. 4; Boss, 2008) is “a body of role-
playing game theory”, the ideas of which “offer an outline of the structure of role-
playing and describe techniques used in tabletop and other role-playing games.”
(Boss, 2008). Often referenced in TRPG literature is Forge Theory’s Big Model, a
diagram which, as Boss (2008) puts it, serves as the “central organizing representation
of the concepts of the theory...” The Big Model sticks out as one of the most
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ambitious attempts at a single, unified TRPG theory. Without commenting on its
effectiveness, White (2020, Ch. 4) argues that the Big Model itself can “be understood
as a kind of frame-theoretic approach, or at least as being consistent with one, with
Creative Agenda occupying a very interesting conceptual position in the model,” and
that “there appears to be significant conceptual overlap between the Big Model and
scholarly frame-analytic approaches to RPGs.” An example of this is how the Big
Model’s concept of Social Contract can be seen as mirrored in Fine’s, Mackay’s and
Cover’s (2010, Ch. 5) research, referred to there as “primary framework”,
“idiocultural frame” and “social frame” respectively (White, 2020, Ch. 4).
Player Typology
Attempts have been made to categorize role-players into different player types. As
Hamari and Tuunanen (2014) put it, in their meta-synthesis of previous player
typology studies within the broader scope of video games: “The goal of segmentation
is to identify groups of people that are as homogenous as possible, but that differ from
each other in a significant way.” Hamari and Tuunanen’s findings may not apply
directly to TRPGs, since video games are played differently, but naturally the benefits
of segmentation could. There is not yet any consensus on which of the many TRPG
player typologies is best, however, nor is the available research adamant in proving
the usefulness of one typology above the other. This could be considered a gap for
future research to fill. Attempts at formulating a TRPG player typology seem to have
been driven mostly by a communal need for shared terminology, with which to enable
more accurate discourse across the diversity of games and play-styles within the
medium. On occasion, player typologies have also appeared as results of TRPG
market research, such as the Wizards of the Coast’s model of four player categories:
the character actor, the storyteller, the thinker, and the power gamer. The survey
which served as the origin for the typology reported an even split between the player
types, approximately 22 percent each, with another 12 percent falling somewhere in
the middle (Cover, 2010, Ch. 9). It could be argued that the even statistical divide
implies a working segmentation.
However, like most discussions on TRPG theory, most attempts at player typology
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have taken place in online forums and in published RPG fan-magazines (Torner,
2018). One of the most academically discussed player typologies is “GNS” of Forge
Theory, GNS being short for Gamism, Narrativism, and Simulationism. Important to
note is that Boss refers to GNS not as a typology, but as three “agendas”, which are
“mutually incompatible”, which helps illustrate GNS’s relation to The Big Model’s
concept of Creative Agenda, which “describes a player’s aesthetic preferences and
choice in play.” (2008). Additionally, when Torner (2018) compares GNS to the often
referenced “Bartle types” typology of Multi-User Dungeon players (Hamari and
Tuunanen, 2012), White (2020, Ch. 4) somewhat objects to this. White highlights that
GNS has been argued to not just indicate an individual’s preferences but to also refer
to patterns in the game design itself, as well as the total style of play emerging from a
role-play group. In this regard, GNS and other aspects of the Big Model might defy
normal definitions of a player typology but are nevertheless useful for analysing
different playstyles. For instance, the Big Model presents a variable such as Stance.
Stance, according to Boss, “refers to the attitude or mental positioning a player takes
with respect to their character and the other elements of the Shared Imagined Space”
(2008). In a way, this is yet another expression of playstyle, which in and of itself
could constitute a typology, or at least an axis of one. In this manner, many aspects of
the Big Model might be able to function as types of TRPG player typologies, not
merely GNS.
White (2020, Ch. 4) also highlights the TRPG community’s critiques of Forge
Theory, such as a perceived lack of granularity in GNS, how it may negatively divide
players, accusations of bias towards Narrativism as a more sophisticated style of play,
and accusations towards the entire Forge for trying to take the fun out of RPGs.
Meanwhile, Boss (2008) doesn’t offer the same nuance, perhaps partially due to the
fact that she isn’t focused on the reception of The Forge, just its theories.
Cover (2010, Ch. 9) and Torner (2018) both claim that one of the most well-known
theories about styles of play is the Threefold Model, consisting of the dramatist, the
simulationist, and the gamist “gameplay types”, each of which Mason (2004) instead
refers to as “stances”, which counterintuitively enough, has no relation to the Stances
of Forge Theory. Indeed, this is a predecessor to GNS, coined in the Usenet group
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rec.games.frp.advocacy (RGFA) many years prior. The model has a few notable
differences to GNS, however, one of which is how Dramatism is different from what
GNS’s Narrativism came to be. In White’s (2020, Ch. 4) words: “Fairly rapidly,
Narrativism came to be understood as having to do with emphasizing techniques that
prioritized the creation of story in play, in the sense of presenting players with
thematically resonant or meaningful choices for their characters, without including a
type of ‘story creation’ that fit within RGFA Dramatism, that of the GM trying to lead
players through a preset plot or storyline”. It could be argued that Forge Theory’s
GNS is more relevant today, being an iteration of the Threefold Model, but the
available literature seems to suggest that the Threefold Model itself is more well-
known. On the other hand, GNS has the benefit of being part of the larger Big Model,
which has uses beyond what the Threefold Model can offer. In the end, it is hard to
discern if either model is “superior” in any way. As Torner (2018) writes: “Each time,
a new piece of theory wishes to end fruitless debate and provide a big ecumenical tent
of tolerance, yet by contributing a new piece that disagrees with previous ones, it does
the opposite: continue the debate.”
With this, maybe the choice of a specific typology over another is less of a concern.
Typologies may create division, but there is nonetheless an underlying fact that TRPG
players play in many different ways, a fact which might be of higher importance than
typology choice. A common standpoint amongst the literature is, as Tychsen et al.
(2008) put it: “if the players have very disparate aims when playing the game, this
might result in confrontations, disagreement, and/or frustration, and thus lower
enjoyment. On the other hand, a well-functioning group might find the game sessions
to be more enjoyable due to the positive group dynamics.” This is echoed by
Edwards, one of the authors behind Forge Theory, saying that “players have different,
sometimes conflicting preferences about desired styles of play” (Boss, 2008).
Naturally, this is already expressed in the TRPG community itself in many ways, one
being the dichotomy of OSR vs. Story Games, which Mizer (2015, Ch. 1) reports are
two significantly distinct camps. However, he does admit that “the borders are as
fuzzy as any cultural divisions”, and Torner (2018) holds that “Despite occasional
status battles … the two communities overlap significantly”, even if it isn’t entirely
clear which significant overlaps Torner is referring to.
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Purpose and Research Question
The purpose of the study undertaken is to find out what constitutes components of a
satisfying TRPG play experience in terms of player types, characteristics, and
preferences. Are there any patterns or clues to more or less satisfied players and
groups to be found in the game setup, the way the groups are set up, or in the earlier
experiences of players? While there has been research conducted about TPRGs in
general, there seems to be none2 addressing how the playgroups function when it
comes to group coherence, i.e. how similar a particular player’s views and preferences
are to the other group members and if that has an impact on the satisfaction of playing
the game.
The research question is formulated as an overarching question pertaining to the
overall discussion in the section above, with an emphasis on groups in the form of a
sub-question.
Research question: How do player typology and play preferences affect the enjoyment
of play in tabletop/pen-and-paper RPG play? And, in particular, how does perceived
group coherence in player typology and play preferences affect the enjoyment of play
in role-playing?
The research is limited to tabletop (TRPG) and pen-and-paper (PnP-RPG) games and
does not include computer-based role-playing games (CRPGs) in general, neither
classical CRPGs nor MMORPGs (such as World of Warcraft and similar). The
research does not address the internal group dynamics in groups (group psychology)
but limits itself to the experienced group coherence by individual players and its
effects on gameplay satisfaction.
2 At least, the author found none during an extensive literature search.
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Research Design and Method
The research questions were addressed by the design of a mixed-method process
(Creswell, 2014, Ch. 10). In a mixed-method study, both quantitative and qualitative
methods of collecting data are used in conjunction. A quantitative method is used for
collecting rather close-ended data in the form of predetermined questions, in this case
asked using an online survey method hosted by Google Forms. A qualitative method,
on the other hand, is used for collecting more open-ended data that cannot be captured
by predetermined questions.
Since the aim of this thesis was to probe a reasonable sample of the table-top playing
community, a qualitative method like a survey was the only possibility to reach out
far enough to be able to collect and analyse the data required for the study. And only a
statistic software package could reasonably analyse what in the end became close to
one hundred thousand data points. But a survey cannot by itself catch the finer/deeper
details of the research questions. Therefore, the quantitative data collected by surveys
was supplemented with a set of semi-structured deep interviews after the survey data
had been collected and partly analysed.
It is important that the quantitative and qualitative parts of the research design are
well integrated. This way, they can build on each other and compensate for each
other’s weaker properties. In this thesis, the integration was done as follows: While
the survey was being designed and constructed, ideas on which questions could not go
deep enough in a survey were kept note of. This set of notes became one basis for the
interview template for the qualitative part. Further, during pilot testing additional
insights into what would not be captured in a 15-minute survey were gathered. Thus,
even though the interviews were conducted after the surveys, the interview template
began its development in parallel and as an integral part of the total research design. It
was also decided that each respondent in the interview study should prepare
beforehand by taking the survey in order to better facilitate a semi-structured
discussion around the same questions and statements that were asked to the survey
respondents. This way, an integration between the two parts of the research design
was achieved, and the interviews were guided in a direction that would enhance the
quantitative study and facilitate a deeper analysis and understanding of the results.
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Quantitative Survey
The quantitative research design is based on an earlier study on tabletop and computer
role-playing games (TRPGs and CRPGs) and uses their design for enabling analyses
of the data collected (Tychsen et al., 2008). The design uses a method based on a
measure for evaluating the satisfaction of RPG players called a FUN construct that in
their adaption to RPGs consists of five components, see Table 1.
Table 1: FUN construct factors (from Tychsen et al., 2008)
Temporal Dissociation (TD): The degree to which the player felt time passing quickly, suggesting a high level of engagement in the activity. Focused Immersion (FI): The degree to which the player felt immersed in and focused on the game. Heightened Enjoyment (HE): The degree to which the player enjoyed the gaming experience. The questions as-sociated with the HE sub-construct directly allowed the players to state their enjoyment of the experience. Narrative Engagement (NE): This sub-construct captures the degree to which players felt they were actively en-gaged with and joined in the game. Intention to Revisit (IR): The degree to which the player, given the opportunity, would want to revisit the experi-ence.
The FUN construct was originally presented in (Newman, 2005), but it then only
contained the first four categories and was intended for investigating improvisational
theatre rather than role-playing games. The extensions by (Tychsen et al., 2008) make
the FUN construct more suitable for RPG research.
For each category, two questions were prepared for the survey questionnaire. The
questions and their categories are shown in Table 2.
Table 2: FUN statements
Q FUN Statement P/N
F1 TD Most sessions, I feel like time passes quickly. P
F2 TD I’d like to take more or longer breaks during sessions. N
F3 FI I often do things in the background when my character isn’t involved, like using my phone, browsing the internet, grinding in a video game, etc. N
F4 FI During sessions, I spend most of the time deeply focused on the gameplay, game world, or story, imagining the characters, events, and encounters. P
F5 HE-S I enjoy playing my character(s) a lot. P
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Q FUN Statement P/N
F6 HE-O I enjoy the play-styles of my fellow group members a lot. P
F7 NE-I Most of the time, I feel like my character is making a difference in the story and/or game world. P
F8 NE-M I seldom feel there is an opportunity out-of-session to influence the content of the campaign. N
F9 IR Most of the time, I look forward to sessions a lot. P
F10 IR I’d be sad to see this campaign end, even if our group would start a new one together. P
F11 ** I’m happy with the campaign so far. P
F12 IR I’d hesitate to start a new campaign with this group, should this one end. N
F13 HE-O I often wish my fellow players played their characters differently. N
In the original survey, the first 11 questions were asked – two from each category plus
a general satisfaction question (F11, marked ** since it does not belong to any
particular FUN category but is rather an overall measure). Two categories also have
subcategories: HE can be subdivided into HE-S (Heightened Enjoyment due to self)
and HE-O (Heightened Enjoyment due to others) which are both represented with one
statement each. Similarly, NE can be subdivided into NE-I (Narrative/Play
Engagement due to in-game agency) and NE-M (Narrative/Play Engagement due to
meta-level agency) which are also present with one statement each. The P/N column
indicates whether a statement is positive or negative with respect to satisfaction, and
thus whether a satisfied player would score it high or low on a Likert scale (the study
uses 5-point Likert scales).
In the follow-up survey (discussed below under Data Collection), two more
statements were added. It was found from the original survey that the negatively
worded statements F2, F3, and F8 were much less indicative than the positive ones.
To investigate if that was due to the negative format itself or the particular content of
the statements (and thus increase the internal validity), two more negative FUN
statements were added (F12 and F13).3 The other 11 statements remained unaltered.
The FUN statements were preceded in the questionnaire by statements (called B-
statements in the survey) concerning the GNS aspects of TRPG games. As noted
above on player typology, Gamism, Narrativism, and Simulationism are the player
types, sometimes referred to as agendas. Further, Simulationism can be divided into
3 There was no sign of a general ineffectiveness associated with negative statements in either
(Newman, 2005) or (Tychsen et al., 2008).
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the labels System Integrity (SSI), Setting Believability (SSB), Situation Focus (SSF),
Character Believability and Integrity (SCB), and Adherence to Source Material (SAS),
thus creating seven categories in total (White, 2020; Boss, 2008). In order to map out
which category or categories each responding player belongs most to, and to what
degree, a set of statements were designed in the form of pairs of stances, one from
each GNS category. In Table 3, the GNS statements are shown.
Table 3: GNS statements
Q GNS Statement
B1 G‒
SCB+
If my character has a significant flaw that becomes relevant during a tense mo-ment in-game, I would still want to act out the flaw, even if it would be costly or dumb from a tactical standpoint, to stay true to my character.
B2 G+ N‒
It is more important to me that scenes challenge my thinking and/or decision-mak-ing, rather than pose questions about human existence, culture, or society. In the end, I find difficult battles and/or mysteries a lot more interesting than exploring moral dilemmas or social issues.
B3 G+
SSI‒
I prefer it when GMs reward players for being smart, creative, or extra engaged, by handing out additional benefits to their attempted actions, like bonuses to skill checks. Ideally, I'd want GMs to do this, even if the extra rewarding of players isn’t included in the rules as written.
B4 SSI+ SSB‒
If a supposedly powerful enemy is found to be easily killable through an unfore-seen and convenient exploit, then congratulations to the player who found it. They should be able to use the exploit freely, even if it would imply that everyone else in the game world has been too dumb to consider it themselves.
B5 G‒
SSB+
If the GM would allow an unintelligent enemy to wield complex weaponry or magic, just to offer us a more challenging fight, I’d be skeptical. If the GM wouldn’t soon offer a plausible explanation as to why the enemy knew how to use the ad-vanced tools, it would become harder for me to care about the setting, and I'd have less fun.
B6
SSB+
SSF‒
As a believable, functioning society, an in-game city should have citizens capable of dealing with important problems. Unless the player characters really are the best fit for the job, I’d expect NPCs to have begun elsewhere, perhaps even solved part of the mystery themselves. To me, it isn’t enough that a mission is given to players “just because”, even if the mission is exciting enough to build a campaign around.
B7 SSB‒ SAS+
If I were to play a campaign set in a premade setting that I am a big fan of, I would prefer the GM to be faithful to the premade setting, even at points where the orig-inal work may arguably have inconsistencies or oddities in its world-building.
B8 SCB‒ SAS+
For a campaign with a more serious tone, I’d dislike if a fellow player came with a very light-hearted character and vice versa. In my opinion, if a character breaks the tone assumed by the setting or campaign, that character will detract from play, and should preferably be tweaked to better fit in.
B9 SSI‒ N+
I am personally in favor of disregarding the rules at times where following them would be to miss a good storytelling moment. For instance, if characters very cen-tral to the plot should have died according to the rules, I am in favor of letting them survive anyway, if them being alive serves the story exploration better than them being dead.
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Q GNS Statement
B10 SSB‒ N+
In an otherwise medieval fantasy setting, it might be weird to include a technologi-cally advanced civilization of robots. However, if the robots aided one of the play-ers in the thematic exploration of what it means to be human, a question very cen-tral to their character, then I would quickly forgive the leap in world-building logic since its inclusion gave way to good storytelling.
B11 SSF‒ N+
I prefer when the story can be free to go wherever needed in the exploration of deeper topics, rather than being limited to a specific pre-defined situation, mis-sion, or activity. Instead of events unfolding only based on logical progression, ide-ally events are designed by both GM and players to challenge the beliefs of the characters and to pose questions about what really matters.
B12 SCB‒ N+
I’m fine with tweaking a character’s backstory on the fly or justifying their behav-iors in hindsight, if it helps us tell a more interesting story. To me, the original vi-sion for a character is secondary to actually telling a great story together. I’d prefer if all players prioritized interesting contributions to the storytelling when making their characters.
B13 **
It is important to me that all participants, including the GM, are happy with what happens in the campaign. I’d much rather find a compromise than hold on to prin-ciples and/or ideals. In the end, what’s most important is that everyone enjoys themselves and gets along.
B14 **
Certain principles and/or role-playing ideals are very important for my enjoyment of play. I wouldn’t want to be in a campaign where these ideals would need to be disregarded, even if it was for sake of everyone getting along and having fun, as playing without these principles would make it hard for me to have fun myself.
The reason for the pairwise format is because a dedicated TRPG player would
probably appreciate most GNS aspects if asked about them in isolation, as opposed to
asking for a choice between them at points where they come into conflict. This may
stem from the fact that TRPGs often incorporate all aspects of GNS to some degree
(White, 2020, Ch. 4). This would create answers at the upper end of the scale, which
would be harder to analyse. By avoiding this, it consequently becomes easier and
more reliable to ask whether the player’s group members would agree or disagree
with the statements.
The first column of the table contains the numbering of the statement. The GNS
statements in the original survey are numbered in the B-series and range from B1 to
B13 (B14 was added in the follow-up survey). For each statement, the respondents are
asked how much they agree with the statement on a 5-point Likert scale from 1 =
strongly disagree to 5 = strongly agree. The second column shows which two of the
seven GNS-categories that the statement concerns – one in a positive way (+) and the
other in opposition (‒). The legends of the categories are as in Table 4.
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Table 4: GNS categories derived from types and subtypes
G Gamism
SSI Simulationism: System Integrity
SSB Simulationism: Setting Believability
SSF Simulationism: Situation Focus
SCB Simulationism: Character Believability and Integrity
SAS Simulationism: Adherence to Source Material
N Narrativism
Since there are seven categories, there are (7∙6) / 2 = 21 possible pairwise statements.
This would have been too much since the entire survey (consisting of three parts) was
designed to take no more than 15 minutes to complete. This was felt to be the
maximum that could be asked from strangers on internet forums where the sampling
was to take place. Therefore, a selection had to be made where all categories were
covered. The goal was to cover the big and non-multi-faceted categories (G and N)
with 4‒5 statements each and then the various S-categories with at least 2‒3
statements each. Statements B13 and B14 are general statements that do not belong to
any category in particular.4 Table 5 maps out the positive vs. negative components of
each pairwise statement.
Table 5: Pairs of GNS categories in survey statements
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12
Gamism (G) ‒ + + ‒ Simulationism: System In-tegrity (SSI) ‒ + ‒ Simulationism: Setting Be-lievability (SSB) ‒ + + ‒ ‒ Simulationism: Situation Focus (SSF) ‒ ‒ Simulationism: Character Believability and Integrity + ‒ ‒ Simulationism: Adherence to Source Material (SAS) + +
Narrativism (N) ‒ + + + +
For each B-statement (B1‒B13), there was a corresponding C-question directly
following. The C-questions were always the same, asking the respondent about how
4 B14 was only present in the follow-up survey.
17
many fellow participants in the campaign he or she thought would share his or her
view on each of the B-statements. There were thus as many C-questions as there were
B-statements.5
Table 6: Group coherence questions
Q GNS Statement
C ** How many participants in your current campaign (except yourself) do you think would AGREE with your answer to the previous question?
D ** How many participants in your current campaign (except yourself) do you think would DISAGREE with your answer to the previous question?
In the analysis of the original survey, there could be seen some skew in the data that
could possibly emanate from response biases. There were two kinds of possible
biases. (i) The respondents might be more willing to agree to a statement than to
disagree. (ii) The respondents might be inclined to think that the other group members
agree with them, or the respondents might tend to answer about the group in general
rather than in relation to the particular statement in question. To investigate that, two
actions were taken prior to sending out the revised follow-up study. (i) For half of the
B-statements, the phrases were reversed, now supporting the previously opposed
GNS-category and vice versa. For the revised statements, the same C-questions were
kept as in the original study. (ii) For the other half, the original B-statements were
kept but the respondents were asked how many fellow participants in the campaign he
or she thought would disagree with his or her view on each of the non-reversed B-
statements. The revised question is called a D-question and is also shown in Table 6.
The survey was assembled with the GNS-statements preceding the FUN-statements.
There was a first part added before those sections, which contained some questions on
the background experiences of the respondents. The EXP questions are found in
Table 7.
5 Note that the C-questions ask how the respondent his/herself perceives his/her similarities to the
group regarding the pairwise stances in B-statements B1‒B13. This is not an attempt to ask the
respondent to characterise or judge the feelings of other members in group. It is only a measure of the
respondent’s own perception of coherence.
18
Table 7: EXP questions
Q Statement
A1 What game system is used in the campaign? (If you are running a fully homebrew system, just say so here, but see also the next question!)
A2 If you are running a fully homebrew system, please mention a published system similar to it. Otherwise, leave the field blank.
A3 For how long have you been playing this campaign?
A4 For how long have you been playing with the people in your group, give or take one member?
A5 What is your role in this campaign?
A6 How many participants are in the campaign, yourself and the GM included?
A7 How many hours is an average session in this campaign?
A8 For how many years have you been playing tabletop/PnP-RPGs?
A9 How many different tabletop/PnP-RPG systems have you played? (Looking for a number here :) )
In the follow-up survey, one more general statement B14 was added because the
answers to B13 were disproportionately ubiquitous compared to other questions,
which brought its validity into question. Thus, Figure 1 summarises the original and
the follow-up survey questionnaires.
Figure 1: Structure of original and follow-up surveys
EXP Questions A1-A9
GNS Statements B1-B13
Questions C1-C13
FUN Statements F1-F11
Original survey
EXP Questions A1-A9
GNS Statements B1-B14
Questions C/D1-C/D14
FUN Statements F1-F13
Follow-up survey
19
The surveys were anonymous. The only identification was that each respondent was
asked to enter a key phrase identifying the group they play in. This enabled the
grouping together of respondents from the same group without identifying any of
them as individuals and it also aided in finding duplicates.
The reliability of the total survey and its questions was primarily tested by doing two
surveys with small changes in between, plus a pilot to test the questionnaire out
before launching the first survey. The pilot responses were not included in the survey
and those participating in the pilot did not participate in the survey. As explained
above, in order to check for response bias, e.g. that respondents are inclined to be
positive in general to a topic they are interested in, half of the comparative GNS
statements were reversed in the follow-up study. This means that a high score on a
particular question would correspond to a low score in the follow-up survey and vice
versa. For the group coherence questions, all asked for the group agreeing with the
respondent in the first survey, while half was reversed in the second study, asking for
the group disagreeing with the respondent instead. This way, the reliability was tested.
The results are discussed in the section on data collection below.
The validity was addressed in several ways (Creswell, 2014, Ch. 8). Care was taken
during the pilot to ensure that the statements and questions were understood in the
way intended, and adjusted according to responses. The total time of responding was
kept to 15 minutes by keeping the number of statements and questions down to a total
of 45. This way, the quality of the responses should be kept up by minimising survey
fatigue. It was also stated in the beginning that the expected completion time was 15
minutes in order to set the expectations right and dissuade those with impatience or
limited time to take the survey, else possibly yielding less valid answers. The
respondents were drafted from several online forums, not only a few, for two reasons.
(i) To combat sample bias. To obtain a distribution across play systems that are as
representative of the population as possible. See Table 8 for the distribution of game
systems in the survey and in a market research compilation. With the exception of one
system, a balance was fairly well obtained. Homebrew systems were not collected by
the market research. (ii) To combat response bias. Broad participation was sought, but
the sample actually responding will invariably risk containing a bias toward
respondents with an interest in thinking about TRPG games in a more general way
20
than just playing. This risk for bias was alleviated by formulating the GNS statements
in a personal way, one more relatable to casual players without an explicit interest in
TRPG design or theory, and by showing an interest in the respondents’ TRPG
backgrounds. Finally, statistical conclusion validity was achieved by the very large
sample sizes obtained.
Table 8: Percentage of the most widespread systems in active play6
Survey ORR
Dungeons & Dragons 51.6% 53.9%
Pathfinder 3.7% 5.3%
Call of Cthulhu 2.8% 10.3%
Homebrew 3.8% N/A
Rest/uncategorised 38.0% 30.5%
Qualitative Interviews
Following the two surveys, semi-structured interviews were conducted to further
follow up on the themes of the surveys (Creswell, 2014, Ch. 9). The respondents were
sampled using convenience sampling from players known to the author. These were
the ones available without any remuneration to offer. The aim was for having two
players from each group sampled to get a deeper perspective of the group coherence
aspect. As a starting point, and to focus the interviews on the same topics as the
surveys, the respondents were asked to complete the same survey as the online
respondents had. The interview template was designed to follow up on their survey
replies, by asking general clarifying questions but also going deeper on some aspects.
The reliability and validity of qualitative data collection differ from quantitative in
several respects. The design of the interview part of the study followed (Creswell,
2014) but due to the character of the interviews being more following up than
exploring new, and with a small sample size, the applicable reliability and validity
measures from (Creswell, 2014, Ch. 9) were employed. In particular, one purpose of
the interview study was to be able to verify the quantitative results by using
qualitative data sources. This strengthens the overall study. The study also used data
6 ORR is the ORR Group Industry Report 2020 (https://blog.roll20.net/posts/the-orr-group-industry-
report-q4-2020-8-million-users-edition/). While this reports the usage of a digital platform facilitating
TRPG play, it is the closest proxy found to a market share distribution. The list may seem short, but
apart from the entries in the table, all other game systems have 1% of the market share or less.
21
checking by allowing the interviewees to react to anything unclear in the preceding
survey they took and checking whether they felt that the interpretations of their
answers were accurate. Further, the interviewer declared the purpose of the interview
and the interest in group coherence in the beginning, so as to clarify the purposes of
the study.
Data Collection
The original survey was posted on five Reddit forums that were identified for having
a large number of active TRPG-players. Table 9 shows the forums that were targeted
with a post on Dec 22, 2020. The survey was open until Jan 5, 2021, with one
reminder posted in each forum on Jan 3, 2021. There were 1179 respondents
completing the survey during the time period it was open. Of these, 6 were found to
be duplicates or unreadable, so the total number of valid responses was 1173.
Table 9: Reddit forums targeted for the original study
https://www.reddit.com/r/osr/
https://www.reddit.com/r/DnD5e/
https://www.reddit.com/r/savageworlds/
https://www.reddit.com/r/DungeonWorld
https://www.reddit.com/r/ApocalypseWorld/
After having started to analyse the material received, there were a couple of things
that needed to be followed up. They were, in no particular order (some have been
mentioned above):
o The respondents might be more willing to agree to a statement than to
disagree.
o The respondents might be inclined to think that the other group members agree
with them more than they actually do.
o The respondents might tend to answer about the group in general rather than in
relation to the particular statement in question.
o All three statements in the FUN part, statements F2, F3, and F8, that were of
the “negative” kind, i.e. scoring high on them meant discontentment, had
almost no correlation with anything else. On the other hand, the other eight
statements that were of the “positive” kind, i.e. scoring high on them meant
22
contentment, had a lot of correlation with other variables.
Thus, a follow-up survey was posted on six other Reddit forums that were also identi-
fied for having a fairly large number of active TRPG-players. Table 10 shows the
forums that were targeted with a post on Feb 6, 2021. The survey was open until Feb
25, 2021, with one reminder posted in each forum on Feb 21, 2021. There were 816
respondents completing the survey during the time period it was open. Of these, seven
were found to be duplicates or unreadable, so the total number of valid responses was
809. The respondents of the follow-up survey were specifically instructed not to
answer if they had already answered the original study. No duplicates between the
two surveys were found, making the total number of valid respondents 1,982 yielding
over 92,000 data points to be analysed.
Table 10: Reddit forums targeted for the follow-up study
https://www.reddit.com/r/rpg/
https://www.reddit.com/r/RPGdesign/
https://www.reddit.com/r/callofcthulhu/
https://www.reddit.com/r/Shadowrun/
https://www.reddit.com/r/DungeonMasters/
https://www.reddit.com/r/PBtA/
Finally, developed in parallel with and based on the results of the two surveys, a set of
semi-structured deeper interviews were conducted with six TRPG players. These were
sampled using convenience sampling from TRPG groups in the internet vicinity of the
author. There were two players each from two of the selected groups and one GM per
group from two more groups, and the interviews were conducted between Feb 21 and
Feb 28, 2021, except one delayed until Mar 3. The respondents were asked to
complete the same survey as the online respondents prior to the interview in order to
create a baseline and have the same basis for discussion as the online respondents
were considering when answering the survey from the forums. The interview template
consisted of following up on their replies to the survey, and also by focusing on two
themes that went deeper. The themes were (i) how integral to their enjoyment each
stated GNS preference actually was and (ii) how confident they felt judging their own
perception of other group members’ attitudes toward the GNS statements. This way,
qualitative data could be added to clarify and strengthen the results of the analysis of
23
the survey, and a deeper understanding of how the statements and questions had been
interpreted was also gained. The interviewer (thesis author) recorded the dialogues in
a written format during the discussions and went through the results for completeness
after the interviews. By adhering to the structure of the survey at the beginning of the
sessions, the replies from the interviews were to a large part usable in the final part of
the data analysis.
Data Analysis
The surveys were made using Google Forms7 and the resulting data was delivered in
Google Sheets format which is a standard spreadsheet format. Each respondent
occupied one row, and the two sheets thus had around one thousand rows each.
Unfortunately, Forms did not allow for advanced kinds of input data checks. Thus,
some input fields such as A3 (For how long have you been playing this campaign) or
A4 (For how long have you been playing with the people in your group) had a
mixture of replies in years, months, weeks, rounds, or other scales. Some other fields
were misinterpreted by Forms to contain dates or other custom data. It took a fairly
long time of data transformations to reach consistent data sheets to be further
analysed. In the transformations, duplicates and invalid responses were removed.
There was also a response bias check performed. Since, as described above, half of
the GNS statements and half of the group coherence questions were reversed in the
follow-up survey, the analyses should also be reversed. The sample sizes in both the
original and the follow-up studies were large enough for such a comparison to take
place. For the 5-point Likert scales used for both the GNS statements and the group
coherence questions, the sum of the average of the entries from the original and the
reversed responses should be the sum of the endpoints of the scales if there is no
response bias present. For the GNS statements this means 1+5 = 6, and for the group
coherence questions 0%+100% = 100% = 1. In Tables 11 and 12, the sum of the
averages for the original and reversed statements and questions are shown.
7 https://www.google.com/forms/about/
24
Table 11: Sum of averages for the original and reversed statements
GNS B1 B5 B7 B8 B9 B10 Avg. sum 6.133 6.175 6.753 6.571 6.380 6.264
Bias 0.133 0.175 0.753 0.571 0.380 0.264
Table 12: Sum of averages for the original and reversed questions
Group C2 C3 C4 C6 C11 C12 C13 Avg. sum 1.015 1.184 1.105 1.066 1.193 1.139 1.345
Bias 0.015 0.184 0.105 0.066 0.193 0.139 0.345
In Table 11, the sum of the two averages should be 6. There is a small bias that
becomes noticeable in statements B7 (SSB‒ SAS+) and B8 (SCB‒ SAS+). This means
that the respondents were somewhat inclined to agree with both the original statement
and the reversed one. On a scale with the width of 4 (max-point 5, min-point 1), this is
a 9% bias for B7, a 7% bias for B8, and less for the others. The common denominator
of these two questions (B7 and B8) is that they are the only ones containing GNS-
SAS (Simulationism – Adherence to Source Material) statements. Such statements
seem to incur a bias easier than other questions.
On the other hand, in Table 12, the sum of the two averages should be 1. Also here,
there is a small bias which becomes larger especially in question C13. On a scale with
the width of 1 (max-point 1, min-point 0), this is a 17% bias. This is the only bias in
the survey that puts the responses into some doubt. Recall that question C13 did not
pertain to a particular statement from the GNS suite but was a general “catch-all”
question (B13, marked ‘**’ in Table 3). This question and its replies will
consequently have less weight in the final analysis of the thesis results.
The end result from the transformations was the first baseline for each survey. These
were then locked and never subsequently changed.
The survey material has been analysed using the statistical package jamovi.8 This is a
freeware package also including the statistics language R, but the package requires no
knowledge of R since it presents a complete graphics interface to all functionality.
(Jamovi Project, 2020). The version available at download time was 1.2.27. The
8 https://www.jamovi.org/
25
current version is 1.6.15, but the functions of the former were sufficient for the
purposes of this thesis. The package is also supplemented with a statistics textbook as
a tutorial (Navarro and Foxcroft, 2019).
A preliminary analysis was made on the first, original survey. The analysis showed
some concerns listed above, and a follow-up survey was launched. During the follow-
up survey, the original survey was analysed in more detail. When the follow-up
survey closed, its data and findings were added to the total analysis. The description
below will focus on the analysis of the original survey and comments will be made if
and when the follow-up study could shed more light on any question even though the
follow-up survey carried equal weight in determining the end results of the thesis.
When the dataset from Google Sheets had been transformed where necessary and
solidified, it was made a first baseline and locked. The baseline also contained some
aggregate statistics on each of the variables, see Appendix B.
This baseline was then fed into the software package jamovi which uses another,
internal format separate from Sheets or Excel. jamovi’s automatic data classifier made
some erroneous guesses, so a bit of manual labour was required in order to start
working with the package. Once accepted and verified, the internal format of jamovi
became the second baseline for the datasets and they were again locked. In jamovi,
two types of analyses were performed: correlation analyses and factor
analyses/principal component analyses (PCA), both using the suggested default
settings in each function.
The output from the correlation analysis in jamovi is complex. It shows how each pair
of variables correlate in terms of how much they tend to follow each other, i.e. if one
increases then the other is more likely than not to also increase (positive correlation)
or decrease (negative correlation). If they show no such tendency, then the correlation
is zero. Each correlation value is then also assigned a p-value, telling how probable it
is that the correlation value shown is only due to randomness, i.e. that it only
happened to become so without any underlying cause (statistical significance). The
three levels of significance jamovi displays are p<5%, p<1%, and p<0.1%
respectively. For readability and for aiding the analyses, these levels of significance
26
have been manually colour coded in the output file when re-exported to Google
Sheets/Excel. Green signifies p<0.1% (less than one chance in a thousand of
randomness) and yellow signifies p<1% (less than one chance in a hundred of
randomness). Correlations either being only p<5% or not even that are left white and
are considered as “no correlation found” in this analysis. This way, the two numbers
representing correlation and significance/p-value have been combined into one cell,
greatly enhancing the ease of analysis. In the correlation tables, there are two further
colours. Blue fields signify stronger correlation, with not only p<0.1% but also a
correlation value of 0.175 or higher – but only in one part of the matrix: that which
compares GNS-variables with FUN-variables (i.e. B/C versus F). Likewise, orange
signifies a stronger correlation, with not only p<0.1% but also with a correlation value
of ‒0.175 or less in the comparison of B/C variables with each other. Appendix C
shows the colour-coded correlation matrix divided into three parts (it is too big to
show in one piece) and also the original output matrix from jamovi complete with the
actual p-values (also in three parts).
It is important to keep the two concepts of relevance and significance apart.
Relevance means that the correlation is far enough from zero to indicate that there is
some commonality underlying them leading them to following each other together, in
the same or opposite directions. While significance means that there is a negligible
risk that the numbers seen are an act of randomness. Since there are so many samples
in these datasets, a lot of correlations will relatively easily become significant. But a
small correlation is uninteresting, however secured it is from a statistical point of
view. It still does not say anything interesting – rather we are able to say something
uninteresting with confidence. Having noted that, in the analysis, the correlations will
be classified by using a notation as follows:
if corr < 0.1 “no correlation”
else if corr < 0.2 “correlation”
else if corr < 0.3 “clear correlation”
else if corr < 0.45 “strong correlation”
else “very strong correlation”
and the same for negative numbers which will be prefixed with “negative” such as in
“clear negative correlation” for, e.g., ‒0.235.
27
The correlation matrix will now be analysed block by block, starting with EXP vs.
EXP (this will be the terminology in analysing the blocks).
EXP vs. EXP
As expected, there are strong and very strong correlations among some of the
variables, such as between A8 (For how many years have you been playing
tabletop/PnP-RPGs) and A9 (How many different tabletop/PnP-RPG systems have
you played), or between A3 (For how long have you been playing this campaign) and
A4 (For how long have you been playing with the people in your group), but no
correlation among some of the others. This is all as expected. No surprising or
noteworthy correlations were found in this block.
EXP vs. GNS
There are very few correlations to be found here. That means that a player’s stance on
GNS matters is not to any noticeable degree dependent on the level of experience, but
rather on preferences and possibly traits. The only notable correlations are that A8
(For how many years have you been playing tabletop/PnP-RPGs) and/or A9 (How
many different tabletop/PnP-RPG systems have you played) correlates negatively
with B3 (G+ SSI‒), B9 (SSI‒ N+), and B12 (SCB‒ N+), and positively with B8 (SCB‒
SAS+).9 Thus, the N (Narrativist) and G (Gamist) factors seem to become a little less
important to those with more TRPG experience.
EXP vs. FUN
There are even fewer correlations to be found here. That means that a player’s
satisfaction with playing is not to any noticeable degree dependent on experience, but
on other factors. Again, the only notable correlations are that A8 (For how many years
have you been playing tabletop/PnP-RPGs) and/or A9 (How many different
tabletop/PnP-RPG systems have you played) correlates negatively with a FUN-factor,
namely F5 (I enjoy playing my character(s) a lot). This is a borderline correlation but
9 The correlation with B13/C13 is not taken as much into account.
28
it is the only one in this block and it was not vindicated in the follow-up study.
GNS vs. GNS
The B-statements are best analysed together due to the fact that they were formulated
as opposing pairs of GNS factors. The pairing yields a much higher response quality
(White, 2020, Ch. 4) but it also somewhat complicates the analysis since each
statement contains two factors and there are no indications as to whether it is the
“positiveness” of a positive factor or the “negativeness” of a negative factor that
contribute the most to the ratings by the respondents. For this reason, and to aid
separability, it was assured that each factor occurred in several pairs, see Table 5.
The GNS block contains both GNS statements and group coherence questions. Thus,
this block must be analysed with some care since there are a lot of correlations, some
in distinguishable patterns. The first observation is that almost all group coherence
(C-numbered) questions correlate with each other, many of them even clearly. That
could mean that respondents have a tendency to judge the group’s similarities to
his/her own views at a more general level than only the specific statement at hand.
This was then investigated to see if some causes could be uncovered. By design, the
follow-up survey had half of its group questions reversed, asking the respondents how
many they thought would disagree in the group instead. Now an even stronger but
somewhat different pattern emerged. The correlations among disagree-formulated
group questions (D-numbered) were very high, being strong or in some cases even
very strong. This was a higher correlation than before (in the original survey). The
correlations among agree-formulated group questions (C-numbered) were as before,
being clear but not strong. They stayed almost the same as in the original survey. The
most noticeable fact was that the correlations between C- and D-questions were much
lower – a fact that needs further investigation in future research.
For the pure GNS statements (B-numbered), there is a clear classification among
them. Some have little correlation with other B-statements and seem to “stand on their
own two feet”. Others have stronger bonds with each other in correlation clusters.
This is most easily studied with a factor analysis to see which ones are strongly
29
linked. A standard PCA (principal component) analysis in jamovi, run on only the B-
variables, yields the following table (Table 13).
Table 13: PCA analysis of responses to GNS statements (from jamovi)
Principal Component Analysis
Component Loadings
Component
1 2 3 4 Uniqueness
B1 0.521 0.683
B2 -0.689 0.441
B3 0.740 0.394
B4 0.577 0.660
B5 0.713 0.459
B6 0.681 0.446
B7 0.417 0.732
B8 -0.679 0.463
B9 0.724 0.414
B10 0.403 -0.413 0.564
B11 0.616 0.482
B12 0.484 0.662
B13 0.303 0.416 0.629
The most important components or clusters are the highest-ranked ones, in this case
the two first. The first (component 1) contains the B-variables that correlate the most
among each other, followed by component 2.
Table 14: Correlation matrix for GNS statements
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12
B2 -0.187 —
B3 0.070 0.086 —
B4 -0.002 0.069 0.037 —
B5 0.065 -0.016 -0.039 -0.016 —
B6 0.055 0.004 0.032 0.039 0.268 —
B7 0.019 0.048 -0.020 0.018 0.118 0.089 —
B8 0.040 -0.033 -0.076 -0.071 0.141 0.009 0.089 —
B9 0.133 -0.079 0.327 -0.073 -0.098 -0.040 0.008 -0.081 —
B10 0.076 -0.150 0.096 0.016 -0.199 -0.110 -0.060 -0.122 0.267 —
B11 0.175 -0.206 0.161 0.046 0.003 0.118 -0.039 -0.065 0.191 0.235 —
B12 0.123 -0.106 0.053 0.041 -0.046 -0.021 -0.059 0.039 0.201 0.201 0.223 —
B13 0.058 -0.080 0.089 -0.018 -0.077 -0.037 -0.024 0.038 0.230 0.149 0.177 0.252
30
That can also be seen in a recoloured cut-out from the correlation matrix in Appendix
C, see Table 14. Here, green cells signify positive correlations with p<0.1% and
orange cells signify negative correlations with p<0.1%.
Next, these clusters are compared to the GNS categories that they represent. This
gives a picture of which GNS categories are the most coherent. In Table 15, the two
clusters and the rest from the PCA are mapped to their respective GNS categories.
The dominant categories in cluster 1 are GNS-G and GNS-N. Note that GNS-G is
negative in statement B1 and positive in B2. Consequently, there is a negative
correlation between them in Table 14. Similarly, B2 with a negative GNS-N
correlates negatively to all of B10‒B12. Thus, GNS-G (Gamist) and GNS-N
(Narrativist) are the clearly dominating categories in cluster 1. It can be seen that the
categories Gamism and Narrativism oppose each other in this cluster. The various
GNS-S (Simulationist) categories, on the other hand, have less impact on cluster 1.
Table 15: Clustering of responses to GNS statements (from jamovi)
Cluster 1
B1 G‒ SCB+
B2 G+ N‒ B10 SSB‒ N+ B11 SSF‒ N+
B12 SCB‒ N+
Cluster 2 B3 G+ SSI‒
B9 SSI‒ N+
Non-cluster
B4 SSI+ SSB‒
B5 G‒ SSB+
B6 SSB+ SSF‒
B7 SSB‒ SAS+
B8 SCB‒ SAS+
In cluster 2, the common denominator is SSI‒ which keeps B3 and B9 together in a
clear correlation. The rest (non-cluster) is a mix of GNS-S factors that do not cluster
together in the PCA and thus have less impact in this part of the analysis. This means
that the System Integrity aspects of Simulationism (from the second cluster) is an
important subcategory. For the other aspects of Simulationism, no such claim can be
made from analysing the PCA.
31
The fact that it was possible to find the first two clusters and identify that most of G
(Gamism) and N (Narrativism) components fall into the first cluster points to the GNS
typology being a working and useful concept. The five Simulationism components are
harder to place anywhere from the PCA, but an extended analysis (see Appendix F)
reveals that, from a correlation analysis, SSB and SSI seem to be viable concepts
while SSF, SCB, and SAS seem to be weaker categories. This can partly be explained
by there being a total of five subcomponents of the S component, which could be too
fine-granulated. This suggests that the particular division into GNS-S subcategories
should be investigated further in order to clarify the GNS typology among the
preferences of TRPG players.
GNS vs. FUN
As noted above, the three negatively phrased FUN statements have no impact at all.
They do not correlate to anything (except among themselves) and are thus excluded
from further analyses. Then it remains eight factors in the original study. For this
reason, two new negative FUN statements F12 and F13 were introduced in the follow-
up survey. Both fared much better, showing that the negative/inverse format in itself
is not the culprit. In addition, Tychsen et al. (2008) did not encounter any such
problems in their larger set of FUN statements.
Table 16: GNS statements’ correlation to FUN
Clear corre-lation
B1 G‒ SCB+
B3 G+ SSI‒
B11 SSF‒ N+
Correlation B10 SSB‒ N+
Unclear cor-relation
B2 G+ N‒
B3 G+ SSI‒
B4 SSI+ SSB‒
B5 G‒ SSB+
B6 SSB+ SSF‒
B7 SSB‒ SAS+
B8 SCB‒ SAS+
B9 SSI‒ N+
B12 SCB‒ N+
32
From the original survey, there are, in this section, three sets of GNS statements that
correlate to a varying degree with the FUN statements. The sets can be seen in the
correlation matrix in Appendix C and are shown in Table 16.
This shows how agreeing with some of the B-statements results, on average, in more
satisfying gameplay. When discussing the B-statements in the interviews, this table is
illuminated from another angle. The respondents in the interviews were asked if any
B-statements were hard to assess. The statements indicated as being hard by 3 of 6
respondents were B10 and B14. No other statements received more than one vote.
B10 was considered wordy (it is long, but not the longest – B6 is longer but received
no votes). It was also considered a bit confusing and some even disagreed with the
question’s premise, that an advanced civilization would necessarily affect the rest of
the setting. Thus, the results for B10 are a bit weaker. For B14, the phrasing “certain
principles” seem to open for respondents to read in various instantiations and it was
felt to be confusing as well. Since B14 was not in the original study and was found to
be weak, it was dropped from further study.
The respondents in the interviews were also asked how much the content of each B-
statement mattered to them personally. The statements indicated as important by at
least 3 of 6 respondents were B1, B9, B11, and B12. B10 got 2 votes and the other
statements fewer. This does not influence the confidence in the correlations in Table
16 but is rather another dimension in the analysis. Analysing the interview material
further, it was found that there was a strong correlation between how important the
respondents felt that the statements were and how hard it was to answer. Statements
that mattered less were substantially easier to respond to. There was also a strong
correlation between responding at the endpoints of the Likert scale (“strongly agree”
and “strongly disagree”) and how hard it was to answer. Responses that were closer to
the middle of the scale were substantially easier to respond to. But since this result
was not a part of the research question of the thesis, the jamovi correlation matrix is
presented in Appendix E and the result does not appear in the Discussion or
Conclusion sections.
There are also correlations between FUN and group coherence. This relationship is in
33
general stronger, for two reasons. First, the correlation numbers are higher, placing
seven C-questions in the highest group.10
Table 17: Group coherence statements’ correlation to FUN
Correlation
C1 G‒ SCB+
C3 G+ SSI‒
C6 SSB+ SSF‒
C8 SCB‒ SAS+
C9 SSI‒ N+
C11 SSF‒ N+
C12 SCB‒ N+
Unclear cor-relation
C2 G+ N‒
C4 SSI+ SSB‒
C5 G‒ SSB+
C7 SSB‒ SAS+
C10 SSB‒ N+
But there is another contributing factor as well: From earlier in the analysis, it was
seen that the group coherence factors correlated among each other, meaning that they
tend to be partly a judgement of the respondent’s perception of the group more as a
whole. Regarding the C-questions, the respondents in the interviews found them easy
to answer. All respondents had one C-question labelled hard, except for one that had
two. The hardness was not attributed to any question in particular. By following up on
that, it became clear that with one exception of one respondent, they had understood
the questions as intended.11 This indicates that the C-questions have high validity.
To sum up, for each B-statement in the first group of Table 16, favouring the plus
factor over the minus factor contributes on average to greater satisfaction. The picture
is not entirely clear, since GNS-G appears in both a negative and a positive statement.
However, this might be due to the nature of each statement’s respective expression of
GNS, B1 focusing specifically on valuing tactics while B3 could be interpreted more
generally as valuing creativity. For group coherence, the picture is much clearer
(Table 17). The seven group coherence variables that correlate with FUN all do so
more strongly than the strongest correlation among the B-statements. It is a clear
10 Again, B13/C13 were excluded from the analyses. 11 To the contrary, as noted in the section Interviews below, the D-questions were often misunderstood.
34
indication that group coherence matters when seven of the 12 variables correlate. The
group coherence C13 for the general statement B13 also correlates, making the count
eight, but recalling the possibly flagged bias for C13 it is not included in the table. In
the follow-up survey, the statement B14 was added as a new “catch-all” statement
since B13 did not perform as effectively as expected. It can be seen that B14/C14
correlates as strongly as the top cluster in Table 17. Tables 16 and 17 are summarised
together with the statements spelled out in clear text in Appendix D.
Interviews
Many of the results of the interviews are discussed above in relation to the analysis
block they relate to. Here, a summary of the entire set of interviews is given. Six
interviews were conducted as a final follow-up of the surveys. The lengths of the
interviews ranged between 90 and 140 minutes. The interview sessions were preceded
by the respondents answering the same online survey as the other respondents (they
took the follow-up survey due to that being current when the interviews were being
scheduled). Their responses were followed up and discussed along with any problems
or misconceptions they might have felt during responding to the survey questions in
the questionnaire. On average, the respondents of the interviews found 5‒9 of the 45
questions harder to answer than the others. For each question in the survey, the
interview respondent was asked to rank how hard it was to answer on a scale from 1
to 10. Responses over 5 were recorded as “hard”. The average number of perceived
hard questions (either hard to understand or hard to answer) was 4.3 with a standard
deviation of 2.2. But the hard questions were not the same among the respondents,
pointing to personal interpretations rather than flaws in particular questions being the
factor behind the difficulties in answering. Due to the very large number of
respondents to the survey in total, this does not seem to be a threat to the validity of
the results. Since interviewing 1,982 respondents was out of the question, the results
in this thesis are as good as it gets given the time and resources available.
The responses confirmed the relevance of the questions, and it was uncomplicated to
discuss the issues probed. This tends to show that the overall design of the survey is
of relevance to the TRPG community and from the respondents’ reactions, the results
35
could be useful in future gameplay. The questions that were perceived to be hard for
at least half of the interview respondents were D4 (5 out of 6) and D3 (3 out of 6).
These come from the ‘disagree’ questions on group coherence, and once more
underline the fact that respondents found it harder to judge disagreement with their
group than agreement. But since all of the group coherence questions in the original
study were of the ‘agreement’ kind, and half of the questions in the follow-up study,
this does not constitute any serious problem to the results of the thesis. The follow-up
study was designed for the exact purpose of finding out how response patterns might
influence the results. But given the interview responses, the survey results for the D-
questions have not been taken into consideration in the final analysis. This reduced
the remaining number of factors/variables (statements and questions) by 6 from 45 to
39 in the follow-up survey.12 By this removal, the average number of perceived hard
questions remaining from the interviews was reduced to 1.8 with a standard deviation
of 1.0. This points to an increase in quality when the D-questions were removed.
In addition, the interview respondents were asked whether or not some campaigns
they had participated in had died in their infancy, thus in effect not even reaching the
point where they could be included in a study like this. The most common answer
seemed to be that up to half of all campaigns do indeed die young, sometimes due to
mismatch in interests or perceived lack of group chemistry between participants. This
does not constitute a threat to the validity of the results in the thesis since there was no
indication of specific types of campaigns failing, such as being of particular GNS
types.
Factor Analysis
Finally in the quantitative analysis, a factor analysis (PCA) was run on the entire set
of 45 survey variables. This analysis confirmed that the three sets of variables from
the three parts of the survey should be seen as separate sets of data.
12 The original survey did not contain any D-questions and was thus not affected by the removal.
36
Table 18: PCA analysis of all variables
Principal Component Analysis
Component Loadings
Component
1 2 3 4 5 6 7 Uniqueness
B1 0.774
B2 -0.635 0.579
B3 0.766 0.353
B4 -0.424 0.726
B5 0.655 0.545
B6 0.708 0.446
B7 0.384 0.731
B8 0.407 0.741
B9 0.627 0.529
B10 0.364 -0.363 0.639
B11 0.442 0.671
B12 0.466 0.663
B13 -0.345 -0.343 0.589
F1 0.682 0.425
F2 -0.323 0.364 0.706
F3 -0.674 0.506
F4 0.394 0.582 0.488
F5 0.721 0.444
F6 0.607 0.513
F7 0.588 0.495
F8 -0.342 0.730
F9 0.731 0.437
F10 0.516 0.707
F11 0.741 0.424
A3 0.415 0.807
A4 0.672 0.513
A6 0.866
A7 0.939
A8 0.776 0.364
A9 0.627 0.525
C1 0.482 0.312 0.615
C2 0.458 -0.430 0.532
C3 0.653 0.452
C4 0.574 0.518
C5 0.558 0.652
C6 0.523 0.623
C7 0.485 0.707
37
Component Loadings
Component
1 2 3 4 5 6 7 Uniqueness
C8 0.627 0.517
C9 0.564 0.374 0.513
C10 0.605 0.595
C11 0.570 0.611
C12 0.512 0.593
C13 0.388 0.579
The first cluster contains all of the FUN factors, except for F3 and F8 which are the
weakest and have already been excluded from the preceding analyses together with F2
which has a large negative value in the PCA. This result confirms the previous
analyses and is a good indicator that the FUN construct works as intended in the same
way as in (Tychsen et al., 2008). The second cluster contains the group coherence
variables except for C3. C3 was probably excluded by the PCA procedure because it
has the least uniqueness among the variables, i.e. the result suffers the least from
removing this variable. This does not, however, indicate that C3 fares worse in a
correlation analysis, and it has been kept throughout the analyses. The third cluster
contains the “age/experience” variables from the EXP section, i.e. those that tend to
increase with the number of years a respondent have played. But they look at different
aspects of experience and have all been used in the analyses. The clusters/components
4-6 constitute another way of dividing the GNS statements into groups, but the result
from the above PCA analysis in Table 13 provides a clearer picture. Here, cluster 4
contains only B3 and B9 (together with C3 and C9). Those are the two variables that
contain the category GNS-SSI‒, so it is no surprise that they have been clustered
together. Cluster 5 here is similar to cluster 1 in Table 13, and cluster 6 here is similar
to cluster 1 in that table. So the results coincide fairly well except for the fact that B3
and B9 were being separated out.
38
Results
The research questions (i) How do player typology and play preferences affect the
enjoyment of play in tabletop/pen-and-paper playing? and (ii) How does perceived
group coherence in player typology and play preferences affect the enjoyment of play
in role-playing? can be answered as follows:
(i-a) Previous experiences (length and/or depth) have little to do with gameplay
satisfaction. The number of years playing and the number of game systems played
even correlates slightly negatively with the one satisfaction factor, namely “I enjoy
playing my character(s) a lot”. Regarding the emphasis on different aspects of TRPG
gaming, the results indicating that the Gamism and Narrativism factors seem to
become a little less important to those with increased TRPG experience.
(i-b) Looking at preferences for different factors in Table 15, it can be seen that a
preference for Gamism often coincides with less of a preference for Narrativism and
vice versa. For the five different aspects of Simulationism, no such combination of
categories is evident. Further, in Table 16, it was seen that responses to 3-4 paired
statements in themselves were correlated to enjoyment. But the categories appearing
on top in that table shows no clear pattern.
(ii) There is an indication that group coherence matters in seven of the 12 pairs of
GNS aspects investigated. This means that having a similar stance as the group is
important for most of the GNS aspects included in the study: Gamism, Narrativism,
and four of the five aspects of Simulationism. No such claim can, however, be made
for the Setting Believability subcategory (marked in orange in Table 5).
39
Discussion
This work uses the GNS framework for characterising the player typology aspects.
The GNS categorisation of player types underlays the formation of the statements in
the middle part of the survey.
The research approach, with a survey at the core for reaching TRPG players from all
over the world, has been proven a successful communication method in this
community. The number of respondents became far higher than anticipated, and the
sheer volume of the responses aids in obtaining good results since interesting
correlations were also always statistically significant at the 99.9% level (p<0.001) or
better.
The research design is partly borrowed from an earlier investigation on role-playing
games (Tychsen et al., 2008). That study investigated the enjoyment of three different
RPGs – of which a TRPG was one. While that study was smaller, comprising only 56
people in total, the research design carried over well to this thesis. The design was
also influenced by a study on improvisational theatre where 20 staff and students at a
university played a short improvisational piece and then the enjoyment was measured
with the FUN construct (Newman, 2005) that is also being used in this thesis.
The results in this thesis do not run counter to any previous research since, to the
author’s knowledge, an effort to try to answer this thesis’ research questions have not
been undertaken previously. An extensive literature search did not come up with
anything close to this. Regarding the results in relation to current typologies such as
GNS, the results tend to indirectly confirm the usability of GNS, at least regarding the
Gamism and Narrativism categories. As discussed in the analysis, the results for
Simulationism are more unclear13, possibly because the division into the five
subcategories could be done in another way – at least for an investigation such as this
one. Attempting to recategorise the Simulationism category within GNS constitutes
an interesting line of future research. Another interesting research idea would be to
connect the results on Narrativism to the Threefold Model’s category of Dramatism,
perhaps by a survey that as its result would end up in different clustering results
13 See Appendix F where two out of five subcategories were found to work well.
40
depending on whether Narrativism or Dramatism is the more effective categorisation.
Those two ideas could be combined into one subsequent survey.
While the theories on frames and frame analyses were a background against which the
statements in the survey were checked when they were designed, there are no results
in this investigation that pertains particularly to frames.
This data material is far from fully analysed. It is beyond the scope of a BSc thesis to
be able to do that, both in terms of time and in terms of statistical knowledge. This is,
again to the author’s knowledge, one of the largest datasets on TRPG gaming
collected, and should preferably be explored and expanded in cooperation with other
disciplines.
It would also be interesting to cooperate with researchers from psychology (especially
group psychology) to further investigate the group dynamics aspects of TRPG play,
and how this work on group coherence can be further investigated in such a context.
Another interesting direction would be to work together with researchers within
improvisational theatre in order to look at TRPG play, and group coherence in
particular, from their set of research tools and approaches.
41
Conclusions
This thesis investigates whether TRPG (tabletop role-playing, also known as pen-and-
paper role-playing) players’ types/characteristics and preferences affect the enjoyment
of play, and in particular whether perceiving to be coherent with the fellows in the
playing group in these respects affects the enjoyment of playing the TRPG. The
research was conducted between September 2020 and March 2021.
While there has been a lot of research on TRPGs in general, the group coherence
aspects seem to be rare or missing altogether. Knowing the impact of such factors
could give important clues to how to increase enjoyment.
The research questions were addressed by a survey among TRPG players that
received 1,982 completed questionnaires. These were then analysed by correlation
and factor analyses in order to find connections between variables. The questionnaire
consisted of three parts: the player’s previous experience with TRPGs, his/her stance
on what aspects of the play are important and how that corresponds to the group’s
views, and finally how enjoyable he/she finds the game to be. The survey was then
followed up with semi-structured interviews to gain a deeper understanding of some
aspects.
The thesis work shows that the length and depth of previous experiences with TRPGs
have almost no correlation to the level of satisfaction. A few variables, namely the
number of years playing TRPGs in total and the number of game systems played
(both of which could be considered seniority markers) correlated slightly negatively
with satisfaction. While this was not a strong indication, perhaps more interesting is
the fact that the correlation did not go the other way. There was no increase in
satisfaction with a larger total experience gained. One could presuppose that the more
you know about different game systems and the more you have played, the easier it
would be for you to select play systems, scenarios, and groups to your satisfaction.
But this was indicated against.
Another observation in this section of the survey was that the Gamism and Narra-
tivism factors of GNS seem to be a little less important to those more experienced
42
with TRPGs. A somewhat surprising fact was that B13/C13, the “catch-all” statement
(It is important to me that all participants, including the GM, are happy with what
happens in the campaign...) was negatively correlated with the total level of TRPG
experience. This indicates that the longer you have played TRPGs, the less you
emphasise the collective group’s overall satisfaction. Due to some problems with the
B13/C13 indicator (as noted in the data analysis), this conclusion should be taken
more as an interesting observation to be explored in further research rather than an
established fact.
The next set of results concerned which aspects of game playing were considered
more important. The first observation in this section was that a preference for Gamism
often coincides with less of a preference for Narrativism. This means that those two
aspects seem to be emphasised one over the other by players, i.e. that the players who
emphasise gameplay challenge seem not to also emphasise narrative as much, or vice
versa. This does not seem to come with any clear tendency to put comparatively more
or less emphasis on the Simulationism factors such as consistency of the game world
or the integrity of the game rules. This points to two different ways of appreciating the
gameplay: either the focus lies a bit more on the story or on an exciting gameplay
challenge, regardless of if it has some logical flaws seen from a world-building point
of view or if it lies somewhat more on the side of logic and consistency.
The clearest indications on what contributes to game playing satisfaction were found
in the section on group coherence – whether the players perceived the group to have
the same or similar stances as him/herself on game preferences. Group coherence
mattered in more than half of the GNS aspects investigated. This means that the
perception of having a similar position or viewpoint as the group is important for
many players. This and other facts found in this study can be used to design more
pleasing TPRG gaming experiences in the future as well as adding to the knowledge
of what underlies favourable experiences in TPRG gameplay.
43
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45
Appendix A
Acronyms used in the thesis.
A-question Question on previous experience in the EXP section
B-statement Statement comparing two GNS aspects with each other
C-question Question on whether the group is perceived to be coherent
Campaign Ongoing set of stories/adventures with the same or similar settings
CRPG Computer-based role-playing game
EXP The experience section of the survey
F-statement Statement measuring an aspect of the FUN construct
FUN Construct for measuring fun/enjoyment (also a section of the survey)
GM (or DM) Game master (or Dungeon master in the game Dungeons & Dragons)
GNS Gamism, Narrativism, and Simulationism (also a section of the survey)
LARP Live action role-play
MMORPG Massive multiplayer online role-playing game
OSR Old school renaissance
PCA Principal component analysis
PnP-RPG Pen-and-Paper role-playing game (a synonym for TRPG)
RGFA Usenet discussion group rec.games.frp.advocacy
RPG Role-playing game
SAS Adherence to source material (from Simulationism)
SCB Character believability and integrity (from Simulationism)
SSB Setting believability (from Simulationism)
SSF Situation focus (from Simulationism)
SSI System integrity (from Simulationism)
TRPG Tabletop role-playing game (sometimes TTRPG)
46
Appendix B
The number of non-blank data points and some basic statistics for each variable (ex-
cluding the blanks) in the dataset of the original survey. A blank means that the re-
spondent answered “I don’t know”.
Non-blanks Min Max Average St. dev. Non-blanks Min Max Average St. dev.
A3 1173 0.00 29.00 1.07 1.65 B9 1173 1.00 5.00 3.71 1.33
A4 1171 0.00 38.00 3.39 5.25 C9 985 0.00 1.00 0.71 0.24
A6 1173 0.00 45.00 5.76 2.52 B10 1173 1.00 5.00 3.26 1.30
A7 1164 0.00 13.00 3.90 1.40 C10 771 0.00 1.00 0.69 0.24
A8 1172 0.33 54.00 10.95 11.16 B11 1173 1.00 5.00 3.98 1.05
A9 1173 0.00 148.00 7.66 10.96 C11 909 0.00 1.00 0.74 0.22
B1 1173 1.00 5.00 4.41 0.72 B12 1173 1.00 5.00 4.16 0.98
C1 1148 0.00 1.00 0.71 0.22 C12 974 0.00 1.00 0.70 0.22
B2 1173 1.00 5.00 3.17 1.00 B13 1173 1.00 5.00 4.65 0.73
C2 929 0.00 1.00 0.60 0.23 C13 1107 0.00 1.00 0.90 0.18
B3 1173 1.00 5.00 4.25 0.97 F1 1173 1.00 5.00 4.32 0.80
C3 1043 0.00 1.00 0.80 0.22 F2 1173 1.00 5.00 2.09 1.02
B4 1173 1.00 5.00 3.81 1.16 F3 1173 1.00 5.00 2.17 1.20
C4 962 0.00 1.00 0.70 0.24 F4 1173 1.00 5.00 4.24 0.83
B5 1173 1.00 5.00 3.28 1.29 F5 1173 1.00 5.00 4.60 0.61
C5 936 0.00 1.00 0.67 0.24 F6 1173 1.00 5.00 4.04 0.87
B6 1173 1.00 5.00 3.57 1.12 F7 1173 1.00 5.00 3.97 0.89
C6 902 0.00 1.00 0.67 0.24 F8 1173 1.00 5.00 2.66 1.25
B7 1173 1.00 5.00 2.65 1.19 F9 1173 1.00 5.00 4.62 0.65
C7 782 0.00 1.00 0.67 0.25 F10 1173 1.00 5.00 3.74 1.20
B8 1173 1.00 5.00 3.37 1.31 F11 1173 1.00 5.00 4.48 0.75
C8 960 0.00 1.00 0.65 0.25
47
Appendix C Correlation matrix for the 45 variables of the original survey. First the colour-coded
version (in three parts), where the p-values are integrated with the correlations
(p<0.1% is green, p<1% is yellow), followed by the original output from jamovi with
the p-values separate. The information is exactly the same in both versions.
A3 A4 A6 A7 A8 A9
A3 —
A4 0.317 —
A6 0.141 0.002 —
A7 0.093 0.049 0.041 —
A8 0.211 0.457 0.049 0.006 —
A9 0.080 0.282 0.019 -0.047 0.475 —
B1 0.030 0.041 -0.083 0.007 0.064 0.050
C1 0.008 0.059 -0.078 0.020 0.026 0.084
B2 0.052 0.017 0.074 0.051 0.043 -0.089
C2 0.084 0.095 -0.003 0.007 0.108 0.024
B3 -0.025 -0.062 0.069 -0.016 -0.088 -0.174
C3 -0.020 0.034 -0.024 -0.067 -0.037 -0.012
B4 0.063 0.045 -0.005 -0.042 0.027 0.026
C4 0.056 0.088 -0.062 -0.065 0.047 0.043
B5 0.019 0.010 -0.015 0.005 0.077 0.006
C5 0.001 0.043 -0.052 -0.016 0.032 0.046
B6 0.045 0.024 0.022 0.063 0.030 -0.071
C6 0.040 0.051 -0.078 0.044 0.027 0.069
B7 0.012 0.049 -0.030 -0.012 0.036 -0.059
C7 -0.035 -0.011 -0.087 -0.049 -0.062 0.020
B8 0.001 0.124 -0.041 -0.012 0.142 0.137
C8 0.026 0.036 -0.039 -0.050 0.059 0.049
B9 -0.075 -0.028 -0.030 -0.014 -0.045 -0.141
C9 0.024 0.038 0.019 -0.048 -0.017 0.006
B10 -0.049 0.006 -0.053 -0.072 -0.057 -0.023
C10 0.021 0.010 -0.035 -0.035 -0.044 0.005
B11 -0.088 -0.068 -0.102 -0.033 -0.097 -0.045
C11 0.045 -0.012 -0.056 -0.031 -0.055 0.016
B12 -0.127 -0.073 -0.048 -0.060 -0.111 -0.016
C12 -0.010 0.039 -0.048 -0.054 0.015 0.040
B13 -0.047 -0.062 -0.035 0.000 -0.132 -0.103
C13 -0.019 -0.056 -0.050 0.011 -0.109 -0.094
F1 -0.005 0.010 -0.030 0.033 -0.040 -0.042
F2 -0.027 -0.067 -0.038 0.030 -0.050 -0.004
F3 -0.032 -0.071 0.034 -0.056 -0.073 -0.027
F4 0.016 0.013 -0.049 0.020 -0.041 -0.062
F5 0.034 -0.029 -0.009 0.058 -0.103 -0.097
F6 0.009 0.003 -0.012 -0.010 -0.051 -0.031
F7 0.013 0.012 -0.018 0.025 -0.031 -0.038
F8 0.010 0.019 0.002 0.030 -0.029 -0.083
F9 0.049 0.004 -0.025 0.041 -0.069 -0.075
F10 0.076 -0.025 0.016 0.045 -0.055 -0.085
F11 0.060 -0.015 -0.020 0.047 -0.060 -0.024
B1
C1
B2
C2
B3
C3
B4
C4
B5
C5
B6
C6
B7
C7
B8
C8
B9
C9
B1
0 C1
0 B1
—
C1
0.17
5 —
B2
-0
.187
-0
.059
—
C2
0.02
8 0.
172
0.23
9 —
B3
0.
070
-0.0
45
0.08
6 0.
026
—
C3
0.
080
0.12
5 -0
.005
0.
207
0.41
8 —
B4
-0
.002
-0
.027
0.
069
0.02
4 0.
037
0.01
6 —
C4
0.07
9 0.
200
-0.0
13
0.15
9 -0
.040
0.
185
0.34
9 —
B5
0.
065
0.05
2 -0
.016
0.
000
-0.0
39
-0.0
60
-0.0
16
-0.0
19
—
C5
0.
035
0.14
0 -0
.022
0.
180
-0.0
64
0.07
2 -0
.013
0.
240
0.01
8 —
B6
0.
055
0.04
6 0.
004
-0.0
49
0.03
2 0.
034
0.03
9 -0
.008
0.
268
-0.0
35
—
C6
0.
032
0.23
6 -0
.023
0.
177
-0.0
52
0.17
4 0.
012
0.22
2 -0
.025
0.
302
0.09
0 —
B7
0.
019
-0.0
18
0.04
8 -0
.007
-0
.020
-0
.073
0.
018
0.08
4 0.
118
-0.0
59
0.08
9 -0
.088
—
C7
0.06
1 0.
130
0.00
2 0.
168
-0.0
36
0.10
8 0.
026
0.17
8 -0
.059
0.
252
-0.0
48
0.20
0 -0
.214
—
B8
0.
040
0.04
0 -0
.033
-0
.012
-0
.076
-0
.017
-0
.071
-0
.081
0.
141
-0.0
45
0.00
9 0.
015
0.08
9 -0
.038
—
C8
0.10
9 0.
218
0.01
2 0.
248
-0.0
19
0.14
3 0.
059
0.24
7 0.
023
0.22
9 0.
002
0.25
3 -0
.050
0.
176
-0.0
28
—
B9
0.13
3 0.
015
-0.0
79
-0.0
09
0.32
7 0.
172
-0.0
73
0.03
1 -0
.098
-0
.006
-0
.040
0.
006
0.00
8 0.
063
-0.0
81
-0.0
06
—
C9
0.
095
0.14
2 0.
041
0.20
6 0.
108
0.28
2 0.
043
0.23
5 -0
.066
0.
136
0.01
3 0.
150
-0.0
37
0.22
1 -0
.041
0.
290
0.29
9 —
B1
0 0.
076
0.00
4 -0
.150
-0
.086
0.
096
0.03
7 0.
016
-0.0
23
-0.1
99
-0.0
39
-0.1
10
0.10
2 -0
.060
0.
004
-0.1
22
0.00
9 0.
267
0.10
6 —
C10
0.02
9 0.
164
-0.0
52
0.16
6 -0
.022
0.
094
0.07
3 0.
245
-0.0
79
0.20
2 -0
.020
0.
178
0.01
7 0.
239
-0.0
48
0.28
7 0.
070
0.29
4 0.
206
—
B11
0.17
5 0.
067
-0.2
06
-0.0
64
0.16
1 0.
118
0.04
6 -0
.001
0.
003
0.05
6 0.
118
0.03
7 -0
.039
0.
059
-0.0
65
0.05
9 0.
191
0.00
9 0.
235
0.02
3 C1
1 0.
102
0.23
6 -0
.040
0.
254
0.03
1 0.
214
0.07
5 0.
262
-0.0
30
0.17
6 -0
.026
0.
278
-0.0
84
0.24
1 -0
.052
0.
264
0.10
0 0.
223
0.02
3 0.
313
B12
0.12
3 0.
066
-0.1
06
-0.0
50
0.05
3 0.
030
0.04
1 0.
001
-0.0
46
0.03
9 -0
.021
0.
066
-0.0
59
0.04
1 0.
039
0.02
8 0.
201
0.07
0 0.
201
0.08
2 C1
2 0.
062
0.24
3 -0
.039
0.
191
0.04
4 0.
233
0.07
7 0.
289
-0.0
27
0.16
8 0.
002
0.26
8 -0
.076
0.
203
0.02
0 0.
227
0.08
9 0.
281
0.09
7 0.
315
B13
0.05
8 0.
064
-0.0
80
-0.0
29
0.08
9 0.
088
-0.0
18
-0.0
14
-0.0
77
0.03
0 -0
.037
0.
063
-0.0
24
0.04
9 0.
038
0.00
3 0.
230
0.13
7 0.
149
0.00
6 C1
3 0.
074
0.17
4 -0
.070
0.
118
0.05
8 0.
217
0.01
3 0.
141
-0.0
90
0.12
2 -0
.052
0.
160
-0.0
61
0.15
5 -0
.047
0.
135
0.11
8 0.
277
0.11
4 0.
176
F1
0.11
2 0.
097
0.04
8 0.
145
0.10
2 0.
135
0.05
5 0.
106
-0.0
83
0.06
6 -0
.009
0.
130
-0.0
46
0.11
0 -0
.075
0.
150
0.05
4 0.
188
0.08
3 0.
011
F2
-0.0
67
-0.0
51
-0.0
76
-0.1
07
0.00
0 -0
.067
-0
.018
-0
.031
0.
013
0.02
1 0.
010
-0.0
45
-0.0
35
-0.0
48
0.01
0 -0
.065
0.
002
-0.0
33
0.04
5 0.
001
F3
-0.0
55
-0.0
09
-0.0
06
-0.0
77
0.04
4 -0
.085
0.
010
-0.0
41
-0.0
16
-0.0
84
-0.0
06
-0.0
19
-0.0
09
-0.0
35
-0.0
64
-0.0
55
0.08
5 -0
.073
0.
025
0.01
4 F4
0.
169
0.04
3 -0
.080
0.
029
0.09
4 0.
104
-0.0
39
0.02
1 0.
001
0.07
3 0.
042
0.09
7 -0
.021
0.
082
0.04
4 0.
041
0.04
0 0.
115
0.10
7 0.
066
F5
0.13
7 0.
159
-0.0
13
0.10
0 0.
124
0.13
8 0.
031
0.02
6 -0
.022
0.
046
0.02
3 0.
151
-0.0
26
0.11
2 -0
.049
0.
076
0.07
5 0.
150
0.07
9 0.
015
F6
0.05
0 0.
284
0.00
1 0.
170
0.07
1 0.
169
0.03
6 0.
152
-0.0
33
0.12
7 0.
040
0.18
4 0.
004
0.16
9 -0
.092
0.
224
0.02
5 0.
155
0.04
8 0.
135
F7
0.10
6 0.
188
-0.0
78
0.14
4 0.
042
0.14
2 0.
007
0.06
0 0.
022
0.05
1 0.
051
0.19
6 -0
.034
0.
122
-0.0
16
0.12
4 0.
058
0.17
8 0.
079
0.09
0 F8
-0
.049
-0
.037
0.
078
-0.0
49
0.00
9 -0
.058
0.
028
-0.0
35
-0.0
10
-0.0
29
0.01
8 -0
.097
0.
053
-0.0
29
0.05
3 -0
.104
-0
.013
-0
.048
-0
.039
0.
006
F9
0.12
0 0.
113
0.02
2 0.
084
0.10
2 0.
149
0.05
6 0.
119
-0.0
37
0.04
8 0.
001
0.12
3 -0
.036
0.
123
-0.0
73
0.15
4 0.
025
0.11
6 0.
063
0.05
8 F1
0 0.
054
0.09
3 0.
018
0.00
1 0.
188
0.13
0 0.
020
0.00
4 -0
.019
0.
029
0.02
6 0.
104
-0.0
14
0.07
3 0.
002
0.08
5 0.
072
0.06
4 0.
049
0.03
0 F1
1 0.
130
0.18
5 -0
.011
0.
123
0.10
7 0.
172
0.02
6 0.
119
-0.0
72
0.07
5 0.
027
0.18
1 -0
.044
0.
171
-0.0
62
0.19
1 0.
051
0.16
8 0.
086
0.10
3
49
P N N P P P P N P P P B11 C11 B12 C12 B13 C13 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11
B11 — C11 0.313 — B12 0.223 0.081 — C12 0.103 0.372 0.256 — B13 0.177 0.112 0.252 0.071 — C13 0.063 0.257 0.082 0.254 0.476 — F1 0.121 0.149 0.057 0.129 0.116 0.161 — F2 0.003 -0.024 0.050 -0.063 0.068 -0.011 -0.203 — F3 -0.016 -0.071 0.013 -0.078 -0.012 -0.035 -0.183 0.117 — F4 0.131 0.130 0.025 0.087 0.095 0.121 0.265 -0.089 -0.394 — F5 0.101 0.152 0.045 0.086 0.094 0.164 0.383 -0.172 -0.032 0.265 — F6 0.081 0.250 0.058 0.216 0.114 0.211 0.288 -0.070 -0.085 0.194 0.287 — F7 0.094 0.168 0.056 0.134 0.071 0.111 0.302 -0.095 -0.130 0.292 0.373 0.298 — F8 -0.025 -0.084 -0.065 -0.067 0.003 -0.011 -0.051 -0.032 -0.008 -0.054 -0.069 -0.073 -0.170 — F9 0.110 0.176 0.043 0.159 0.049 0.154 0.495 -0.223 -0.139 0.239 0.460 0.344 0.335 -0.064 —
F10 0.109 0.112 -0.012 0.064 0.046 0.077 0.186 -0.088 -0.032 0.155 0.316 0.228 0.220 -0.043 0.301 — F11 0.130 0.186 0.046 0.156 0.078 0.202 0.395 -0.112 -0.033 0.186 0.437 0.445 0.378 -0.080 0.514 0.401 —
A3
A4
A6
A7
A8
A9
B1
C1
B2
C2
B3
C3
B4
C4
B5
C5
B6
C6
B7
C7
B8
C8
A3
Pear
son'
s r
—
p-va
lue
—
A4
Pe
arso
n's r
0.
317
—
p-
valu
e <
.001
—
A6
Pe
arso
n's r
0.
141
0.00
2 —
p-
valu
e <
.001
0.
937
—
A7
Pe
arso
n's r
0.
093
0.04
9 0.
041
—
p-
valu
e 0.
001
0.09
6 0.
163
—
A8
Pear
son'
s r
0.21
1 0.
457
0.04
9 0.
006
—
p-va
lue
< .0
01
< .0
01
0.09
4 0.
851
—
A9
Pe
arso
n's r
0.
080
0.28
2 0.
019
-0.0
47
0.47
5 —
p-va
lue
0.00
6 <
.001
0.
506
0.10
9 <
.001
—
B1
Pe
arso
n's r
0.
030
0.04
1 -0
.083
0.
007
0.06
4 0.
050
—
p-va
lue
0.31
0 0.
163
0.00
4 0.
805
0.02
8 0.
089
—
C1
Pe
arso
n's r
0.
008
0.05
9 -0
.078
0.
020
0.02
6 0.
084
0.17
5 —
p-va
lue
0.77
7 0.
045
0.00
8 0.
506
0.37
3 0.
004
< .0
01
—
B2
Pear
son'
s r
0.05
2 0.
017
0.07
4 0.
051
0.04
3 -0
.089
-0
.187
-0
.059
—
p-
valu
e 0.
076
0.56
1 0.
011
0.08
2 0.
142
0.00
2 <
.001
0.
045
—
C2
Pe
arso
n's r
0.
084
0.09
5 -0
.003
0.
007
0.10
8 0.
024
0.02
8 0.
172
0.23
9 —
p-va
lue
0.01
1 0.
004
0.93
1 0.
821
0.00
1 0.
461
0.40
1 <
.001
<
.001
—
B3
Pe
arso
n's r
-0
.025
-0
.062
0.
069
-0.0
16
-0.0
88
-0.1
74
0.07
0 -0
.045
0.
086
0.02
6 —
p-
valu
e 0.
385
0.03
3 0.
018
0.58
5 0.
003
< .0
01
0.01
6 0.
131
0.00
3 0.
431
—
C3
Pe
arso
n's r
-0
.020
0.
034
-0.0
24
-0.0
67
-0.0
37
-0.0
12
0.08
0 0.
125
-0.0
05
0.20
7 0.
418
—
p-
valu
e 0.
519
0.27
5 0.
442
0.03
1 0.
237
0.69
6 0.
009
< .0
01
0.86
7 <
.001
<
.001
—
B4
Pe
arso
n's r
0.
063
0.04
5 -0
.005
-0
.042
0.
027
0.02
6 -0
.002
-0
.027
0.
069
0.02
4 0.
037
0.01
6 —
p-
valu
e 0.
030
0.12
4 0.
857
0.15
7 0.
347
0.37
4 0.
934
0.35
6 0.
018
0.46
3 0.
208
0.61
3 —
C4
Pear
son'
s r
0.05
6 0.
088
-0.0
62
-0.0
65
0.04
7 0.
043
0.07
9 0.
200
-0.0
13
0.15
9 -0
.040
0.
185
0.34
9 —
p-va
lue
0.08
5 0.
007
0.05
4 0.
045
0.14
9 0.
181
0.01
4 <
.001
0.
676
< .0
01
0.21
0 <
.001
<
.001
—
B5
Pe
arso
n's r
0.
019
0.01
0 -0
.015
0.
005
0.07
7 0.
006
0.06
5 0.
052
-0.0
16
0.00
0 -0
.039
-0
.060
-0
.016
-0
.019
—
p-
valu
e 0.
514
0.74
4 0.
603
0.85
4 0.
009
0.82
5 0.
026
0.07
7 0.
582
0.99
0 0.
186
0.05
2 0.
573
0.56
2 —
C5
Pear
son'
s r
0.00
1 0.
043
-0.0
52
-0.0
16
0.03
2 0.
046
0.03
5 0.
140
-0.0
22
0.18
0 -0
.064
0.
072
-0.0
13
0.24
0 0.
018
—
p-
valu
e 0.
981
0.18
5 0.
115
0.61
7 0.
327
0.15
7 0.
284
< .0
01
0.50
1 <
.001
0.
049
0.03
3 0.
689
< .0
01
0.59
2 —
B6
Pe
arso
n's r
0.
045
0.02
4 0.
022
0.06
3 0.
030
-0.0
71
0.05
5 0.
046
0.00
4 -0
.049
0.
032
0.03
4 0.
039
-0.0
08
0.26
8 -0
.035
—
p-
valu
e 0.
128
0.42
1 0.
451
0.03
1 0.
310
0.01
5 0.
061
0.11
8 0.
879
0.13
9 0.
271
0.27
3 0.
185
0.80
3 <
.001
0.
291
—
C6
Pe
arso
n's r
0.
040
0.05
1 -0
.078
0.
044
0.02
7 0.
069
0.03
2 0.
236
-0.0
23
0.17
7 -0
.052
0.
174
0.01
2 0.
222
-0.0
25
0.30
2 0.
090
—
p-
valu
e 0.
235
0.12
9 0.
019
0.19
1 0.
411
0.04
0 0.
330
< .0
01
0.48
3 <
.001
0.
121
< .0
01
0.71
7 <
.001
0.
458
< .0
01
0.00
7 —
B7
Pe
arso
n's r
0.
012
0.04
9 -0
.030
-0
.012
0.
036
-0.0
59
0.01
9 -0
.018
0.
048
-0.0
07
-0.0
20
-0.0
73
0.01
8 0.
084
0.11
8 -0
.059
0.
089
-0.0
88
—
p-va
lue
0.68
8 0.
095
0.30
4 0.
671
0.21
9 0.
044
0.51
8 0.
546
0.09
8 0.
826
0.50
0 0.
019
0.53
9 0.
009
< .0
01
0.07
4 0.
002
0.00
8 —
C7
Pear
son'
s r
-0.0
35
-0.0
11
-0.0
87
-0.0
49
-0.0
62
0.02
0 0.
061
0.13
0 0.
002
0.16
8 -0
.036
0.
108
0.02
6 0.
178
-0.0
59
0.25
2 -0
.048
0.
200
-0.2
14
—
p-
valu
e 0.
335
0.75
0 0.
014
0.17
1 0.
085
0.57
0 0.
087
< .0
01
0.94
6 <
.001
0.
319
0.00
3 0.
464
< .0
01
0.09
7 <
.001
0.
178
< .0
01
< .0
01
—
B8
Pear
son'
s r
0.00
1 0.
124
-0.0
41
-0.0
12
0.14
2 0.
137
0.04
0 0.
040
-0.0
33
-0.0
12
-0.0
76
-0.0
17
-0.0
71
-0.0
81
0.14
1 -0
.045
0.
009
0.01
5 0.
089
-0.0
38
—
p-va
lue
0.97
6 <
.001
0.
159
0.68
9 <
.001
<
.001
0.
174
0.17
2 0.
258
0.71
7 0.
010
0.57
4 0.
015
0.01
2 <
.001
0.
165
0.77
0 0.
644
0.00
2 0.
292
—
C8
Pe
arso
n's r
0.
026
0.03
6 -0
.039
-0
.050
0.
059
0.04
9 0.
109
0.21
8 0.
012
0.24
8 -0
.019
0.
143
0.05
9 0.
247
0.02
3 0.
229
0.00
2 0.
253
-0.0
50
0.17
6 -0
.028
—
p-va
lue
0.41
8 0.
269
0.22
1 0.
126
0.06
8 0.
126
< .0
01
< .0
01
0.70
9 <
.001
0.
554
< .0
01
0.06
8 <
.001
0.
470
< .0
01
0.95
8 <
.001
0.
124
< .0
01
0.38
0 —
A3
A4
A6
A7
A8
A9
B1
C1
B2
C2
B3
C3
B4
C4
B5
C5
B6
C6
B7
C7
B8
C8
B9
Pear
son'
s r
-0.0
75
-0.0
28
-0.0
30
-0.0
14
-0.0
45
-0.1
41
0.13
3 0.
015
-0.0
79
-0.0
09
0.32
7 0.
172
-0.0
73
0.03
1 -0
.098
-0
.006
-0
.040
0.
006
0.00
8 0.
063
-0.0
81
-0.0
06
p-
valu
e 0.
010
0.34
5 0.
301
0.62
9 0.
125
< .0
01
< .0
01
0.62
3 0.
007
0.77
4 <
.001
<
.001
0.
013
0.33
4 <
.001
0.
865
0.17
5 0.
858
0.78
8 0.
077
0.00
5 0.
864
C9
Pear
son'
s r
0.02
4 0.
038
0.01
9 -0
.048
-0
.017
0.
006
0.09
5 0.
142
0.04
1 0.
206
0.10
8 0.
282
0.04
3 0.
235
-0.0
66
0.13
6 0.
013
0.15
0 -0
.037
0.
221
-0.0
41
0.29
0
p-va
lue
0.45
2 0.
239
0.54
6 0.
134
0.59
7 0.
854
0.00
3 <
.001
0.
202
< .0
01
< .0
01
< .0
01
0.17
3 <
.001
0.
037
< .0
01
0.67
7 <
.001
0.
243
< .0
01
0.19
8 <
.001
B1
0 Pe
arso
n's r
-0
.049
0.
006
-0.0
53
-0.0
72
-0.0
57
-0.0
23
0.07
6 0.
004
-0.1
50
-0.0
86
0.09
6 0.
037
0.01
6 -0
.023
-0
.199
-0
.039
-0
.110
0.
102
-0.0
60
0.00
4 -0
.122
0.
009
p-
valu
e 0.
095
0.84
3 0.
071
0.01
5 0.
052
0.42
9 0.
010
0.88
4 <
.001
0.
009
0.00
1 0.
236
0.59
0 0.
469
< .0
01
0.23
5 <
.001
0.
002
0.04
1 0.
901
< .0
01
0.78
7 C1
0 Pe
arso
n's r
0.
021
0.01
0 -0
.035
-0
.035
-0
.044
0.
005
0.02
9 0.
164
-0.0
52
0.16
6 -0
.022
0.
094
0.07
3 0.
245
-0.0
79
0.20
2 -0
.020
0.
178
0.01
7 0.
239
-0.0
48
0.28
7
p-va
lue
0.56
9 0.
782
0.33
7 0.
341
0.22
4 0.
883
0.41
8 <
.001
0.
147
< .0
01
0.55
0 0.
012
0.04
1 <
.001
0.
028
< .0
01
0.58
8 <
.001
0.
642
< .0
01
0.18
7 <
.001
B1
1 Pe
arso
n's r
-0
.088
-0
.068
-0
.102
-0
.033
-0
.097
-0
.045
0.
175
0.06
7 -0
.206
-0
.064
0.
161
0.11
8 0.
046
-0.0
01
0.00
3 0.
056
0.11
8 0.
037
-0.0
39
0.05
9 -0
.065
0.
059
p-
valu
e 0.
003
0.02
0 <
.001
0.
268
< .0
01
0.12
6 <
.001
0.
024
< .0
01
0.05
1 <
.001
<
.001
0.
115
0.97
9 0.
910
0.08
4 <
.001
0.
264
0.18
5 0.
098
0.02
5 0.
067
C11
Pear
son'
s r
0.04
5 -0
.012
-0
.056
-0
.031
-0
.055
0.
016
0.10
2 0.
236
-0.0
40
0.25
4 0.
031
0.21
4 0.
075
0.26
2 -0
.030
0.
176
-0.0
26
0.27
8 -0
.084
0.
241
-0.0
52
0.26
4
p-va
lue
0.17
6 0.
716
0.09
2 0.
351
0.10
1 0.
624
0.00
2 <
.001
0.
228
< .0
01
0.35
1 <
.001
0.
023
< .0
01
0.37
2 <
.001
0.
425
< .0
01
0.01
1 <
.001
0.
118
< .0
01
B12
Pear
son'
s r
-0.1
27
-0.0
73
-0.0
48
-0.0
60
-0.1
11
-0.0
16
0.12
3 0.
066
-0.1
06
-0.0
50
0.05
3 0.
030
0.04
1 0.
001
-0.0
46
0.03
9 -0
.021
0.
066
-0.0
59
0.04
1 0.
039
0.02
8
p-va
lue
< .0
01
0.01
2 0.
101
0.04
0 <
.001
0.
590
< .0
01
0.02
6 <
.001
0.
126
0.07
1 0.
326
0.16
5 0.
979
0.11
1 0.
237
0.47
4 0.
047
0.04
2 0.
254
0.18
0 0.
394
C12
Pear
son'
s r
-0.0
10
0.03
9 -0
.048
-0
.054
0.
015
0.04
0 0.
062
0.24
3 -0
.039
0.
191
0.04
4 0.
233
0.07
7 0.
289
-0.0
27
0.16
8 0.
002
0.26
8 -0
.076
0.
203
0.02
0 0.
227
p-
valu
e 0.
758
0.22
4 0.
137
0.09
2 0.
651
0.20
7 0.
053
< .0
01
0.22
0 <
.001
0.
170
< .0
01
0.01
7 <
.001
0.
407
< .0
01
0.95
3 <
.001
0.
018
< .0
01
0.54
3 <
.001
B1
3 Pe
arso
n's r
-0
.047
-0
.062
-0
.035
0.
000
-0.1
32
-0.1
03
0.05
8 0.
064
-0.0
80
-0.0
29
0.08
9 0.
088
-0.0
18
-0.0
14
-0.0
77
0.03
0 -0
.037
0.
063
-0.0
24
0.04
9 0.
038
0.00
3
p-va
lue
0.10
6 0.
033
0.23
4 0.
999
< .0
01
< .0
01
0.04
7 0.
031
0.00
6 0.
374
0.00
2 0.
005
0.53
8 0.
663
0.00
9 0.
366
0.20
9 0.
059
0.40
8 0.
175
0.19
0 0.
924
C13
Pear
son'
s r
-0.0
19
-0.0
56
-0.0
50
0.01
1 -0
.109
-0
.094
0.
074
0.17
4 -0
.070
0.
118
0.05
8 0.
217
0.01
3 0.
141
-0.0
90
0.12
2 -0
.052
0.
160
-0.0
61
0.15
5 -0
.047
0.
135
p-
valu
e 0.
530
0.06
5 0.
094
0.72
7 <
.001
0.
002
0.01
4 <
.001
0.
021
< .0
01
0.05
2 <
.001
0.
658
< .0
01
0.00
3 <
.001
0.
083
< .0
01
0.04
2 <
.001
0.
118
< .0
01
F1
Pear
son'
s r
-0.0
05
0.01
0 -0
.030
0.
033
-0.0
40
-0.0
42
0.11
2 0.
097
0.04
8 0.
145
0.10
2 0.
135
0.05
5 0.
106
-0.0
83
0.06
6 -0
.009
0.
130
-0.0
46
0.11
0 -0
.075
0.
150
p-
valu
e 0.
858
0.72
7 0.
312
0.26
2 0.
175
0.15
0 <
.001
0.
001
0.09
8 <
.001
<
.001
<
.001
0.
060
0.00
1 0.
005
0.04
3 0.
763
< .0
01
0.11
2 0.
002
0.01
0 <
.001
F2
Pe
arso
n's r
-0
.027
-0
.067
-0
.038
0.
030
-0.0
50
-0.0
04
-0.0
67
-0.0
51
-0.0
76
-0.1
07
0.00
0 -0
.067
-0
.018
-0
.031
0.
013
0.02
1 0.
010
-0.0
45
-0.0
35
-0.0
48
0.01
0 -0
.065
p-va
lue
0.35
4 0.
023
0.19
1 0.
307
0.08
4 0.
901
0.02
2 0.
085
0.00
9 0.
001
0.99
1 0.
029
0.52
9 0.
330
0.64
7 0.
521
0.73
7 0.
179
0.22
8 0.
180
0.74
1 0.
043
F3
Pear
son'
s r
-0.0
32
-0.0
71
0.03
4 -0
.056
-0
.073
-0
.027
-0
.055
-0
.009
-0
.006
-0
.077
0.
044
-0.0
85
0.01
0 -0
.041
-0
.016
-0
.084
-0
.006
-0
.019
-0
.009
-0
.035
-0
.064
-0
.055
p-va
lue
0.27
9 0.
015
0.24
6 0.
055
0.01
2 0.
362
0.06
2 0.
773
0.85
0 0.
018
0.13
3 0.
006
0.72
7 0.
204
0.57
3 0.
010
0.84
3 0.
579
0.75
9 0.
331
0.02
7 0.
086
F4
Pear
son'
s r
0.01
6 0.
013
-0.0
49
0.02
0 -0
.041
-0
.062
0.
169
0.04
3 -0
.080
0.
029
0.09
4 0.
104
-0.0
39
0.02
1 0.
001
0.07
3 0.
042
0.09
7 -0
.021
0.
082
0.04
4 0.
041
p-
valu
e 0.
593
0.65
1 0.
093
0.50
1 0.
161
0.03
3 <
.001
0.
143
0.00
6 0.
380
0.00
1 <
.001
0.
181
0.52
0 0.
980
0.02
6 0.
152
0.00
3 0.
482
0.02
2 0.
129
0.20
1 F5
Pe
arso
n's r
0.
034
-0.0
29
-0.0
09
0.05
8 -0
.103
-0
.097
0.
137
0.15
9 -0
.013
0.
100
0.12
4 0.
138
0.03
1 0.
026
-0.0
22
0.04
6 0.
023
0.15
1 -0
.026
0.
112
-0.0
49
0.07
6
p-va
lue
0.25
1 0.
320
0.76
5 0.
049
< .0
01
< .0
01
< .0
01
< .0
01
0.65
2 0.
002
< .0
01
< .0
01
0.28
5 0.
420
0.45
4 0.
159
0.42
7 <
.001
0.
365
0.00
2 0.
097
0.01
8 F6
Pe
arso
n's r
0.
009
0.00
3 -0
.012
-0
.010
-0
.051
-0
.031
0.
050
0.28
4 0.
001
0.17
0 0.
071
0.16
9 0.
036
0.15
2 -0
.033
0.
127
0.04
0 0.
184
0.00
4 0.
169
-0.0
92
0.22
4
p-va
lue
0.75
3 0.
929
0.69
2 0.
727
0.07
8 0.
292
0.08
8 <
.001
0.
984
< .0
01
0.01
6 <
.001
0.
215
< .0
01
0.25
3 <
.001
0.
170
< .0
01
0.89
5 <
.001
0.
002
< .0
01
F7
Pear
son'
s r
0.01
3 0.
012
-0.0
18
0.02
5 -0
.031
-0
.038
0.
106
0.18
8 -0
.078
0.
144
0.04
2 0.
142
0.00
7 0.
060
0.02
2 0.
051
0.05
1 0.
196
-0.0
34
0.12
2 -0
.016
0.
124
p-
valu
e 0.
653
0.68
5 0.
543
0.39
7 0.
288
0.19
2 <
.001
<
.001
0.
007
< .0
01
0.15
3 <
.001
0.
802
0.06
1 0.
458
0.11
8 0.
083
< .0
01
0.24
8 <
.001
0.
578
< .0
01
F8
Pear
son'
s r
0.01
0 0.
019
0.00
2 0.
030
-0.0
29
-0.0
83
-0.0
49
-0.0
37
0.07
8 -0
.049
0.
009
-0.0
58
0.02
8 -0
.035
-0
.010
-0
.029
0.
018
-0.0
97
0.05
3 -0
.029
0.
053
-0.1
04
p-
valu
e 0.
737
0.51
7 0.
934
0.30
3 0.
328
0.00
4 0.
092
0.21
4 0.
007
0.13
8 0.
752
0.05
9 0.
346
0.27
5 0.
725
0.36
9 0.
530
0.00
4 0.
068
0.42
4 0.
072
0.00
1 F9
Pe
arso
n's r
0.
049
0.00
4 -0
.025
0.
041
-0.0
69
-0.0
75
0.12
0 0.
113
0.02
2 0.
084
0.10
2 0.
149
0.05
6 0.
119
-0.0
37
0.04
8 0.
001
0.12
3 -0
.036
0.
123
-0.0
73
0.15
4
p-va
lue
0.09
6 0.
890
0.38
5 0.
162
0.01
9 0.
010
< .0
01
< .0
01
0.45
1 0.
011
< .0
01
< .0
01
0.05
3 <
.001
0.
210
0.14
2 0.
969
< .0
01
0.22
1 <
.001
0.
012
< .0
01
F10
Pear
son'
s r
0.07
6 -0
.025
0.
016
0.04
5 -0
.055
-0
.085
0.
054
0.09
3 0.
018
0.00
1 0.
188
0.13
0 0.
020
0.00
4 -0
.019
0.
029
0.02
6 0.
104
-0.0
14
0.07
3 0.
002
0.08
5
p-va
lue
0.00
9 0.
402
0.58
4 0.
128
0.06
0 0.
004
0.06
3 0.
002
0.54
7 0.
974
< .0
01
< .0
01
0.49
2 0.
894
0.51
8 0.
369
0.36
7 0.
002
0.64
0 0.
040
0.94
0 0.
008
F11
Pear
son'
s r
0.06
0 -0
.015
-0
.020
0.
047
-0.0
60
-0.0
24
0.13
0 0.
185
-0.0
11
0.12
3 0.
107
0.17
2 0.
026
0.11
9 -0
.072
0.
075
0.02
7 0.
181
-0.0
44
0.17
1 -0
.062
0.
191
p-
valu
e 0.
041
0.60
5 0.
501
0.10
9 0.
04
0.41
2 <
.001
<
.001
0.
718
< .0
01
< .0
01
< .0
01
0.37
8 <
.001
0.
013
0.02
2 0.
353
< .0
01
0.13
6 <
.001
0.
034
< .0
01
B9
C9
B10
C10
B11
C11
B12
C12
B13
C13
F1
F2
F3
F4
F5
F6
F7
F8
F9
F10
F11
B9
Pear
son'
s r
—
p-
valu
e —
C9
Pe
arso
n's r
0.
299
—
p-va
lue
< .0
01
—
B1
0 Pe
arso
n's r
0.
267
0.10
6 —
p-va
lue
< .0
01
< .0
01
—
C10
Pear
son'
s r
0.07
0 0.
294
0.20
6 —
p-
valu
e 0.
053
< .0
01
< .0
01
—
B1
1 Pe
arso
n's r
0.
191
0.00
9 0.
235
0.02
3 —
p-va
lue
< .0
01
0.77
9 <
.001
0.
529
—
C11
Pear
son'
s r
0.10
0 0.
223
0.02
3 0.
313
0.31
3 —
p-
valu
e 0.
002
< .0
01
0.48
3 <
.001
<
.001
—
B12
Pear
son'
s r
0.20
1 0.
070
0.20
1 0.
082
0.22
3 0.
081
—
p-
valu
e <
.001
0.
029
< .0
01
0.02
3 <
.001
0.
015
—
C12
Pear
son'
s r
0.08
9 0.
281
0.09
7 0.
315
0.10
3 0.
372
0.25
6 —
p-
valu
e 0.
005
< .0
01
0.00
3 <
.001
0.
001
< .0
01
< .0
01
—
B1
3 Pe
arso
n's r
0.
230
0.13
7 0.
149
0.00
6 0.
177
0.11
2 0.
252
0.07
1 —
p-va
lue
< .0
01
< .0
01
< .0
01
0.87
9 <
.001
<
.001
<
.001
0.
027
—
C13
Pear
son'
s r
0.11
8 0.
277
0.11
4 0.
176
0.06
3 0.
257
0.08
2 0.
254
0.47
6 —
p-
valu
e <
.001
<
.001
<
.001
<
.001
0.
036
< .0
01
0.00
6 <
.001
<
.001
—
F1
Pear
son'
s r
0.05
4 0.
188
0.08
3 0.
011
0.12
1 0.
149
0.05
7 0.
129
0.11
6 0.
161
—
p-
valu
e 0.
065
< .0
01
0.00
5 0.
753
< .0
01
< .0
01
0.05
2 <
.001
<
.001
<
.001
—
F2
Pe
arso
n's r
0.
002
-0.0
33
0.04
5 0.
001
0.00
3 -0
.024
0.
050
-0.0
63
0.06
8 -0
.011
-0
.203
—
p-
valu
e 0.
956
0.30
3 0.
125
0.98
7 0.
919
0.47
7 0.
086
0.04
9 0.
020
0.71
1 <
.001
—
F3
Pear
son'
s r
0.08
5 -0
.073
0.
025
0.01
4 -0
.016
-0
.071
0.
013
-0.0
78
-0.0
12
-0.0
35
-0.1
83
0.11
7 —
p-va
lue
0.00
4 0.
021
0.39
8 0.
697
0.57
9 0.
033
0.66
7 0.
015
0.68
8 0.
243
< .0
01
< .0
01
—
F4
Pear
son'
s r
0.04
0 0.
115
0.10
7 0.
066
0.13
1 0.
130
0.02
5 0.
087
0.09
5 0.
121
0.26
5 -0
.089
-0
.394
—
p-
valu
e 0.
173
< .0
01
< .0
01
0.06
7 <
.001
<
.001
0.
401
0.00
7 0.
001
< .0
01
< .0
01
0.00
2 <
.001
—
F5
Pear
son'
s r
0.07
5 0.
150
0.07
9 0.
015
0.10
1 0.
152
0.04
5 0.
086
0.09
4 0.
164
0.38
3 -0
.172
-0
.032
0.
265
—
p-
valu
e 0.
010
< .0
01
0.00
7 0.
673
< .0
01
< .0
01
0.12
6 0.
007
0.00
1 <
.001
<
.001
<
.001
0.
276
< .0
01
—
F6
Pear
son'
s r
0.02
5 0.
155
0.04
8 0.
135
0.08
1 0.
250
0.05
8 0.
216
0.11
4 0.
211
0.28
8 -0
.070
-0
.085
0.
194
0.28
7 —
p-
valu
e 0.
393
< .0
01
0.09
7 <
.001
0.
005
< .0
01
0.04
7 <
.001
<
.001
<
.001
<
.001
0.
016
0.00
4 <
.001
<
.001
—
F7
Pear
son'
s r
0.05
8 0.
178
0.07
9 0.
090
0.09
4 0.
168
0.05
6 0.
134
0.07
1 0.
111
0.30
2 -0
.095
-0
.130
0.
292
0.37
3 0.
298
—
p-
valu
e 0.
045
< .0
01
0.00
7 0.
013
0.00
1 <
.001
0.
056
< .0
01
0.01
5 <
.001
<
.001
0.
001
< .0
01
< .0
01
< .0
01
< .0
01
—
F8
Pear
son'
s r
-0.0
13
-0.0
48
-0.0
39
0.00
6 -0
.025
-0
.084
-0
.065
-0
.067
0.
003
-0.0
11
-0.0
51
-0.0
32
-0.0
08
-0.0
54
-0.0
69
-0.0
73
-0.1
70
—
p-va
lue
0.66
5 0.
131
0.18
2 0.
866
0.40
2 0.
011
0.02
5 0.
037
0.92
8 0.
725
0.07
8 0.
276
0.78
1 0.
063
0.01
8 0.
012
< .0
01
—
F9
Pe
arso
n's r
0.
025
0.11
6 0.
063
0.05
8 0.
110
0.17
6 0.
043
0.15
9 0.
049
0.15
4 0.
495
-0.2
23
-0.1
39
0.23
9 0.
460
0.34
4 0.
335
-0.0
64
—
p-
valu
e 0.
393
< .0
01
0.03
1 0.
105
< .0
01
< .0
01
0.13
7 <
.001
0.
093
< .0
01
< .0
01
< .0
01
< .0
01
< .0
01
< .0
01
< .0
01
< .0
01
0.02
9 —
F1
0 Pe
arso
n's r
0.
072
0.06
4 0.
049
0.03
0 0.
109
0.11
2 -0
.012
0.
064
0.04
6 0.
077
0.18
6 -0
.088
-0
.032
0.
155
0.31
6 0.
228
0.22
0 -0
.043
0.
301
—
p-va
lue
0.01
3 0.
043
0.09
3 0.
398
< .0
01
< .0
01
0.69
3 0.
046
0.11
2 0.
010
< .0
01
0.00
3 0.
274
< .0
01
< .0
01
< .0
01
< .0
01
0.14
5 <
.001
—
F11
Pear
son'
s r
0.05
1 0.
168
0.08
6 0.
103
0.13
0 0.
186
0.04
6 0.
156
0.07
8 0.
202
0.39
5 -0
.112
-0
.033
0.
186
0.43
7 0.
445
0.37
8 -0
.080
0.
514
0.40
1 —
p-va
lue
0.08
<
.001
0.
003
0.00
4 <
.001
<
.001
0.
113
< .0
01
0.00
8 <
.001
<
.001
<
.001
0.
262
< .0
01
< .0
01
< .0
01
< .0
01
0.00
6 <
.001
<
.001
—
53
Appendix D This is the summary of the results in Tables 16 and 17 together with the actual B-statements for easier reference. In the table in the appendix, the statements are divided into three sets depending on whether both B-statements and C-questions correlated with the FUN construct, only C-questions, or none. Both B-statements and C-questions correlate with FUN
Q GNS Statement
B1 G‒
SCB+
If my character has a significant flaw that becomes relevant during a tense mo-ment in-game, I would still want to act out the flaw, even if it would be costly or dumb from a tactical standpoint, to stay true to my character.
B3 G+
SSI‒
I prefer it when GMs reward players for being smart, creative, or extra engaged, by handing out additional benefits to their attempted actions, like bonuses to skill checks. Ideally, I'd want GMs to do this, even if the extra rewarding of players isn’t included in the rules as written.
B11 SSF‒ N+
I prefer when the story can be free to go wherever needed in the exploration of deeper topics, rather than being limited to a specific pre-defined situation, mis-sion, or activity. Instead of events unfolding only based on logical progression, ide-ally events are designed by both GM and players to challenge the beliefs of the characters and to pose questions about what really matters.
C-questions but not B-statements correlate with FUN
Q GNS Statement
B6 SSB+ SSF-
As a believable, functioning society, an in-game city should have citizens capable of dealing with important problems. Unless the player characters really are the best fit for the job, I’d expect NPCs to have begun elsewhere, perhaps even solved part of the mystery themselves. To me, it isn’t enough that a mission is given to players “just because”, even if the mission is exciting enough to build a campaign around.
B7 SSB‒ SAS+
If I were to play a campaign set in a premade setting that I am a big fan of, I would prefer the GM to be faithful to the premade setting, even at points where the orig-inal work may arguably have inconsistencies or oddities in its world-building.
B8 SCB‒ SAS+
For a campaign with a more serious tone, I’d dislike if a fellow player came with a very light-hearted character and vice versa. In my opinion, if a character breaks the tone assumed by the setting or campaign, that character will detract from play, and should preferably be tweaked to better fit in.
B9 SSI‒ N+
I am personally in favor of disregarding the rules at times where following them would be to miss a good storytelling moment. For instance, if characters very cen-tral to the plot should have died according to the rules, I am in favor of letting them survive anyway, if them being alive serves the story exploration better than them being dead.
B12 SCB‒ N+
I'm fine with tweaking a character’s backstory on the fly or justifying their behav-iors in hindsight, if it helps us tell a more interesting story. To me, the original vi-sion for a character is secondary to actually telling a great story together. I’d prefer if all players prioritized interesting contributions to the storytelling when making their characters.
54
Neither B-statements nor C-questions correlate with FUN
Q GNS Statement
B2 G+ N‒
It is more important to me that scenes challenge my thinking and/or decision-mak-ing, rather than pose questions about human existence, culture, or society. In the end, I find difficult battles and/or mysteries a lot more interesting than exploring moral dilemmas or social issues.
B4 SSI+ SSB‒
If a supposedly powerful enemy is found to be easily killable through an unfore-seen and convenient exploit, then congratulations to the player who found it. They should be able to use the exploit freely, even if it would imply that everyone else in the game world has been too dumb to consider it themselves.
B5 G‒
SSB+
If the GM would allow an unintelligent enemy to wield complex weaponry or magic, just to offer us a more challenging fight, I’d be skeptical. If the GM wouldn’t soon offer a plausible explanation as to why the enemy knew how to use the ad-vanced tools, it would become harder for me to care about the setting, and I'd have less fun.
B10 SSB‒ N+
In an otherwise medieval fantasy setting, it might be weird to include a technologi-cally advanced civilization of robots. However, if the robots aided one of the play-ers in the thematic exploration of what it means to be human, a question very cen-tral to their character, then I would quickly forgive the leap in world-building logic since its inclusion gave way to good storytelling.
55
Appendix E This is the jamovi correlation matrix underlying the analysis results that (i) there is a strong correlation between how important the respondents felt that the statements were and how hard it was to answer. Statements that mattered less were substantially easier to respond to. There is also (ii) a strong correlation between responding at the endpoints of the Likert scale (“strongly agree” and “strongly disagree”) and how hard it was to answer. Responses that were closer to the middle of the scale were substan-tially easier to respond to. The original answers on the 5-point Likert scale were recoded such that 1-3 were kept, 4 was recoded to 2 and 5 was recoded to 1. This way, both endpoints had the same en-coding. This is the new S2 (survey) variable. The hardness to respond was recorded during the interviews on a 10-point Likert scale and is the H variable. The perceived personal importance of each statement was also recorded on a 10-point Likert scale and is the I variable. The reason for choosing a 10-point scale during the interviews was that it is possible to catch many more nuances in an interview discussion format than in a survey. The jamovi correlation matrix between the three variables is shown here.
Correlation Matrix S2 H I
S2 Pearson's r —
p-value —
H Pearson's r 0.301 —
p-value 0.004 —
I Pearson's r -0.364 -0.183 —
p-value < .001 0.138 —
From the matrix, it can be seen that the strongest correlation is between S2 and I, with a correlation of 0.364 having p<0.1% in the direction of the responses being further from the midpoint of the 5-point Likert scale the more important they were perceived to be. An almost as strong correlation is found between S2 and H, with a correlation of 0.301 having p=0.4% in the direction of statements being harder to respond to the further from the midpoint of the 5-point Likert scale the responses were.14
While the p-values point to a high significance in the correlations, it is important to keep in mind that while there are many data points, they originate from interviews of only six respondents. In addition, those respondents were selected using convenience sampling. Finally, the concept of “hard to respond” could be divided into three com-ponents: difficulties in interpreting the question, difficulties in thinking the question through, and difficulties in having an opinion on other team members’ views. Thus, the results in this appendix should be seen as interesting observations rather than es-tablished facts.
14 The correlation between H and I is not interesting.
56
Appendix F While the PCA of the B-statements resulted in a clustering, see Table 13, a deeper correlation analysis revealed some further facts. As was seen in the table, and stated in the main analysis, the first cluster consisted to a large degree of GNS-N and GNS-G category statements. The PCA clustering in Table 13 of course coincides with the cor-relation matrix, but it constitutes a one-dimensional reduction of two-dimensional data. Thus, some aspects are invariably lost. In an attempt to recover some more infor-mation from the correlation matrix, it was reinspected after the PCA cluster analysis.
Table 14 (repeated): Correlation matrix for GNS statements
B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B2 -0.187 — B3 0.070 0.086 — B4 -0.002 0.069 0.037 — B5 0.065 -0.016 -0.039 -0.016 — B6 0.055 0.004 0.032 0.039 0.268 — B7 0.019 0.048 -0.020 0.018 0.118 0.089 — B8 0.040 -0.033 -0.076 -0.071 0.141 0.009 0.089 — B9 0.133 -0.079 0.327 -0.073 -0.098 -0.040 0.008 -0.081 —
B10 0.076 -0.150 0.096 0.016 -0.199 -0.110 -0.060 -0.122 0.267 — B11 0.175 -0.206 0.161 0.046 0.003 0.118 -0.039 -0.065 0.191 0.235 — B12 0.123 -0.106 0.053 0.041 -0.046 -0.021 -0.059 0.039 0.201 0.201 0.223 — B13 0.058 -0.080 0.089 -0.018 -0.077 -0.037 -0.024 0.038 0.230 0.149 0.177 0.252
In Table 14 (repeated here), it can be seen that, apart from the correlations highlighted by the PCA, there exists a clear correlation between B5, B6, and B10. The common denominator between those three is SSB, which seems to be one of the GNS-S subcat-egories that worked well in the survey. Further, the correlation between B3 and B9 (both containing SSI), the constituents of PCA cluster 2, is the strongest in the entire GNS block of the correlation matrix.
This, in summary, leads to the conclusion that the categories GNS-N, GNS-G, GNS-SSI, and GNS-SSB worked well as components of a typology in this study while GNS-SSF, GNS-SCB, and GNS-SAS did not.
