Measuring Narrative Engagement

Abstract

Research indicates that the extent to which one becomes engaged, transported, or immersed in a narrative influences the narrative's potential to affect subsequent story-related attitudes and beliefs. Explaining narrative effects and understanding the mechanisms responsible depends on our ability to measure narrative engagement in a theoretically meaningful way. This article develops a scale for measuring narrative engagement that is based on a mental models approach to narrative processing. It distinguishes among four dimensions of experiential engagement in narratives: narrative understanding, attentional focus, emotional engagement, and narrative presence. The scale is developed and validated through exploratory and confirmatory factor analyses with data from viewers of feature film and television, in different viewing situations, and from two different countries. The scale's ability to predict enjoyment and story-consistent attitudes across different programs is presented. Implications for conceptualizing engagement with narratives as well as narrative persuasion and media effects are discussed.

Notes

*Key to items' original theoretical constructs: CP = cognitive perspective taking; EP = empathy; SM = sympathy; NP = narrative presence; NI = narrative involvement; LT = loss of time; LS = loss of self; EC = ease of cognitive access; DS = distraction; NR = narrative realism.

*Key to items' original theoretical constructs: CP = cognitive perspective taking; EP = empathy; SM = sympathy; NP = narrative presence; NI = narrative involvement; LT = loss of time; LS = loss of self; EC = ease of cognitive access; DS = distraction; NR = narrative realism.

*Key to items' original theoretical constructs: CP = cognitive perspective taking; EP = empathy; SM = sympathy; NP = narrative presence; NI = narrative involvement; LT = loss of time; LS = loss of self; EC = ease of cognitive access; DS = distraction; NR = narrative realism.

* = p < .05

** = p < .01

* = p < .05

** = p < .01

1. Pilot and season premier episodes are more useful than mid-season episodes because characters and plot lines either are not yet developed or are recapitulated.

2. In the first step, a principle components analysis (PCA) was performed to assess factorability of the data set, multicollinearity, and singularity, as well as to determine the number of factors. The Kaiser-Meyer-Olkin measure of sampling adequacy was .93, well above the recommended 0.5, indicating the factorability of the data set. The determination coefficient of the correlation matrix approached 0, indicating a possible problem with multicollinearity and singularity. However, there were no correlations between any two variables exceeding .70, and no eigenvalues associated with the factors approaching 0 (lowest eigenvalue was .235). Thus, no variables causing multicollinearity could be identified. Thus, all items in the set were retained preserving all of the theoretically justified variables.

3. A test run of a factor analysis (principal axis factoring) with an oblique rotation (SPSS Promax with kappa = 3; see Tataryn, Wood, & Gorsuch, 1999) showed that several factors were correlated more than .32 (equaling 10% shared variance) indicating that oblique rotation is more appropriate than orthogonal.

4. A concern that our emotional engagement subscale is redundant with the emotional dimension of Green and Brock's (2000) transportation scale appears unwarranted. Items from the emotion dimension of the transportation scale appear to capture the consequences of emoting with characters, while the items on our subscale focus on empathy. Correlations among the dissimilar items from the two scales ranged from .01 to .29 across datasets.