Answers the Question
How should you approach the analysis of longitudinal data that may possess dynamic cycle of influence among multiple variables?
How it Began
Research interest in dynamic processes are increasing in the field of organisational psychology, at the same time the length and complexity of longitudinal data structures have increased. Longitudinal data are increasingly important in the study of organizational behaviour. However, the current models used to represent the patterns present in longitudinal data are largely limited to the study of recursive relations (i.e., HLM and SEM). This is inconsistent with what we know about the self-regulated functioning of organizations, teams, and individuals where feedback loops and cyclical processes are thought to be the norm.
Multivariate statistical analysis – a large set of algorithms used to identify patterns of dependence existing between variables that share the same probability of distribution.
Static Dimensionality – the ordinary factorial representation of performance
Dynamic Dimensionality – temporal factors influencing the performance domain
Individual Dimensionality – variability in the type of performance across persons in the same job.
Recursive Relations – Recursion is the process of repeating items in a self-similar way. In mathematics, a recurrence relation is an equation that recursively defines a sequence.
Questions about the dynamic processes that drive behaviour at work have been the focus of increasing attention in recent years. Models describing behaviour at work and research on momentary behavior indicate that substantial variation exists within individuals. Of central interest to applied psychologists is how to define job performance, both conceptually and operationally. Most validation research treats job performance as a monolithic and static construct. There is considerable empirical evidence that job performance is multidimensional and it is possible that job performance is not stable over time. In fact, job performance data can usually be classified by three modes: the individuals assessed, the variables measured, and the times of measurement providing systematic sources of variance in job performance data, that is, multidimensionality. Organizational Science has advanced calls for job performance studies that include changes over time, referring to this approach as multivariate dynamic.
In order to validly measure the frequency and the patterning of mental processes in everyday-life situations procedures are needed that capture variations in self-reports of those processes. To this end, experience sampling methodology has been developed in which a participant at random or specific times has to report on his or her mental state or those activities in which he or she is involved at that moment.
If performance changes over time, it would useful to find predictors of the change itself. Personality measures have been used in addition to cognitive ability measures to predict individual growth curves for performance criteria. Research indicates that both cognitive ability and conscientiousness predict initial academic performance, but only conscientiousness predicts performance trajectories. This may happen because early performance is a transition phase of skill acquisition and later performance is a maintenance phase.
Workplace behaviour comes in two basic kinds: affect driven and judgment driven. Workplace events cause affective reactions, and these affective reactions directly influence affect-driven behavior. But these affective reactions also influence job attitudes that in turn directly influence judgment-driven behaviours.
Affective events theory hypothesizes that momentary affect should thus show stronger relationships with momentary behaviours such as work withdrawal (e.g., taking long coffee breaks or surfing the Web) and that job attitudes should have stronger relationships with more considered behaviours such as job withdrawal (e.g., job search, turnover intentions, quitting). Furthermore, it is expected that individual differences in personality will moderate both the link between events and affect and predict the affective reactions themselves.
The episodic process model suggests that there will be important momentary fluctuations in the affective and regulatory resources available for employees to apply to performance behaviours. This model articulates reasons performance behaviours should meaningfully vary within persons over short time periods. For example, if my supervisor yells at me, and I then need to interact with a client, I may have to regulate my emotional display to appear positive to the client. This act of emotion regulation uses up some of my regulatory resources and may therefore make it more difficult for me to focus my attention on a report I need to write later in the day.
Obtaining evidence requires research designs that are capable of untangling both within- and between-individual variability.
What does this mean for Organizational Development?
Dimensions in the individuals mode differentiate types of employees. For example, two salespersons may provide the same economic benefit to the organization, but one contributes by directly making sales, whereas the other contributes by cre- ating goodwill, encouraging customers to make purchases throughout the store. Such individual difference dimensions of performance could be important in a variety of situations. If the organization derives the same economic benefit from different employees in different ways, these differences should be reflected in selection and reward systems.
Static dimensionality refers to the latent structure of the variables measuring job performance. Historically, the study of job performance was characterized by a search for the “ultimate criterion,” a comprehensive index of performance. It has been pointed out that this is an inappropriate way to conceptualize performance. There is evidence across many jobs that overall job performance can consist of as many as eight dimensions. At minimum, organizational development consultants researching job performance as part of an organisational diagnostic should consider both task performance, the technical core of the job, and contextual performance, the social and nontechnical contributions an individual makes at work.
These dimensions have been found to independently contribute to supervisors’ perceptions of their subordinates’ overall job performance. For instance, consider two salespeople who have equal sales. One is known to be a loner, whereas the other gives advice and assistance to coworkers. The latter will be viewed as the superior performer due to the social contributions this salesperson makes.
The dynamic nature of performance criteria is also important to consider for employee selection.
Experience sampling methods are ideally suited to explore dynamic models of work behaviour because measurements may be taken throughout the work day on several variables. Experience sampling data are three-mode (Persons x Variables x Occasions) and are frequently analyzed with multilevel models with the occasions mode nested within persons.
The data been collected as part of an organizational diagnostic with the intent of examining structure and dynamics, requires that the performance variables would need to be systematically sampled from the repertoire of performance behaviours. Self-reported measures may not accurately represent what a worker is actually doing.
Diagnosis of the job performance domain and its three sources of variance should be examined across a sample from the population of jobs. This requires collecting experience sampling and other longitudinal data in many organizations with many different types of employees.
Data should be collected using multiple methods and examined using multiple analytic procedures. Such a strategy will allow for a scientific understanding of the dynamic interaction of individual and workplace attributes in the study of organizational behaviour.
Spain, Seth M. , Miner, Andrew G. , Kroonenberg, Pieter M. and Drasgow, Fritz(2010) ‘Job Performance as Multivariate Dynamic Criteria: Experience Sampling and Multiway Component Analysis’, Multivariate Behavioral Research, 45: 4, 599 — 626