Question

Developmental Psychology:

Provide two shapes of idealized developmental change and an example of each.

Answer 1

Idealized shapes of developmental change, with age shown on the X-axis and an index of behavioral expression or level of performance on the Y-axis. (a) Linear, (b) Accelerating, (c) Asymptotic, (d) Step-like, (e) S-shaped, (f), Variable, (g) Unsystematic, (h) Stair-climbing, (i) U-shaped, (j) Inverted-U-shaped.

Few examples of developmental research have systematically assessed the empirical costs and benefits of large and small sampling intervals on descriptions of developmental change. Anotable exception is Lampl and colleagues’ research on patterns of physical growth (Johnson,Veldhuis, & Lampl, 1996; Lampl, Johnson, & Frongillo, 2001; Lampl, Veldhuis, & Johnson,1992). Traditionally, children’s growth is characterized as a continuous function from birth to adulthood, with more rapid growth rates during infancy and adolescence. However, when children’s height is measured every day, growth appears to be episodic. Infants’ height, for example, can increase 1.65 cm in the course of a single day, separated by long periods of days or weeks during which no growth occurs. Sampling at weekly intervals results in developmental trajectories that preserve the episodic nature of children’s growth but reduce the observed number of growth spurts, increase the amplitude of the spurts, and prolong the periods of stasis.And sampling at quarterly or yearly intervals, as in traditional studies of growth, results in the smooth, continuous growth curves on standard growth charts.Even within a 24-hour period, growth is not continuous. In a tour de force of micro-measurement, Lampl and colleagues (Noonan et al., 2004) demonstrated episodic growth on two time scales: brief periods of substantial growth on a scale of minutes and days, flanked by long periods of no growth on an hourly and weekly scale. Leg growth in freely moving lambs was measured with a micro transducer surgically implanted across the tibial growth plate. Bonelength was sampled at 167-sec intervals over a period of 3 weeks, synchronized with video recordings of the lambs’ activity. Periods of bone growth revealed by the micro transducercoincided with periods of recumbency revealed by the video recordings, and periods when bones did not grow coincided with periods of loading the limbs in stance or locomotion. Theauthors calculated that 90% of bone growth occurs while lying down, even though lambs spend just over 50% of their time in a recumbent position, and little or no growth occurs while standing or walking. Clearly, tradition, intuition, and convenience that informed traditional studies of physical growth have been inadequate for capturing the richness of the actual trajectory.The case of physical growth shows how increased sampling resolution from years to days to minutes can provide novel insights into developmental process. The episodic growth pattern from minute to minute indicates that bones lengthen only when compressive forces on the leg are absent. Paradoxically, other research has demonstrated that the presence of physical forces applied to bone promote growth by stimulating the expression of genes that regulate cartilage and bone formation (Muller, 2003). Together, these research findings imply that cellular processes involved in regulating physical growth must be coordinated and synchronized on atemporal scale previously unsuspected.As exemplified by the research on physical growth, overly large sampling intervals will cause interval data to appear smooth and continuous, regardless of whether the underlying trajectoryis episodic or U-shaped. Similarly, overly large sampling intervals will distort the shape of change for binary data (skills that are indexed as absent or present). Figure 2 shows the potential impact of sampling at monthly intervals on characterizing patterns of development using actual data from daily observations of two infants’ progress in balancing upright. The top panel (A)shows a step function, where the infant exhibited a single transition from not-standing to standing, from one day to the next. The bottom panel (B) shows a variable developmental function, where standing was expressed intermittently (21 times) over a protracted transition period of several weeks. For skills with variable trajectories and reversals, interpolating over the existing data points—which is what all developmental researchers do when measurements are collected at weekly, monthly, and yearly intervals—can distort the shape of the developmental trajectory. Infrequent observations will cause binary data to appear as a step function, with a single abrupt transition, regardless of whether the underlying trajectory is variable, with a series of reversals. As illustrated by the gray curves in the figure, the variable data in (B) will appear to follow the same developmental path as the stage-like data in (A)