Group Dynamics: Theory, Research, and Practice
Group interactions and time: Using sequential analysis to study group dynamics in
project meetings
--Manuscript Draft--
Manuscript Number:
GDN-2015-0132R3
Full Title:
Group interactions and time: Using sequential analysis to study group dynamics in
project meetings
Abstract:
Video-recorded observations of group interactions present a unique challenge for
group researchers. This paper presents methodological advice how to perform
sequential analysis when collecting observational timed-event data of group
discussions. Sequential analyses is a statistical method that examines dynamic
behavioral sequences in group interactions. To exemplify the method, we present data
from one industry project team which was video-taped during 24 consecutive meetings.
Meeting behaviors were coded into different categories (e.g., procedural and actionoriented communication). We compared sequential behavioral patterns in meetings
from the first and second half of the project. We provide guidelines on the topic of interrater reliability and report a detailed psychometric analysis of the observational
instrument. Overall, we show that positive procedural communication can inhibit
dysfunctional communication patterns in group meetings. Our results also show that
communication patterns of negative action-orientation only appeared in the second half
of the project. This study extends previous group research on micro-sequential
patterns with respect to larger scale macro-temporal group dynamics. Overall, we
provide practical suggestions for researchers who aim to run observational research
and aim to look for sequential dynamics in video-recorded team interactions.
Article Type:
Special Issue Article
Keywords:
group interaction patterns; timed-event data; sequential analyses; inter-rater
reliability; video-recordings
Corresponding Author:
Florian Erik Klonek
Technische Universität Braunschweig
Braunschweig, GERMANY
Corresponding Author E-Mail:
[email protected]
Corresponding Author Secondary
Information:
Corresponding Author's Institution:
Technische Universität Braunschweig
Other Authors:
Vicenç Quera
Manuel Burba
Simone Kauffeld
Author Comments:
May 18th, 2016
Dear David Marcus,
Thank you for your quick response and final feedback for our manuscript entitled
"Group interactions and time: Using sequential analysis to study group dynamics in
project meetings" to the call for papers on "Statistical Methods in Group Psychology
and Group Psychotherapy" in Group Dynamics: Theory, Research, and Practice.
We followed your recommendation and included the wording "supplementary online
file" for all supplemental materials that we mention throughout manuscript.
Thank you for allowing us to contribute to Group Dynamics: Theory, Research, and
Practice.
Best regards,
The author team
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Title page with All Author Information
Group interactions and time: Using sequential analysis to study group dynamics in
project meetings
Florian E. Kloneka, Vicenç Querab, Manuel Burbaa, & Simone Kauffelda
a
Technische Universität Braunschweig
b
Universitat de Barcelona
Corresponding author:
Florian E. Klonek
Technische Universität Braunschweig
Department of Industrial/Organizational and Social Psychology
Spielmannstr. 19, 38106 Braunschweig
Germany
Phone: +49 531 3912566
Email: [email protected]
Masked Manuscript without Author Information
Abstract
Video-recorded observations of group interactions present a unique challenge for
group researchers. This paper presents methodological advice how to perform sequential
analysis when collecting observational timed-event data of group discussions. Sequential
analyses is a statistical method that examines dynamic behavioral sequences in group
interactions. To exemplify the method, we present data from one industry project team
which was video-taped during 24 consecutive meetings. Meeting behaviors were coded into
different categories (e.g., procedural and action-oriented communication). We compared
sequential behavioral patterns in meetings from the first and second half of the project. We
provide guidelines on the topic of inter-rater reliability and report a detailed psychometric
analysis of the observational instrument. Overall, we show that positive procedural
communication can inhibit dysfunctional communication patterns in group meetings. Our
results also show that communication patterns of negative action-orientation only appeared
in the second half of the project. This study extends previous group research on microsequential patterns with respect to larger scale macro-temporal group dynamics. Overall, we
provide practical suggestions for researchers who aim to run observational research and aim
to look for sequential dynamics in video-recorded team interactions.
Keywords: group interaction patterns, timed-event data; sequential analyses; interrater reliability; video-recordings
The quote “No one can whistle a symphony. It takes an orchestra to play it” points
out that the temporal dimension of group interactions are noteworthy: In the orchestraexample, we can observe how group interactions unfold over time and produce a continuous
behavioral output. The study of groups requires methods that capture these dynamic,
changing, and complex interactions (Arrow, Poole, Henry, Wheelan, & Moreland, 2004;
Hewes & Poole, 2012). One promising method to adequately assess group dynamics is to
use video-recorded observations: This type of research has been conducted in the study of
organizational group meetings (Grawitch, Munz, & Kramer, 2003; Lehmann-Willenbrock,
Allen, & Kauffeld, 2013; Meinecke & Lehmann-Willenbrock, 2015; Lehmann-Willenbrock,
Meyers, Kauffeld, Neininger, & Henschel, 2011), quality improvement teams (Jackson &
Poole, 2003), group therapy research (Osilla et al., 2015; Shorey, Martino, Lamb, LaRowe,
& Santa Ana, 2015), and laboratory group discussions (Bonito, DeCamp, Coffman, &
Fleming, 2006; Kuk, 2000). More recently, Leenders, Contractor, and Dechurch (2015, p. 6)
proposed that
The holy grail for research on team dynamics is to be able to watch a ‘movie’ of
team process as it unfolds, then pause the movie, and be able to answer the
questions: what will likely happen next and with what implications for outcomes?
Observational methods can thus advance our theoretical and practical reasoning
about the way group behavior dynamically changes over time (Weingart, 1997). The aim of
this paper is to outline sequential analyses in the longitudinal study of group dynamics.
In sum, our paper offers the following contributions. First, we provide readers with
some background on observational data structures for video-recorded and coded interactions.
Second, we provide guidelines in the assessment of observer agreement. Third, we explain
necessary assumptions and common pitfalls for running sequential analyses. Fourth, we
illustrate our explanations on micro- and macro-temporal group dynamics using data from 24
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video-taped organizational project meetings. Fifth, we discuss challenges associated with the
analyses of observational data.
Studying Dynamic Group Interaction Processes with Observational Methods
Observations of group interactions provide multiple information at once: time,
location, and participants’ interactions. This depth of information enables the researcher to
investigate group processes in detail and from a micro-perspective. When researchers want
to look at this interactional stream of behaviors, they need to segment these microinteractions into single events (observational units), record their onset and offset times, and
assign codes to each event. The result of this process are timed-event sequential data
(Bakeman, Quera, & Gnisci, 2007): A behavioral sequence is represented as a series of time
ordered codes for the observed events along with information about their onset and offset
times. Both duration and events can be considered. Timed-event sequences are extremely
useful because the temporal order of behaviors allows answering questions about dynamic
communication patterns (i.e., what happens after what?). Furthermore, timed-event data
represent a solution
to measure team process in continuous time rather than at multiple discrete time
points. This allows the researcher to track the actual development of the team’s
process (which is very likely nonlinear and unevenly spaced across time) without
having to make arbitrary and largely atheoretical decisions about time intervals.
(Leenders et al., 2015, p. 5)
Reliability of Group Dynamic Interactions
Observational research is labor intensive and dependent on trained human observers
(Folger, Hewes, & Poole, 1984; Klonek, Quera, & Kauffeld, 2015; Weingart, 1997). The
problem of reliability is usually addressed by double-coding the data by at least two
observers and estimating how they agree about a coded behavior. There is an abundance of
3
statistical parameters to be used for inter-rater reliability (IRR). Hallgreen (2012) provides a
reader-friendly overview of coding-designs and IRR parameters. Hayes and Krippendorf
(2007) also give guidance on calculating IRR when using more than two observers. Both
reviews focus on code summary counts (e.g., behavioral frequencies). However, when
researchers aim to investigate the micro-level of interactions (e.g., sequential analyses), they
also need to record temporal and sequential information and should assess the point-by-point
IRR (an example of this problem is given in Klonek, Quera et al., 2015). For this reason, the
current study will provide guidance which parameters to report when using timed-event data
of group interactions.
Micro-temporal Dynamics of Group Interactions
Our analysis builds on previous work from Kauffeld and Meyers (2009) who studied
micro-temporal interactions of semi-autonomous work groups. The authors coded
participants’ action-oriented communication (e.g., “complaining behavior” or “statements
that express no interest in change”) using video-taped meetings. They hypothesized a
reciprocity principle within these group interactions, that is, complaints should facilitate
expression of further complaints. Furthermore, they assumed that positive procedural
communication (e.g., “pointing out the main topic”) should inhibit further negative actionoriented communication. Using sequential analysis, the authors found evidence for mutual
facilitation of negative action-oriented communication (e.g., complaining cycles) as well as
for the hypothesized inhibitory effect of positive procedural communication on negative
action-orientation communication (Kauffeld & Meyers, 2009). The current study uses the
same coding instrument and seeks to replicate these findings. Based on this previous work,
we hypothesize:
H1: Positive procedural statements inhibit the emergence of negative action
statements in group meetings.
4
H2: Negative action-oriented statements facilitate the emergence of further negative
action statements in group meetings.
Macro-temporal Dynamics of Group Interactions
The study from Kauffeld and Meyers (2009) suggested that negative action-oriented
meeting behaviors facilitate complaining behavior. While this research focused on microtemporal group dynamic interactions, our study significantly extends this work by studying
the macro-temporal changes of one project team over an extended period. Therefore, this
study is embedded in the groups-as-complex-systems framework which focuses on the
evolution of team work (Arrow, McGrath, & Berdahl, 2000). This framework “proposes that
many emergent patterns of group behavior should be independent of the characteristics of
individual group members, although they may vary systematically, depending on constraints
in the system’s environment.” (Arrow et al., 2004, p. 25).
In extended project team work, larger temporal dimensions (e.g., the overall project
phase as an environmental constraint) might moderate the interactive relation between group
interactions. This problem refers to the challenge of temporal stability in team dynamic
process research (Leenders et al., 2015). Leenders et al. argue that project teams are not
temporally stable and that a static hypothesis like (e.g., “complaining behavior facilitates
further complaining”) does not accurately reflect the effect of larger temporal group
developments.
In contrast to Kauffeld and Meyers (2009), we collected video-recordings from one
single project team over the course of several consecutive meetings, that is, from the
beginning of the project until the end of it. In line with Leenders et al. (2015), we assume
that group dynamic interactions change over the larger temporal course of the project.
Overall, the mutual working history that group members develop over the course of a project
5
should activate different types of group dynamics. As there are no observational studies on
how group dynamic patterns evolve over the project-cycle, we formulate a research question
about the relationship between group dynamic communication and project phases.
Research question: Are group dynamic micro-communication patterns (e.g.,
complaining cycles) stable over the course of a project?
Method
Source of Data
We videotaped 24 consecutive project team meetings from one organization from the
automotive industry. Meetings were recorded from beginning until project termination. The
project team consisted of 29 participants coming from different organizational departments.
The majority of project members had a University degree (N = 21). Mean age was 38 years
(Min = 26; Max = 53), majority of participants were male (N > 25) which is representative of
the industry examined. Each meeting was attended on an average by 10 participants (SD =
2.1; Min = 6; Max = 16). The number of participants who were present in the first half of
project meetings (i.e., t1 = meeting 1 – 12) was not different from number of participants in
the second project half (i.e., t2: meeting 13 – 24; Mt1 = 11.33, SDt1 = 1.92; Mt2= 10.58, SDt2
= 2.43; t(22) = 0.84, p =.41). The number of participants that were present during meetings
showed no relation with summary scores of act4teams group communication behavior (all
p’s > .10).
Group Communication Behavior within Meetings
Project group communication behavior was coded with the act4teams coding scheme
(e.g., Meinecke & Lehmann-Willenbrock, 2015). This coding scheme has been applied in
numerous field studies about team meeting communication (e.g., Lehmann-Willenbrock et
6
al., 2011; Kauffeld & Lehmann-Willenbrock, 2012) and distinguishes seven broad categories
of group communication.
Problem-focused communication captures problem- or solution-oriented statements
(e.g., “We have not received the single parts for building the prototype” – problem; “Then
we need to postpone the test phase to next week” – solution).
Positive procedural communication captures statements that help a project group to
structure the meeting (e.g., Procedural question: “I guess, it’s my turn now?”, Procedural
suggestion: “I’d say we get through this from top to bottom”). Negative procedural
communication happens when participants discuss subjects that are off topic.
Positive socio-emotional communication covers verbal interactions that build
interpersonal relations (e.g., Encouraging participation: “Do you have some information for
us?” or Expressing feelings: “This makes me really happy”). Negative socio-emotional
communication captures negative feelings or critical statements (e.g., Criticizing: “Honestly,
this department presents the same boring slides every year”).
Positive action-oriented communication captures statements that promote change and
the willingness to take project-related actions (e.g., Taking responsibility: “We will get this
done – no worries!”; Action planning: “I will put this on my list to call Mr. X.”). Negative
action-orientation encompass statements that express resistance to changes (e.g.,
Complaining: “This never works.” or No interest in change: “I don’t care.”).
The supplemental material “Coded Transcript” provides a meeting transcript that
exemplifies how the data was segmented and coded. Each category can also be subdivided
into more fine-grained codes (cf., Table 1). Depending on the research question, researchers
can either use macro-categories (e.g., Meinecke & Lehmann-Willenbrock, 2015) or the more
fine-grained behavior codes (e.g., Kauffeld & Meyers, 2009).
7
Coding and Analysis Software: “How to run sequential analysis”
Meetings were coded using the INTERACT software (Mangold, 2010; the
supplementary online file “Software Options” provides a link for alternative coding tools).
After coding of the verbal interaction stream, we converted the data into the SDS-format
using ActSds-program (Bakeman & Quera, 2008; the supplementary online material
provides a commented SDS file: “Sample SDS Data File”). We used the SDS-files for
running sequential analyses in GSEQ (i.e., transition matrices and statistical test parameters,
Bakeman & Quera, 2011). We have included two video tutorials in the supplementary online
materials that give step-by-step instructions how to run sequential analyses: For GSEQ, see
the supplementary online file “Sequential Analyses with GSEQ.WMV”; for running
sequential analyses in SPSS, see the supplementary online material “Sequential Analyses
with SPSS.WMV”). Both programs can be downloaded for free (the link is provided in the
supplementary online file “Software Options”).
Assessing IRR with Behavioral Coded Data
How many sessions should be used for assessing IRR? To estimate sample size for
IRR analysis, we used the following guidelines: 15 – 20 % of a corpus should be sampled for
calculation of kappa (Bakeman, Deckner, & Quera, 2005). Yoder and Symons (2010)
recommend to use ten or more sessions to calculate IRR based on summary measures. In our
case, we double-coded five meetings, that is, about 300 minutes (5 hours). It is also possible
to time-sample 20% of all meetings for the assessment of IRR. The latter approach provides
a larger sample (N = 24 instead of N = 5) for calculating IRR based on summary scores (e.g.,
intra-class correlations, ICC).
Parsing versus coding of behavioral data streams. Observers need to parse (i.e.,
“sequence” or “unitize”) and code (i.e., “categorize”) the observational stream (Guetzkow,
1950; Weingart, 1997). Parsing involves decisions how to segment speaker utterances
8
(Quera et al., 2007). Coding schemes can also differ in terms of parsing rules (e.g., Auld &
White, 1956; Kauffeld & Lehmann-Willenbrock, 2012; Schermuly & Scholl, 2012; used all
different parsing rules). Some researchers also use pre-parsed sessions, that is, one observer
parses (and codes) the interaction stream, while the second observer (used for calculation of
IRR) only codes pre-parsed segments. Calculating classical indices of IRR (e.g., Cohen’s
kappa) is easier with pre-parsed data streams for standard statistical software packages (e.g.,
SPSS) as both observers code an equal amount of segments (cf., Bakeman et al., 2007, for a
detailed discussion).
Keeping observers independent and blind. Researchers should ensure
independence and blindness of observers (Yoder & Symons, 2010). Independence means
that an observer’s decision to code should not be influenced by another observer. Blindness
means that observers should not know which sessions are chosen in the IRR analysis (Yoder
& Symons, 2010). An observer who codes a pre-parsed observational stream is partly
influenced by the observer who parsed the data because this “parser” determined the length
of the observational unit. Keeping the main observer (who parses and codes) blind for IRR
analysis can circumvent this problem. In our case, the entire sample of project meetings was
parsed and coded independently by the main coder who was blind to the IRR sample.
Calculation of IRR parameters. Observer agreement was estimated with Cohen’s
kappa based on the seven categories using the following formula: κ = Po − Pc ⁄ 1 − Pc ,
where Po denotes the observed proportion of agreement and Pc denotes the expected
proportion of chance agreement, that is, kappa estimates the observed level of agreement
between two coders and corrects for chance agreement.
As a second index of IRR, we computed ICCs to evaluate how observers agreed
about summary measures of group communication (Bakeman & Quera, 2011). Agreement
about summary measures can be either absolute (criterion-referenced agreement) or relative
9
(norm-referenced agreement). Absolute ICCs indicate the degree of strict agreement, that is,
whether observers obtained exactly the same values for the measures, whereas relative ICCs
simply indicate whether the values can be rank-ordered the same way.
Analytical Approach: Sequential Analysis
To test our hypotheses, we used sequential analysis. This method examines how
spontaneous behaviors are associated with subsequent behaviors and allowed to
systematically test whether negative action-oriented behaviors facilitate further negative
action-oriented statements.
The association between behaviors may be analyzed at different time lags. Lag1
indicates the association between a given behavior (utterance) and the immediately following
target behavior. We generated lag-sequence matrices containing joint frequencies for
sequentially adjacent behaviors with given behaviors (i.e., preceding) at lag0 and subsequent
behaviors (i.e., targets) at lag1.
We first ran sequential analyses on individual sessions: However, table totals for the
7x7 matrices for each meeting were too small (M = 1107, SD = 352, Min = 423, Max =
1858) to allow interpretation of sequential analysis based on these individual sessions.
Therefore, we computed statistics based on pooled sessions (i.e., meetings) (cf., Klonek,
Lehmann-Willenbrock, & Kauffeld, 2014; Kauffeld & Myers, 1997). Note that sequential
analyses “from pooled data may differ from the same statistics computed as the mean of
individual cases” (i.e., meetings, Bakeman & Quera, 2011, p. 125). However, pooling is
always recommendable when the events of interest (e.g., most meetings had session-row
sums < 30 for negative action-oriented behavior) are so rare that the accuracy of sequential
statistics become questionable (for a detailed argument about using pooled results, see
Bakeman & Quera, 2011, p.125-126).
10
We used log-linear analysis (Bakeman & Robinson, 1994) to test homogeneity (i.e.,
similarity of transition matrices across meetings, cf., Gottman & Roy, 1990) of lag1
sequential associations across meetings. A three-dimensional (24 meetings x 7 given
behaviors x 7 target behaviors) lag1 frequency table showed that given-target associations
were not independent from meetings (log-likelihood G2(1104)= 2176.6, p < .01). Since many
table cell frequencies were low (39.8% of the 1,176 cells had a 0 or 1 frequency) that result
cannot be taken as conclusive. In order to reduce non-homogeneity and since we were
interested in macro-temporal influences on the project group’s dynamics, we pooled
sequential analysis across meetings from the first versus the second project half.
While most psychological research designs focus on individual differences, the
current analytic approach focused on the behavioral talk turn as the relevant unit – it
theoretically aligns with the groups-as-complex systems framework (Arrow et al., 2000).
Sequential analyses can also be used to compare interaction processes between different
group types. For example, a researcher might use an experimental design to test sequential
associations in meetings for teams that received a training (i.e., an intervention about running
better meetings) in comparison to teams with no training (i.e., control group). The researcher
could compare sequential associations (e.g., mean Yule’s Q values for negative actionoriented behavior) between the intervention and the control group.
Results
IRR Analysis
Table 2 shows the agreement matrix for the two observers. Disagreements (i.e.,
confusions of codes) are shown in off-diagonal cells and agreements in diagonal ones, that
is, IRR is high when in-diagonal cell counts are high and off-diagonal cell counts are low.
------------------------------------
11
Insert Table 2 about here
----------------------------------Since off-diagonal cells indicate codes that cause confusions, the matrix can be useful
when developing a coding scheme. In our data, we can see that off-diagonal cells for
problem-focused and positive procedural communication are high (Observer 1 and 2
confused between N = 114 – 119 events). That is, definitions for these categories should be
refined in future research and we should consider including more rules and examples in the
coding manual to help observers better distinguish these categories.
We calculated an overall kappa (K = .78, p <. 01) as an index of overall point-bypoint agreement based on the matrix in Table 2. Furthermore, we calculated code-specific
kappas. Several authors have provided guidelines to interpret kappa values: Landis and Koch
(1977) proposed that values from 0 to .2 = slight agreement, .21 to .40 = fair agreement, .41
to .60 = moderate agreement, .61 to .80 = substantial agreement and .81 to 1.0 = almost
perfect agreement. Sachs (1999) is more conservative with <.40 = poor, .41–.61 =
considerable, .61–.80 = strong, and .81–1.00 = approaches perfect agreement. So far, there is
no clear preference about which of the categorizations should be used (Bakeman, Quera,
McArthur, & Robinson, 1997). Overall, a look at the agreement matrix is equally important
as kappa when evaluating IRR (cf., Table 2). Kappa interpretation also depends on the
prevalence and number of codes (Bakeman & Quera, 2011). In our example, we calculated
kappa for an instrument with seven codes and high variability of code prevalence. For
example, negative procedural communication was very infrequent (N = 10 for coder 1 and N
= 28 for coder 2). The totals for each observer also tell us that there might be a positive or
negative observer bias for procedural communication. These issues are ideally discussed
during observer training.
12
As a second IRR-measure, we calculated code-specific ICCs based on comparisons
of the overall frequencies. The category negative procedural communication showed a low
ICC in our example (ICC = .36). Overall, we would not recommend to derive conclusions
based on the results for that code.
Prerequisites for Interpretation of Sequential Analysis
Do I have sufficient data to interpret sequential results? Sequential analyses start
by tallying the joint frequencies between adjacent verbal behaviors. In order to obtain
statistical parameters that can be trusted, the expected frequency of any transition should be
at least five, and row sums of transitions should be larger than 30 (e.g., Yoder & Symons,
2010).
When sample size (i.e., the table total) is small, some expected frequencies can be
less than five. In that case, there are two strategies to ensure that the expected frequencies are
at least equal to five. The first is to form combinations of categories that are theoretically
meaningful: Since we calculated transition matrix based on seven macro-categories and not
the 44 micro-codes, we have already taken advantage of this first analysis strategy. A second
strategy is to pool sessions (i.e., meetings) (cf., Klonek et al., 2014; Kauffeld & Myers,
1997). Sometimes – as in our analysis -, both strategies can be necessary.
With GSEQ, we tallied two 7x7 matrices with all act4teams behaviors at lag0 (table
rows) and act4teams behaviors at lag1 (table columns). The first sequential matrix was
obtained by pooling the joint frequencies over all verbal behaviors from meetings of the first
project phase (meetings 1 – 12, with total tables = 14,169), whereas the second sequential
matrix was obtained by pooling the joint frequencies of all verbal behaviors from the second
project phase (meetings 13 – 24, with total tables = 12,404).
-----------------------------------13
Insert Table 3 about here
-----------------------------------Lag0 to lag1 joint frequencies and their adjusted residuals (ADJR) from the first
project phase meetings are shown in Table 3. Joint frequencies are not equally distributed for
all behavioral codes: Row totals for negative procedural communication is less than 30;
therefore, we decided not to interpret adjusted residuals for this code.
-----------------------------------Insert Table 4 about here
-----------------------------------Lag0 to lag1 joint frequencies and their adjusted residuals from the second project
phase meetings are shown in Table 4. Again, row totals for negative procedural
communication was too small and precluded further analysis of this category.
Is there a general association between verbal group behaviors? At first, we tested
for a global association within the sequential matrix of joint frequencies. A significant χ²value indicates that associations between lagged variables are not determined by chance (i.e.,
given codes are followed more often by some codes and less often by others). If there is a
global sequential association, researchers can investigate which specific pairs of codes are
associated. In our analysis, both matrices indicated an overall significant non-random pattern
(for the first project phase: χ²(36) = 298.25, p < .01; for the second project phase: χ²(36) =
623.34, p < .01). Therefore, we further looked for cell-specific associations.
How can we test sequential hypotheses about dynamic group interactions? In
order to test specific hypotheses about sequential effects, we used GSEQ to calculate
adjusted residuals (Bakeman & Quera, 2011). Adjusted residuals are standardized raw
residuals (based on the difference between the observed and expected frequency). This cellspecific statistic reveals whether sequential relationships between adjacent behaviors are
14
more or less likely to be expected by chance. If adjusted residuals are > 1.96, there is a
significant positive association; if adjusted residuals < -1.96 there is negative association
between adjacent behaviors, respectively.
In our first hypothesis, we expected that positive procedural statements inhibit the
emergence of negative action-oriented statements. Our results support this hypothesis as
there was a significant negative sequential association between positive procedural
communication and negative action-oriented communication in both the first project phase
(ADJR(P+ AO-)= -3.42, p <.01) and the second project phase (ADJR(P+ AO-)= -3.35, p <.01).
In our second hypothesis, we expected that negative action-oriented statements
facilitate further negative action-orientation. For meetings from the first project phase, we
found no significant sequential associations for negative action-oriented behavior (ADJR(AO= -0.1, p = .92), that is, this result does not support our second hypothesis. However, in
 AO-)
the second project phase, negative action-oriented behavior was significantly sequentially
associated with itself (ADJR(AO- AO-)= 5.94, p < .01). Overall, meeting interactional patterns
from the first project phase are at odds with the findings from Kauffeld and Meyers (2009)
whereas meetings from the second project phase underpin a sequential group dynamic
patterns of negative action-oriented behavior.
In our research question, we asked whether group dynamic communication patterns
are stable over the course of a project. In our example, we compared transition matrices in
the first half and second half of the project. This comparison revealed that the inhibitory
effect of procedural communication on negative action-orientation was consistent across
both project phases, whereas the dynamic sequential pattern for negative action-orientation
was not stable and only emerged in the second project half.
How large are sequential effects of the group interactions? Since values of
adjusted residuals are dependent on the overall sample size of the transition matrix, it is not
15
possible to compare effects across tables. To determine the strength of association, we used
GSEQ to compute Yule’s Q – an index that is independent of sample size and ranges from +1
for a perfect positive relationship to –1 for a negative relationship. Effect sizes like Yules Q
are easier to interpret as it is similar to the more familiar correlation coefficient. In our data,
the inhibitory effect of positive procedural communication on negative action statements was
moderate (Q(P+  AO-)= -.47 in the first phase) to strong (Q(P+  AO-)= -.61 in the second
phase). The self-enhancing effect of negative action-orientated statements on themselves was
close to zero in the first phase (Q(AO- AO-)= .02) and strong in the second project phase (Q(AO AO-)
= .73)
Discussion
The current paper has shown how to analyze group dynamics using video-recorded
observations of project meetings. Overall, we built on previous work from Kauffeld and
Meyers (2009) who reported patterns of negative action-oriented communication in meetings
and positive effects for positive procedural statements on complaining behavior. Our study
extended this work by investigating the same type of group communication behavior within
a project team over an extended period of time.
The previous sequential study about team dynamics in work meetings has shown that
negative action-oriented behavior is less likely to appear in group meetings if preceded by
positive procedural statements (i.e., clarifying goals, time management etc.; Kauffeld &
Meyers, 2009). The current study has replicated this finding. Furthermore, we presented an
effect size measure (i.e., Yules Q) that can be reported for sequential associations.
Kauffeld and Meyers (2009) also reported group dynamic effects for negative actionoriented statements, that is, complaints by one co-worker were more likely if preceded by a
previous complaint. The current study investigated these micro-sequential patterns using a
larger timeframe (i.e., consecutive meetings). Sequential patterns between negative action16
oriented behaviors did not occur in the first project phase and only emerged in the second
project phase. Kauffeld and Meyers (2009) suggested that complaining cycles are more
likely for groups who share a common work history (Kauffeld & Meyers, 2009). As the
temporal stability of project teams only evolves gradually over time (Hollenbeck, Beersma,
& Schouten, 2012), our results support the idea that sequential micro-dynamics of negative
action-oriented statements should only occur after the team members were familiar to each
other (cf., Kauffeld & Meyers, 2009). In other words, patterns in negative action-oriented
behavior were not stable over time and this result theoretically contributes to the assumption
that group dynamics can change over larger temporal periods (cf., Leenders et al., 2015).
This result also supports the groups-as-complex system approach which assumes that
environmental constraints like project phases can impact group dynamic patterns (Arrow et
al., 2004). It is noteworthy that the positive effects of procedural communication was
independent of the project phase – speaking for the stability of this specific communication
behavior. Overall, our results indicate that temporal stability applies only to specific
communication behaviors (i.e., procedural statements) and therewith extends theorizing
about temporal stability of group dynamics. Furthermore, our analysis exemplifies how
sequential dynamics between group interactions might be moderated by macro-temporal
group developments and that this temporal stability needs to be considered in group
dynamics research (Leenders et al., 2015).
Pitfalls and Problems of Sequential Analysis
The coding scheme from the present study has been used in several previous studies
(e.g., Kauffeld & Lehmann-Willenbrock, 2012; Kauffeld & Meyers, 2009; LehmannWillenbrock et al., 2013). However, these studies only reported a global reliability measure
(i.e., overall kappa coefficient) while using category measures in subsequent analyses.
Hewes (1985) pointed out that an underreporting of category-specific reliability can result in
17
systematic biases – particularly when testing temporal dependencies in sequential analyses.
Therefore, the current study also reported category-specific reliabilities (i.e., ICCs and
kappas). We found low reliabilities for negative procedural communication, therewith
underpinning Hewes’s claim (1985) to report category specific reliabilities.
Overall, we recommend to report category-specific reliability based on summary
measures (e.g., ICCs) when interested in static snapshots of group behavior (i.e., aggregating
verbal categories over a time, cf., Weingart, 1997). For example, researchers could ask “how
is the frequency of action-oriented communication related to meeting satisfaction measured
via questionnaires?” (cf., Kauffeld & Lehmann-Willenbrock, 2012). In this case, reporting
category-specific ICCs are sufficient as it only takes into account agreement about code
frequencies (cf., Klonek, Quera et al., 2015). However, when looking at dynamic
interactions (e.g., does negative action-oriented behavior facilitate itself?), we need to ensure
high point-by-point reliability. Researchers can either consider statistical techniques that
remove bias of unreliable categories or remove them in subsequent analyses (cf., Hewes,
1985).
The current study used a pre-parsed data-set. When researchers use independently
parsed (i.e., unitized) and coded data, we recommend to also report unitizing reliability
(detailed descriptions are given in Guetzkow, 1950; Weingart, 1997). This indicates how
well observes consistently segmented the stream of interaction. Overall, sequential analyses
can produce systematically biased parameter estimates when assuming that data was coded
without error (Hewes, 1985). High quality coder training, reporting of code specific
reliabilities, and removing the effect of unreliable categories with statistical techniques (cf.,
Hewes, 1985; Holsclow, Hallgren, Steyvers, Smyth, & Atkins, 2015) can minimize this
problem.
18
In our study, we pooled across several meetings to obtain sequential associations of
group interactions. Even though this approach is necessary when table statistics from
individual sessions are too small (Bakeman & Quera, 2011) and is in line with previous
research (Kauffeld & Meyers, 2009), our results are somehow limited. Our homogeneity test
indicated that sequential associations were not similar across all 24 meetings. Therefore, we
ran sequential analyses separately for the first and the second project half and showed that
sequential associations changed on a macro-temporal scale. Tests of homogeneity or
stationarity (O’Connor, 1999) can help to uncover factors that are responsible for nonhomogenous associations between meetings. For example, we cannot exclude if certain
individual factors (e.g., particular group compositions) might have contributed to the result
that negative action-oriented behavior patterns were absent in the first half of the project.
Future longitudinal studies should replicate our results using project meetings that maintain
the teams’ participant composition.
Finally, our current study has used naturally occurring group interactions and the
analysis does not allow us to draw causal conclusions. Causal inferences can only be made
by employing a methodological design in which the study coordinator can purposefully vary
the conditions of an independent variable (e.g., by using a confederate meeting facilitator
that shows positive procedural communication at previously determined time points).
Practical Implications for Applied Research
Even though observational research is labor-intensive, it has particular value for
designs in applied research fields. First, observational methods are less obtrusive in
comparison to self-reports in repeated measurement designs (Hill, White, & Wallace, 2014)
because participants are disburdened from filling out questionnaires. Especially in applied
field research, participants are often reluctant to fill out questionnaires due to time and work
priorities (e.g., Barclay, Todd, Finlay, Grande, & Wyatt, 2002). Observational methods are
19
an elegant solution to this problem because they allow for the investigation of interaction
processes based on information that participants naturally emit.
Second, participants cannot be sensitized for the construct under investigation, that is,
they could not know that we studied their procedural or action-oriented behavior in relation
to project phases. We could also have focused on participants’ interpersonal styles
(Schermuly & Scholl, 2012), change readiness (Klonek, Paulsen, & Kauffeld, 2015), and
even linguistic style matching (Taylor & Thomas, 2008). The data captured on video is rich
and allows for manifold analytical possibilities. In contrast, filling out surveys requires to
cognitively and actively process information when responding to items, which in turn may
affect the quality of the data (Rogelberg, Fisher, Maynard, Hakel, & Horvath, 2001).
Third, findings from sequential analyses are easier to translate for practitioners. In
our research example, we were able to identify specific behaviors (i.e., procedural
statements) that can potentially inhibit dysfunctional meeting behaviors. Furthermore,
mutual facilitation of dysfunctional behaviors like complaining are more likely to occur
when project teams have already worked for an extended period. Practically, these findings
help trainers, supervisors, or group facilitators to make deliberate choices about when (i.e., in
the second project half) and how to behaviorally intervene (e.g. by using a procedural
suggestions) when negative group dynamics like complaining cycles occur. In a similar vein,
researchers in the field of clinical psychology have started using sequential analyses to test
hypotheses in socio-interactional interventions like motivational interviewing (e.g., Apodaca
et al., 2016; Gaume, Gmel, Faouzi, & Daeppen, 2008). The results from this line of research
can help to empirically identify which therapist behaviors either facilitate or inhibit
motivational client statements and thusly mediate the success of behavior-change
interventions (Moyers, Martin, Houck, Christopher, & Tonigan, 2009). While most
sequential research in motivational interviewing focuses on dyadic interactions (therapist-
20
clients), future research could further test how these interpersonal dynamics transfer to
group-based motivational interventions (e.g., Osilla et al., 2015).
Conclusion
The present study presented methodological advice how to use sequential analyses
for the study of video-recorded meetings. Timed-event data allows researchers to investigate
how different group behaviors are interrelated and dependent on contextual factors - instead
of looking at static information. Overall, this study showed the potential of studying the
dynamic nature of group communication behavior using sequential analyses. We have given
methodological guidelines and extended research on the dynamic effects of procedural and
action-oriented communication team member micro-interactions. In short, we replicated
inhibitory effects of positive procedural communication on negative action-oriented
statements. Furthermore, we have provided first evidence that negative action-oriented
behavior patterns only occur when team members already share a working history and not in
the beginning of a project phase.
21
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Table 1
Table 1.
Coding scheme for verbal behavior in project team meetings (adapted from Meinecke & Lehmann-Willenbrock, 2015)
PROBLEM-FOCUSED (PF+)
Problem
Describing a problem
Connections with problems
Defining the objective
Solution
Describing a solution
Problem with a solution
Arguing for a solution
Organizational knowledge
Knowing who
Question
POSITIVE
PROCEDURAL (P+)
POSITIVE
SOCIO-EMOTIONAL (SE+)
POSITIVE
ACTION-ORIENTED (AO+)
Goal orientation
Clarifying
Procedural suggestion
Procedural question
Prioritizing
Time management
Task distribution
Visualization
Summarizing
Encouraging participation
Providing support
Listening
Reasoned disagreement
Giving feedback
Humor
Separating opinions from facts
Expressing feelings
Offering praise
Expressing positivity
Taking responsibility
Action planning
NEGATIVE
PROCEDURAL (P-)
NEGATIVE
SOCIO-EMOTIONAL (SE-)
NEGATIVE
ACTION-ORIENTED (AO-)
Losing the train of thought
(running off topic)
Criticizing/backbiting
Interrupting
Side conversations
Self-promotion
No interest in change
Complaining
Seeking someone to blame
Denying responsibility
Empty talk
Ending the discussion early
Table 2
Table 2.
Agreement and Confusion Matrix, Code-specific Kappas and Intra-Class Correlations for Five
Double-coded Meetings
Observer 2
Observer 1
PF
P+
P-
SE+
SE-
AO+
AO-
Total
Kappa
ICC
PF
2,537
119
11
49
12
75
35
2,838
.79
.97
P+
114
515
1
36
1
29
4
700
.71
.81
P-
2
0
8
0
0
0
0
10
.42
.36
SE+
76
19
5
970
3
67
6
1,146
.81
.91
SE-
4
1
0
5
288
1
2
301
.94
.98
AO+
104
22
3
74
2
619
4
828
.73
.97
AO-
22
2
0
6
3
2
87
122
.66
.93
Total
2,859
678
28
1,140
309
793
138
5,945
Note. PF= Problem-focused communication, P = Procedural communication, SE = Socioemotional communication, AO = Action-oriented communication; + = Positive, - = Negative
Table 3
Table 3.
Joint Transition Frequencies and Adjusted Residuals for Lag1 Analysis from the First Project
Phase
Joint Frequencies
Target Behavior (Lag1)
Given
behavior
(Lag0)
PF
P+
PSE+
SEAO+
AOTotals
PF
P+
P-
SE+
SE-
AO+
AO-
Totals
4,343
895
15
1,109
260
1,075
106
7,803
787
318
3
242
65
235
26
1,676
11
2
1
5
1
5
0
25
1,193
245
1
267
72
247
41
2,066
206
37
3
80
51
62
19
458
1,138
188
1
238
54
253
37
1,909
123
11
1
51
16
26
4
232
7,801
1,696
25
1,992
519
1,903
233
14,169
Adjusted Residuals
Target Behavior (Lag1)
Given
behavior
(Lag0)
PF
P+
PSE+
SEAO+
AO-
PF
P+
P-
SE+
SE-
AO+
AO-
1.59*
-2.03**
0.5
0.58
-2.32*
1.34*
-2.96**
-7.1**
9.41**
0.03
0.48
0.5
0.76
-0.32
-1.11
-0.61
4.56**
0.86
0.09
0.96
-0.65
2.66**
-0.17
-1.5
-1.61
-0.47
-2.13*
1.32
-4.41**
-2.61**
2.48**
2.13*
8.65**
0.07
4.28**
4.3**
-3.07**
-1.39
-2.15*
-2.09*
-0.24
1.08
-0.63
-3.42**
0.93
3.5**
2.64**
-1
0.1
Note. χ²(36)= 298.25**; * p < .05. ** p < .01;
PF= Problem-focused communication, P = Procedural communication, SE = Socio-emotional
communication, AO = Action-oriented communication; + = Positive, - = Negative
Table 4
Table 4.
Joint Transition Frequencies and Adjusted residuals for Lag1 Analysis from the Second
Project Phase
Joint Frequencies
Target Behavior (Lag1)
Given
behavior
(Lag0)
PF
P+
PSE+
SEAO+
AOTotals
PF
P+
P-
SE+
SE-
AO+
AO-
Totals
3,514
852
4
1,140
210
941
72
6,733
814
263
3
238
53
182
13
1,566
6
0
2
1
2
1
1
13
1,236
234
2
285
43
233
23
2,056
147
42
1
51
46
31
3
321
955
190
0
222
27
161
17
1572
78
5
0
26
7
18
9
143
6,750
1,586
12
1,963
388
1,567
138
12,404
Adjusted Residuals
Target Behavior (Lag1)
Given
behavior
(Lag0)
PF
P+
PSE+
SEAO+
AO-
PF
P+
P-
SE+
SE-
AO+
AO-
-5.43**
-0.48
-1.46
-2.07*
5.08**
1.29
5.68**
-2.09*
0.01
-3.14**
0.16
1.25
5.39**
-0.89
-1.32
0.03
-3.35**
-0.37
3.68**
-0.06
4.91**
-0.5
-0.73
0.62
-1.29
-1.14
-0.6
-1.38
17.74*
*
-0.8
2.54**
-0.54
2.26*
-2.67**
-2.96**
-1.94*
0.03
0.03
11.68**
-1.63
-0.31
-1.98*
-3.44**
-3.05**
-0.13
0.78
1.22
-0.02
5.94**
Note. χ²(36)= 623.34**; * p < .05. ** p < .01;
PF= Problem-focused communication, P = Procedural communication, SE = Socio-emotional
communication, AO = Action-oriented communication; + = Positive, - = Negative
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