Modeling spatial presence: A Structural regression model exploring aspects the Temple Presence Inventory

Maria Cipollone School of Communication and Theater

Temple University

This paper is intended to be a submission to the journal Media Psychology.

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ABSTRACT This paper describes an analysis of the Temple Presence Inventory (TPI), a survey instrument that examines (tele)presence experiences across many dimensions and media formats. In the analysis, a structural regression model was used to confirm the latent variables proposed in earlier exploratory factor analyses (Lombard et. al., 2011). Also, it was hypothesized that there was a positive relationship between the latent variables known as mental immersion, perceptual realism, and spatial presence. The proposed model was upheld by the data, and the analysis revealed significant positive relationships between the latent variables. Finally, the implication of these results for the (tele)presence scholarship are discussed, along with potential future analyses.

MODELING PRESENCE 3

INTRODUCTION

Though scholars have long discussed telepresence (hereafter called ‘presence’)

(Minsky, 1980; Rheingold, 1990; Reeves & Nass, 1996), it is Lombard and Ditton’s

(1997) definition as “the perceptual illusion of nonmediation” that most succinctly

summarizes the core concept, and is one of the more widely cited definitions. Presence

can occur in a multitude of contexts, which range from experiencing spatial presence, or

‘being there’ in the mediated environment, to social presence, which is similar to viewing

the mediated environment as a social actor or being (Reeves & Nass, 1996). Scholars

debate the complexity of these concepts, and the existence of this complexity calls for

more stringent measures of the experience so it is more reliable in a research scenario

(Lombard, Ditton, Crane, Davis, Gil-Egui, Horvath, Rossman & Park, 2000). This

analysis will validate an instrument that measures the presence experience, and

specifically focuses on spatial presence, and its relationship to mental immersion and

perceptual realism. These terms are defined in detail below.

Presence is a concept vital to many academic fields such as computer science,

artificial intelligence, education, health, and art (Lombard, Ditton & Weinstein, 2009).

Scholars attempt to elucidate its cognitive effects because presence can be implicated in

persuasion, desensitization, or even learning via media and technology. If the role of

presence is identified in these effects, then an instrument used to measure the experience

must be tested for reliability (Lombard, Weinstein & Ditton, 2011). The evolution of the

presence scholarship has lead to a call for more tested measures of the experience. Many

scales have been designed to address varying aspects of presence, but the Temple

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Presence Inventory (TPI) was developed as theory-based tool to measure across the many

dimensions of a presence-based experience, and the many possible media that might

induce a presence-based experience (Lombard et al., 2000; 2009).

In this analysis, the validity of the TPI was tested using a structural equation

modeling technique known as structural regression. This technique allows researchers to

examine relationships among latent variables, as well as the strength of their relationship

to certain factor indicators (scale items), because it includes both a measurement model

and a path model with subsequent latent variables. Specifically, the analysis was focused

on the TPI’s ability to measure experiences of mental immersion, perceptual realism, and

spatial presence. First, the presence of the latent variables was confirmed (see Lombard et

al., 2011), and relationships between those variables were tested. It was proposed that the

greater the degree of mental immersion, thus a greater degree of perceptual realism, and

finally, a greater degree of spatial presence. In this paper, the concepts of presence are

reviewed; the development of the TPI is discussed, and the rationale for this analysis is

presented. Finally, the results of the analysis are discussed along with the implications

for further use of the TPI scale.

Defining spatial presence

One of the more common conceptions of presence is the medium’s ability to give

the viewer a sense of being ‘within’ the environment that is presented, or spatial presence

(Ijsselstein, de Ridder, Freeman, & Avons, 2000). Lombard & Ditton (1997) discussed

this concept in three ways: ‘you are there’, ‘it is here’ and ‘we are together’. The nuances

of each are very subtle and scholars argue over their distinctions.

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The sense of ‘being there’ in the environment is possibly one of the oldest forms

of presence. Biocca & Levy (1995) cited the earliest examples of symbolic exchange

such as storytelling and cave painting, which tried to bring the viewer into the mediated

environment. Many scholars have focused on the role of spatial presence (being there)

and its role in virtual environments (Biocca, 1997). In virtual environments, a sense of

‘being there’ has been found to assist in remote surgeries (Westwood, Hoffman, Robb &

Stredney, 1999) and successful psychotherapy (Rizzo, Buckwalter, Neumann,

Kesselman, & Thiebaux, 1998). Ravaja, Saari, Laarni, Salminen & Kallinen (2006)

reported that the level of spatial presence a gamer feels within a gaming environment can

heighten his or her experience of emotional arousal.

The notion of ‘being there’ is, in some ways, symbiotic with the concept of spatial

presence as ‘it is here’. The ‘it is here’ concept can be best exemplified by viewers who

are physically startled by events in a mediated environment or attempt to physically move

out of the path of a mediated object (Lombard & Ditton, 1997). Scholars have also

considered the role of presence as ‘it is here’ in television screen-size (Bracken, 2005),

Scholars have also thought of spatial presence as a notion of ‘we are together’ or

in shared space (Lombard & Ditton, 1997). This form of presence is particularly salient

in terms of virtual environments, and teleconferencing (e.g., Skype or Google Hangout).

A sense of shared space is also seen as vital to the success of virtual learning

environments and engagement with virtual identities (Nowak, 2004; Arbaugh et al.,

2008).

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Mental immersion

Whether ‘being there’, ‘it is here’, or ‘we are together’, the experience of spatial

presence may dependent on other cognitive states, such as mental immersion. Mental

immersion, or immersion, is the degree to which the individual is submerged in the

virtual environment and sequestered from distracting inputs in the surrounding physical

environment (Biocca & Delaney, 1995). In this analysis, mental immersion encompasses

both physiological and psychological engagement. Although screen-size and quality can

be related to experiences of spatial presence (Bracken, 2005), immersion is not

necessarily dependent on these technological elements. In the discussion of 3D game

environments, McMahan (2003) suggested that immersion is an acceptance of an

environment that does not stray too far from the viewer’s (or in her case, player’s)

expectation of the imaginary conventions of that environment, as long as they are

consistent (p. 69). Others have proposed that if the mediated environment is preferred to

the viewer’s imaginative environment, then immersion can occur (Wirth, 2006). If the

viewer sees the imaginary environment as inconsistent or implausible, mental immersion

cannot be achieved. Researchers argued that without sensory, psychological and

emotional immersion, spatial presence can not be adequately achieved (Biocca &

Delaney, 1995). Thus, in this analysis, it was hypothesized that mental immersion would

directly affect the degree to which a participant experiences spatial presence.

Perceptual realism

Just as immersion is dependent upon the viewer’s expectation being matched with

the conventions of the mediated object or environment, perceptual realism is dependent

upon a similar concept. Perceptual realism can include an element of social realism.

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Although both concepts (perceptual and social realism) consider the plausibility of events

in real life, perceptual realism does not require that the events can actually happen, or that

the objects can actually exist. For example, in science fiction films, viewers can know

that the events in the mediated event are not plausible (such as the existence of an

incredibly hulking, green superhero), but if the details of the events are displayed as

realistically as one would expect, then the event can be viewed as perceptually realistic

(Lombard & Ditton, 1997). If mental immersion is the degree of engagement in the

environment, then perceptual realism is an aspect of environment that can assist in

immersion.

Measuring presence

Prior to the creation of the TPI, researchers measured different types of presence

in specific contexts with extensive drawbacks. Lombard et al. (2011) discussed the

shortcomings of some of the more widely used measurement instruments. For example,

the Slater-Usoh-Steed Presence questionnaire, or the Presence Questionaire ,which

respectively assess spatial presence (as ‘being there’ or ‘it is here’) as well as perceptual

realism and mental immersion, are designed for virtual environments. Thus, these scales

are not validated using other media, (e.g., film or television) rendering them isolated to

the virtual reality experience.

Other measures, such as the ITC Sense of Presence Inventory (Lessiter, Freeman,

Keogh & Davidoff, 2001) or the Community of Inquiry Instrument (Arbaugh, Cleveland-

Innes, Diaz, Garrison, Ice, Richardson, & Swan, 2008) are pan-media scales, but they

address issues of social presence, and largely ignore aspects of mental immersion,

perceptual realism, and spatial presence.

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Researchers have also used physiological measures of the spatial presence

(Meehan, et al., 2002; Brogni, Vinayagamoorthy, Steed, & Slater, 2006) but the depth of

these measures does not provide extensive ground for interpretation (Lombard et al.,

2011) suggested that these measures are best used in tandem with more subjective

measures.

Development of the Temple Presence Inventory (TPI)

As it was discussed earlier, Lombard et al. (2000) called for the creation of the

TPI partly because of the many conflicting measures of presence that already existed.

Also, the TPI was designed to address the effect of presence across the conceptual

dimensions proposed by Lombard and Ditton in 1997. These dimension include

measures of mental immersion, social presence, social realism, perceptual realism and

spatial presence. The TPI was designed to address these dimensions across media as well

(Lombard et al., 2000; 2009; 2011).

The data for the present analysis came from the initial testing of the TPI in 1999,

when Lombard and colleagues first formulated 114 items for the scale (Lombard et al.,

2000). They tested across conditions, meaning some participants viewed 3D IMAX film

(presumably high presence) and some were in a classroom with a monaural television

playing VHS cassette tapes of a television show that hasn’t aired in nearly 35 years

(presumably low presence; see Sample section for further details). The results from this

initial study revealed significant mean differences across conditions, thus validating the

scale across conditions. Additionally, exploratory factor analysis (using principal

components analysis followed by principal axis factoring with oblique rotation) initially

revealed seven potential factors (Lombard et al., 2000).

MODELING PRESENCE 9

In 2009, Lombard, Ditton, and Weinstein analyzed the same data in order to

eliminate some of the scale items, and test for reliability between items. Exploratory

factor analysis was conducted (principal axis factoring with oblique rotation), and the

researchers found eight potential factors. This time, they retained scale items that loaded

.40 or greater, but dropped items that did not. They reduced the items to 42 questions

across eight factors, and performed tests of internal validity (using Cronbach’s alpha) to

justify their retention of certain questions (Lombard et al., 2009).

To analyze the validity of the items across media format, they tested the TPI with

46 undergraduates at a large urban university. Participants viewed three types of

television programs (a science-fiction film, a comedic news program, and a historical

documentary). The researchers found significant mean differences between groups in

their reporting of social presence, spatial presence, and other dimensions measured in the

TPI (Lombard et al., 2009). Ultimately, the researchers surmised that these results

provided further validity to the instrument.

A 2011 study by Lombard, Weinstein, and Ditton tested the validity of the scale

across low- and high- presence conditions, but this time using video games (SimCity

Classic and Sims3). The researchers surveyed undergraduate students (N=85) using the

TPI and six other prominent presence scales, in order to test the convergent validity of the

TPI (Lombard et al., 2011, p. 5). Analyses of internal validity, inter-correlations among

subscales, and mean scores supported the validity of the TPI to measure across

dimensions of presence, and across media (Lombard et al., 2011, p. 7).

Rationale

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The current study revisited data generated by the TPI (Lombard et al., 2000) to

apply a structural regression approach. This analysis was conducted as further validation

of the TPI through a measurement model (using Confirmatory Factor Analysis), and

subsequent path model with latent variables, which is a basic method of structural

equation modeling used to explore the relationships between those variables, or factors

(McDonald & Ho, 2002). This method is particularly strong because it combines the

elements of confirmatory factor analysis, which assess convergent validity, along with a

regression/path analysis among the latent variables confirmed by the measurement model

(Floyd & Widaman, 1995). It was expected that this analysis would not only provide

further support for the use of the TPI, but also support the prior analyses by Lombard et

al., (2000; 2009; 2011).

Additionally, the inclusion of the path model would test predicted relationships

among the variables. It was hypothesized that the level of mental immersion would

predict both spatial presence and perceptual realism. It was also predicted that there

would be a significant indirect relationship between mental immersion and spatial

presence through perceptual realism. These hypotheses were derived from conclusions

found in the presence literature, specifically from Lombard and Ditton’s conceptual

explication (1997). This analysis is the first of its kind to use the structural equation

modeling format to test the construct validity of the TPI.

METHOD Sample

The data set used for this analysis (N= 469) was obtained in a prior research

experiment, when the TPI was given to participants in both low and high presence

MODELING PRESENCE 11

conditions (Lombard et al., 2000). A set of participants viewed a 3D IMAX film, T-Rex:

Back to the Cretaceous (N=307), while another group viewed black and white images

with monaural sound, as well as a few episodes of a once-popular American sitcom,

Three’s Company (N=162). Participants were solicited at separate sites, during separate

experiments with the TPI. The data from the low-presence condition was collected at an

earlier point with students at a large urban university. In order to collect data for the high-

presence condition, researchers asked potential participants at an IMAX theater (located

in New York City, NY) to assist university researchers with their work. If they agreed,

participants filled out a paper and pencil version of the TPI. Of the 469 participants,

46.3% were females, and males 52.2% from ages 16 to 76 (M = 30.56, SD = 12.18). A

majority of the participants were White (49.8%), and 17.8% African American, 12.7%

were Hispanic, 11% were Asian, and 6.4% listed “other”. A majority of the participants

had some college education (37.1%), or a college degree (20.8%), a graduate degree

(20%). The original data set was collected as further development of the TPI, and

researchers later tested the internal validity of the scale items and the validity of the

measure across media content and context (Lombard et al., 2009).

Model Results

For the modeling portion of the analysis, the MPlus (sixth edition) program was

used (Muthén & Muthén, 2011). The fit of the model was assessed using the chi-square

model fit statistic (χ2M), the Root Mean Square Error of Approximation (RMSEA), the

Bentler Comparative Fit Index (CFI), and the standardized Root Mean Square Residual

(SRMR) (Kline, 2011, p. 210). The chi-square statistic will test whether there was a

significant difference in the observed covariance data (Klein, 2011, p. 200). The RMSEA

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was examined to also approximate the fit of the model. Klein (2011) points out that

customarily, a value of .05 or greater demonstrates good fit for the RMSEA, but it is very

important to report and inspect the 90% confidence interval, where if the lower bound

estimate is less than .05, then the good fit hypothesis is retained, but if the upper bound

FIGURE 1: The hypothesized model of the structural regression of the relationship between mental immersion, perceptual realism, and spatial presence as measured by the TPI.

MODELING PRESENCE 13

confidence interval exceeds .10, then the poor-fit hypothesis can not be rejected. The

CFI indicates the relative success of the model compared the baseline, and the close-fit

hypothesis tested the baseline model against the researcher’s hypothesized model. CFI

values for goodness of fit should be greater than or equal to .95. The SRMR gives a

sense of the proposed covariance matrix and its relative closeness of fit, and should be

less than or equal to .08 (Kline, 2011, p. 209).

Additionally, path coefficients (both standardized and unstandardized) were

examined to understand the relationship between variables, and the proportion of the

variance that these relationships explain.

Measures

The TPI is a scale that has been developed over the last decade (Lombard et al.,

2000; Lombard et al., 2009; Lombard et al., 2011) to amalgamate the various measures

and theoretical conceptions of presence (namely spatial/social) (Lombard & Ditton,

1997). The TPI is a 42-item, 7-point Lichert scale that asks participants to rate their

experiences concerning a mediated experience. In order to ensure a parsimonious model,

six indicators (out of the 42) were chosen to estimate the model for this particular

analysis.

Mental Immersion

Respondents are asked questions concerning their engagement in the mediated

experience, their perception of being ‘within’ the mediated experience, their physical

condition during the experience (e.g., dizziness or nausea) as well as their awareness of

the ‘mediated’ nature of the event. The proposed model used 6 manifest variables from

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the TPI. The two indicators that are proposed to measure the latent variable ‘mental

immersion’ asked participants on a scale of 1 to 7 (1; not at all to 7; very much) whether

they felt mentally immersed in the experience, and how involved they were in the

experience (see Table 1).

The perceptual realism indicators asked participants if they felt as though they

experienced the objects directly or if they seemed like they actually occurred (1; Not at

all, 7; Very much), and the second asked if participants though the objects they viewed

looked as though they might in the real world, using the same interval scale.

Finally, the indicators of spatial presence asked participants how much they felt

that as though they could reach out and touch object that were presented to them (1; Not

at all, 7; Very much) and to what extent did they feel as though the were actually in the

environment that was presented to them (1; Not at all, 7; Very much).

RESULTS

Descriptive statistics

The descriptive statistics, linearity, normality and multivariate outliers were

assessed using the SPSS program. None of the variables presented more than 1% of

missing data, so there wasn’t a need to investigate the significance of why respondents

didn’t complete a question. Thus, the missing data will be treated as missing completely

at random (MCAR). To examine multivariate outliers, the Mahalanobis’ distance was

calculated for data, and compared with a critical chi-square, χ2 (8) = 20.09 (α=.01).

Thirteen cases were greater than the critical value, thus they were not retained for the

MODELING PRESENCE 15

analysis (N= 456). The descriptive statistics presented in Table 1 reveal relative

normality. Thus, no transformations were applied to the data.

Of the 469 participants, 20.8% reported that they were very involved in the

experience, while 18.0% rated a 6 (out of 7), and 20% rated a 5 (M= 4.81, SD= 1.70).

These frequencies showed a relatively high amount of involvement in the experience.

When asked about mental immersion, 20.5% of participants (M= 4.46, SD= 1.78)

reported a score of 5 (out of 7), meaning they were moderately immersed in the

experience.

TABLE 1: Frequencies (in percent) and descriptive statistics of the six TPI indicators used for the analysis

Question Not at all

2 3 4 5 6 Very much

Skew Kurtosis

Mental Immersion To what extent did you feel mentally immersed in the experience? (N= 466)

6.9 10.3 11.4 20.6 17.8 18.2 14.8 -.292 -.873

How involving was the experience? (N=467)

4.9 4.7 13.1 18.4 20.1 18.0 20.8 -.442 -.612

Perceptual Realism How much did it seem as if you could reach out and touch the objects or people you saw/heard? (N= 467)

27.8 4.5 7.5 9.2 11.1 16.5 23.3 -.196 -1.55

It seemed like the events I saw/heard were actually occurring at the time I saw/heard them. (N= 457)

33.7

12.9

11.8

14.0

8.8

9.8

9.0

-.489 -1.10

Spatial Presence Overall, how much did the things and people in the environment you saw/heard look like they would if you had experienced them

11.8 11.8 10.5 16.1 16.7 16.5 16.7 -.236 -1.14

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The participants in this data were screened in separate scenarios, so it was

possible that they were split over whether they felt they could actually reach out and

touch objects because of spatial presence being lower with a smaller screen versus a

larger one (Lombard et al., 1997). Table 1 demonstrates the descriptive statistics of the

six indicators. Of the 469 participants, 27.8% said that they felt they couldn’t touch the

objects they viewed at all, and 23.3% said they felt they very much could reach out and

touch objects (M= 4.14, SD= 2.37). However comparison of group means revealed non-

significance between group conditions on this particular response F(1, 454)= 1.22, p=.27.

Of the participants, 32.8 % said they strongly disagreed that the events they viewed were

actually happening (M= 3.16, SD= 2.06). Participants were evenly distributed across

responses about objects looking like they might in real life, (M= 4.30, SD= 1.97), and

whether they had a sense of being there (M= 3.80 SD=2.07).

TABLE 2: Fit Indices for the Structural Regression Model Index χ2

M 11.98 dfM 6 p 0.06 RMSEA (90% CI) 0.047 (0.00-0.09) Pclose-fit H0 .497 CFI 0.996 SRMR 0.01 χ2

B 1431.00 dfB 15

Overall, how much did the things and people in the environment you saw/heard look like they would if you had experienced them directly? (N=462)

11.8 11.8 10.5 16.1 16.7 16.5 16.7 -.236 -1.14

To what extent did you experience a sense of being there inside the environment you saw/heard? (N= 457)

21.9 12.3 10.6 11.0 18.2 14.9 11.0 -.010 -1.37

MODELING PRESENCE 17

For the proposed model, the chi-square is non-significant which indicates a

proposed model that isn’t significantly different than the data present. This is known as

the exact-fit hypothesis, which, according to the results, is not rejected. The RMSEA

value is close .05 and the close-fit hypothesis cannot be rejected (p =.497). Also, lower

bound of the 90% confidence interval is below .05, and the upper bound is below .10, so

it can be said the model is relatively close fitting, and we can reject the poor-fit

hypothesis. The CFI statistic is 0.996, indicating a substantial improvement over the

independence model. The SRMR is relatively close to zero (zero can indicate perfect fit),

and well below the accepted value of .08.

Table 3 demonstrates the factor loadings for the measurement portion of the

structural regression model. The standardized factor loadings show the relative strength

of the indicators to measure the latent variables mental immersion, perceptive realism and

spatial presence, which suggest convergent validity. In this analysis, a loading of .60 or

greater was considered a strong factor loading.

TABLE 3: Factor Loadings and Residuals for the Measurement Portion of the Structural

Regression Model of the TPI Factor Loadings Measurement Errors Parameter Unst. SE St. Unst. SE St. Mental Immersion

Involvement 1.00a -- 0.817 0.924 0.090 0.333 Mentally Immersing

1.163 0.060 0.895 0.620 0.099 0.198

Perceptive Realism

Actually Occur

1.00a 0.645 2.490 0.202 0.584

Look Like 1.110 0.089 0.754 1.665 0.177 0.432 Spatial Presence

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Being There 1.00a -- 0.874 1.007 0.123 0.237 Reach Out and Touch

1.050 0.053 0.806 1.966 0.174 0.351

Note: Unst., unstandardized; St., Standardized. Standardized estimates for measurement errors are proportions of unexplained variance. aNot tested for statistical significance. For all other unstandardized estimates, p < .05.

Path model estimation

Table 4 shows the estimates for the proposed path model. The coefficients

evidence a positive relationship between mental immersion and perceptual realism, as

well as mental immersion and spatial presence. Also, there is a significant positive

relationship between perceptual realism and spatial presence. Thus, the hypotheses,

which proposed that an increase in perceptual realism and mental immersion would lead

to an increase in spatial presence was not rejected. Finally, there was a specific indirect

effect of mental immersion on spatial presence via perceptual realism. The standardized

disturbance variances demonstrate the proportion of unexplained variance for the latent

constructs, mental immersion, perceptual realism, and spatial presence.

Parameter Unstandardized SE Standardized Direct effects Mental ImmersionSpatial Presence*

0.420 0.135 0.317

Mental Immersion Perceptive Realism*

0.798 0.080 0.782

Perceptive Realism Spatial Presence*

0.925 0.162 0.684

Indirect effects Mental Immersion Spatial Presence Through Perceptive Realism*

0.335

0.098

0.248

Disturbance variance Mental Immersion* 0.702 0.122 0.389 Perceptive Realism* 1.777 0.258 1.00a Spatial Presence* 0.301 0.142 0.092 aNot tested for statistical significance. Note: Standardized estimates for disturbance variances are proportions of unexplained variance. *p <. 001.

MODELING PRESENCE 19

DISCUSSION

The results of this structural regression analysis demonstrate that the proposed

model (see Figure 1) fit the data. The indicators had strong factor loadings on the

proposed latent variables, thus confirming the strength of the TPI in its measurement of

mental immersion, perceptual realism, and spatial presence. Finally, the results indicate

that there is a significant relationship between the three latent variables, both in direct and

indirect paths. Thus, the hypothesis that an increase in the amount of mental immersion

would lead to an increase in perceptual realism, and an increase in the amount of spatial

presence was confirmed. These significant findings implicate that the TPI is adequately

estimating the proposed dimensions of presence first conceptualized by Lombard and

Ditton (1997). Furthermore, the measurement portion of the model confirms the validity

of the exploratory factor analysis conducted in prior research using the TPI (Lombard et

al., 2009; 2011).

There were several limitations to this analysis. First, the use of structural

equation modeling required a larger sample size, so some of the newer data collected in

more recent studies was not suitable for the method (Kline, 2011, p. 12). In the future,

new data would have to be collected to assess the validity of the TPI using structural

regression techniques.

Another limitation of the study was the selection of only six manifest variables to

measure the latent variables. This analysis investigated the paths of the latent variables

concerning mental immersion, perceptual realism, and spatial presence, but the other

types of presence should be incorporated into the model using a structural regression

technique. More importantly, because the TPI was designed to measure presence across

Cipollone 20

the dimensions explicated by Lombard and Ditton (1997), including the other latent

variables would further validate the strength of the scale. A subsequent analysis would

include the dimensions of social presence through parasocial interaction, which is an

intimate but one-sided type of presence, or social presence- active/passive interpersonal,

where the viewer considers mediated objects that are like other beings, and either

passively or actively respond to those actors (Reeves & Nass, 1996; Lombard & Ditton,

1997). It would also be necessary to include the dimensions of social richness, in which

the medium evokes a sense of immediacy and intimacy, and social realism, in which the

viewer believes that the events in the mediated event could possibly occur in the real

world.

However the inclusion of 42 indicators would increase the complexity of the

model, and may not be practical. Thus, in future analyses, the indicators might be

parceled together to measure latent variables. In case of the TPI, domain representative

parcels could be made using Lombard & Ditton’s (1997) unidimensional constructs.

These parcels could be incorporated into a more parsimonious model with greater

reliability (Coffman & McCallum, p. 237). In their analysis of path models, Coffman and

McCallum (2005) found that models with pareceled indicators (also known as partially

disaggregate models) have larger path coefficients and smaller residual variances than

those models that include unparceled indicators. Therefore, it would make a great deal of

sense to test the TPI using this technique.

MODELING PRESENCE 21

CONCLUSION

The validity of the TP1 was assessed in this analysis, specifically in its ability to

measure the latent variables: spatial presence, mental immersion, and perceptive realism.

The use of the structural regression model was useful in that it was able to confirm prior

factor analyses of the TPI (Lombard et al., 2000; 2009; 2011), but also because important

relationships between the latent variables were evaluated. As a result of this research, the

conditions that induce a spatial presence experience are better understood. On the level

of scale development, these results make a strong case for the use of the TPI when

measuring presence. In a broader sense, the results add strength to the presence

scholarship, because they ensure a more clear measure of the experience. As scholars are

able to more acutely understand presence-based experiences, they can further understand

the nature of these dynamic experiences, and the cognitive processes behind them.

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