Uploaded by User58322

Human Factors

advertisement
Human Factors: The Journal
of the Human Factors and
Ergonomics Society
http://hfs.sagepub.com/
Light Pen Use and Practice Minimize Age and Hand Performance Differences in
Pointing Tasks
Neil Charness, Patricia Holley, Jeffrey Feddon and Tiffany Jastrzembski
Human Factors: The Journal of the Human Factors and Ergonomics Society 2004 46: 373
DOI: 10.1518/hfes.46.3.373.50396
The online version of this article can be found at:
http://hfs.sagepub.com/content/46/3/373
Published by:
http://www.sagepublications.com
On behalf of:
Human Factors and Ergonomics Society
Additional services and information for Human Factors: The Journal of the Human Factors and Ergonomics
Society can be found at:
Email Alerts: http://hfs.sagepub.com/cgi/alerts
Subscriptions: http://hfs.sagepub.com/subscriptions
Reprints: http://www.sagepub.com/journalsReprints.nav
Permissions: http://www.sagepub.com/journalsPermissions.nav
Citations: http://hfs.sagepub.com/content/46/3/373.refs.html
>> Version of Record - Jan 1, 2004
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
What is This?
Light Pen Use and Practice Minimize Age and Hand
Performance Differences in Pointing Tasks
Neil Charness, Patricia Holley, Jeffrey Feddon, and Tiffany Jastrzembski, Florida State
University, Tallahassee, Florida
We contrasted performance with mouse and light pen input devices for younger,
middle-aged, and older adults (N = 72) who were experienced mouse users. Participants used both preferred and nonpreferred hands to perform a menu target
selection task. The light pen minimized age differences in performance relative
to the mouse. Older adults were more lateralized on a handedness test than
young adults and were less efficient using their nonpreferred hand. With practice,
older adults improved their response time more than other age groups did. The
mouse was rated as more acceptable and easier to use than the light pen across
trials, despite the performance advantage of the light pen for all age groups.
Usability ratings correlated moderately with performance. A benefit-cost analysis
indicated that the more efficient light pen might cover its greater initial cost within 11 months for an older adult and within 23 months for a younger adult. Actual
or potential applications of this research include advising older adults to persist
with practice for new input devices, advising those who must switch to their nonpreferred hand to select a direct positioning device, and providing a methodology
for determining the potential payback interval when switching to a faster, though
more expensive, input device.
INTRODUCTION
Computer systems are ubiquitous in work
environments and are becoming so in homes.
Although approximately 51% of households in
the United States reported having a computer
system as of August 2000, older adults lagged
in adopting them for personal use (Newburger,
2001). Newburger noted that fewer than 30%
of households headed by someone 65 years or
older reported owning a computer. In the second
half of 2000 only about 15% of adults aged 65
years or older reported having Internet access,
compared with about 56% for the U.S. population as a whole and 75% for those aged 18 to
29 years (Rainie & Packel, 2001). Computer system usability problems may be one reason older
adults tend to lag behind their younger counterparts in computer ownership and Internet access
(e.g., see Charness, 2003). Research indicates
that novice older adults have difficulty using a
mouse as a pointing device. Walker, Philbin, and
Fisk (1997) showed that novice older adults
were less accurate in using a mouse in a target
acquisition task than were their younger counterparts. Adjustments they made to the interface
(acceleration profiles) did minimize the differences between the two groups.
In a prior study, Walker, Millians, and Worden (1996) showed that older experienced mouse
users have similar problems using a mouse and
that their performance also improved with adjustments to acceleration functions. Smith,
Sharit, and Czaja (1999) also showed that novice
older adults experienced much greater difficulty
than did novice younger adults in pointing tasks,
particularly for double-clicking operations.
Chaparro, Bohan, Fernandez, Choi, and Kattel
(1999) showed that older adults exhibited slower performance with both a mouse and a trackball and exhibited higher ratings of perceived
exertion with the mouse, as compared with
Address correspondence to Neil Charness, Department of Psychology, Florida State University, Tallahassee, FL 323061270; [email protected] HUMAN FACTORS, Vol. 46, No. 3, Fall 2004, pp. 373–384. Copyright © 2004, Human
Factors and Ergonomics Society. All rights reserved.
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
374
Fall 2004 – Human Factors
younger adults. Charness, Kelley, Bosman, and
Mottram (2001) found significantly more mouse
errors during word-processing training for older than for younger novice adults, although this
was not true for experienced word processors.
Others have noted problems in the ability of older adults to control fine motor movements (e.g.,
Jagacinski, Liaou, & Fayyad, 1995; Liao, Jagacinski, & Greenberg, 1997).
A few mouse use properties may pose greater
difficulty for older adults than for younger
adults. First, the mouse is an indirect pointing
device (see Greenstein, 1997) in the sense that
the user must map the movement of the device
in one plane (on the mouse pad or desk surface) with movement of a cursor on a different
plane (the computer screen). Second, normal
mouse settings provide some gain to the device
such that both velocity and acceleration in the
plane of the mouse surface is augmented in
the plane of the screen. There is considerable
evidence that older adults experience significant
declines in spatial abilities (e.g., Salthouse,
Mitchell, & Palmon, 1989) that might support
this mapping and that older adults are generally slower on translation activities, such as those
found in the digit-symbol substitution test (Salthouse, 1992). It is also the case that older adults
show systematic slowing in many aspects of
behavior (Salthouse, 1996).
Not all input devices require similar mapping operations. Touch screens and light pens
offer a “where you point is where you go”
(WYPIWYG) operation for cursor control. They
also exhibit no gain. If it is the case that age
differences in mouse use are partially accounted for by age differences in mapping operation
efficiency, providing a direct positioning device
should minimize these differences relative to
an indirect positioning device such as a mouse.
In this project we chose to compare the usability
of both direct (light pen) and indirect (mouse)
pointing devices for younger, middle-aged, and
older adults.
The first goal for this experiment was to test
hypotheses positing that difficulties in using a
mouse may be attributable to age-related declines in spatial ability. To do this, we compared
the effectiveness of the two types of input device for younger, middle-aged, and older adults
who were experienced mouse users. We selected
experienced individuals for several reasons. First,
we found it nearly impossible to recruit younger
novice adult computer users over a 1-year period, given the ubiquity of computers in the school
systems. Second, we wanted a conservative test
of the potential advantage of a light pen. Finally,
given that future cohorts of older adults are
more likely to be experienced users of computer
systems, we wanted to be able to generalize our
results to the case of experienced users, contrasting this study with prior studies that have
examined mouse use by novices.
A second goal for this study was to contrast
the use of preferred and nonpreferred hands
for pointing tasks with direct and indirect positioning devices. Chronic conditions leading to
impairments and disability show striking increases with age, particularly past the age of 65
(e.g., Benson & Marano, 1998). Hence, older
adults in particular are more likely than younger
ones to experience injury (e.g., musculoskeletal
disorders) or disease (e.g., stroke, arthritis) resulting in loss of function of the preferred hand.
We wanted to examine the case of adaptation
to use of the nonpreferred hand, particularly
for older adults. Prior research showed a complicated interaction of Hand × Device × Task
for movement time in a small sample of young
adults using a mouse, stylus, and trackball (Kabbash, MacKenzie, & Buxton, 1993), although
hand was manipulated between subjects.
One factor that may mediate the ability to
switch performance to the nonpreferred hand
is degree of lateralization. Those who rely a
great deal on one hand, to the exclusion of use
of the other, may be expected to have greater
difficulty in switching. Laterality questionnaire
measures often ask for frequency of use of the
two hands or preference for use. There is some
indication in questionnaire studies that older
adults are more strongly right lateralized for
handedness than are younger adults. Gilbert and
Wysocki (1992) showed a decrease with age in
sinistrality and mixed-handedness with a twoitem (writing, throwing) questionnaire that was
returned by more than 1 million U.S. men and
women. That is, there was greater concordance
in hand use with increased age. Degree of handedness also tends to increase with age in nonhuman primates (e.g., Ward, Milliken, Dodson,
Stafford, & Wallace, 1990).
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
LIGHT PEN VERSUS MOUSE FOR MENUS
However, contrary to questionnaire reports,
studies of manual performance sometimes show
stronger handedness asymmetry in young adults.
Francis and Spirduso (2000) selected righthanded adults in their study of age and type of
motor task and showed that younger adults exhibited a greater disparity between right and left
hands than did older adults, particularly for a
Purdue Pegboard task and a tracing task. We
asked participants to use both their preferred
and nonpreferred hand to perform a pure pointing and selection (point-and-click) task and then
to rate the ease of use and acceptability of the
device, as well as their perceived workload across
practice blocks.
A third goal for our study was to trace the
effects of practice on point-and-click tasks. Although we expected to show that older adults
would be less efficient than younger ones in
pointing tasks and that the preferred hand would
be superior to the nonpreferred hand, we were
particularly interested in whether practice would
interact with the factors of age, hand, and device. For instance, the age-complexity hypothesis
postulates greater age differences in performance
with more difficult tasks (e.g., Cerella, Poon, &
Williams, 1980), and skill acquisition studies
have shown greater performance gains by older
adults across initial trial blocks (e.g., Charness
& Campbell, 1988). Data could be used to provide recommendations for the type of input
device to be adopted when people must switch
to the use of their nonpreferred hand.
The final goal for our study was to examine
the influence of individual difference factors
(e.g., degree of handedness; manual dexterity;
and various visual, psychomotor, and memory
factors) on efficiency with direct and indirect
pointing devices.
METHOD
Design and Participants
The design formed a 3 × 2 × 2 × 5 mixedmodel factorial. Age (young, middle-aged, or
old) was a between-subjects variable. People
were selected for ages 18 to 25, 45 to 55, and
65 to 75. Hand (preferred and nonpreferred),
device (mouse and light pen), and practice blocks
(Blocks1–5) were within-subject variables. Within each age group, 24 participants were pre-
375
sented with each of four conditions (preferred
hand/mouse; nonpreferred hand/mouse; preferred hand/light pen; and nonpreferred hand/
light pen), with the order of presentation counterbalanced across participants within age
groups. All participants received five blocks of
trials on each combination.
The young adults (M = 21.5 years) were recruited from undergraduate psychology classes
or the community and received either extra
course credit or a stipend for their participation.
The middle-aged adults (M = 50 years) and
older adults (M = 69.5 years) were recruited
from the community and were paid a stipend for
their participation. All were experienced mouse
users in terms of the criterion of having at least
5 hr of prior mouse use.
We also gathered a large set of demographic
and psychometric measures in two sessions
that took place before the experiment as part of
a larger project (Czaja et al., 2004). Participants
were given both group-administered (Session 1)
and individually administered (Session 2) tests
of visual and auditory acuity, perception, memory, attention, and reaction time. In addition we
administered the Grooved Pegboard (Lafayette
Model 32025) test of manual dexterity to use
as a predictor variable so that we could assess
to what extent age-related differences in manual
dexterity might mediate age-related differences
in menu selection performance.
Apparatus and Materials
The experiment was conducted at PC workstations equipped with a FastPoint Technologies, Inc., light pen (donated by FastPoint) and a
Microsoft Intellimouse™, a two-button mouse.
The light pen was attached to a Universal Serial Bus (USB) hub to the right of the computer
with a flexible cable and had a clickable end
point. It operates a software cursor via a device
driver program that interacts with the graphics
card to determine when a screen pixel is activated via the left-to-right and top-to-bottom
scan pattern for the CRT. Pressing the tip of
the light pen against the screen registers the
equivalent of a mouse click using the device’s
driver. A program written in Visual C++ provided the menu-based task using standard
Windows™ controls such as drop-down menus
and point-and-click activation (see Figure 1).
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
376
Fall 2004 – Human Factors
Figure 1. Partial screen display of the menu acquisition task.
It also contained embedded subprograms to
measure handedness, preference and acceptability ratings, and workload.
As shown in Figure 1, the menu bar of the
program contained four menu items on the left,
labeled “file,” “view,” “tools,” and “help,” consecutively. (An “experimenter” menu item on
the far right of the screen could be used by the
experimenter to terminate the program.) Each
menu box held a drop-down menu containing
target boxes labeled “1,” “2,” and “3,” consecutively with a maximized window setting on a
19-inch (48-cm) Gateway VX900 monitor that
was set to a resolution of 1280 × 1024 pixels.
A push button labeled “start” was located in
the center of the program window. (Figure 1 is
cropped at the bottom and on the right and
shows the label “Help 1” where “start” appears
at the beginning of the trial.)
Trials were initiated when a participant clicked
on the start button. That label was then replaced
with a target label (e.g., “Help 1”) that designated the correct target response for a trial. The
target label was written to the same screen location as the start item to allow for the possible
disadvantage for some pointing devices (e.g.,
touch screen, light pen) of obscuring the item
to be selected (Greenstein, 1997). That is, after
the start button is clicked on with a light pen,
the target that replaces the word start may not
be visible until the light pen moves away from
that location. Feedback for final target acquisition was a moderately loud 75 dB SPL 1000 Hz
tone when the participant clicked on the correct menu item. A message box area provided
trial reaction time feedback to participants and
was located just below the start/target item push
button.
The Edinburgh Handedness Inventory (Oldfield, 1971) was embedded in the general experimental program and was run first to allow for
quick determination of a participant’s preferred
hand. Two rating scales (0–10) were also embedded in the program to measure acceptability
and ease of use of a given input device at the end
of a trial block (e.g., following completion of a
block of trials with the preferred hand using
a mouse). Finally, a workload inventory, the
NASA-Task Load Index (NASA-TLX) was included in the program. The NASA-TLX scale
(Hart & Staveland, 1988) required the participant to rate the task on the basis of a number of
dimensions encompassing workload. The entire
task, consisting of two parts, was administered: paired comparison of dimensions (which
was greater – mental or physical workload) and
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
LIGHT PEN VERSUS MOUSE FOR MENUS
ratings of the degree of workload for each dimension. We used the combined workload rating in our analyses.
Procedure
Each individual experimental session began
by asking participants to complete the Edinburgh Handedness Inventory. The computer
scored handedness items and provided feedback (via a message box) that indicated the preferred hand. For all but 1 participant this was
the right hand. Next, participants were given
instructions about the menu target acquisition
task. Participants were told to move the cursor
over the start button and click it, which would
change “start” to a target menu item (e.g., “File
1”). Then, participants were told to click on the
menu item (“file”), which would initiate a dropdown menu. Finally, participants were told to
click on the target item (“1”). (All clicks were
single clicks.) All participants were instructed to
respond as quickly as possible. They were told
that the edit box located below the push button
would provide feedback about their performance
in hundredths of a second. At this point, participants were encouraged to ask questions. If participants had no questions they proceeded to the
practice block, where they practiced three trials
for each condition, starting with their preferred
hand. Throughout practice, message boxes instructed participants when to change the device
and/or hand they were using.
After the practice block, the main experimental trials were presented. At the end of each
block participants were asked to rate the acceptability and ease of use for that Hand × Device
condition on a 0 to 10 rating scale. After completing five blocks, participants responded to
the NASA-TLX for the Hand × Device condition that they had just completed. This process
was repeated for each of the remaining three
experimental conditions. Participants were then
paid for their participation and debriefed.
RESULTS
Target Acquisition Time
As is the case in normal computer menu use,
if participants made errors before selecting the
correct item, they continued until the correct
item was acquired. We conducted analyses of
377
errors, defining an error as the case where a
mouse click occurred on a nontarget item before the target item. Using analysis of variance
(ANOVA), we found a significant effect of only
hand, F(1, 69) = 9.4, MSE = 2.67, p < .01,
with slightly more errors for the nonpreferred
than for the preferred hand (0.96 vs. 1.55 errors). However, such errors were very infrequent, considering that participants completed
120 trials with each hand. ANOVA was used to
evaluate the median target acquisition time.
Medians were chosen because of the extended
response times observed on some trials (e.g.,
1 participant paused in midtrial to go to the
washroom), coupled with the small number of
trials per block, which made outlier trimming
procedures difficult. Overall, younger adults
were fastest (4042 ms), middle-aged adults were
slower (4818 ms), and older adults were slowest (5875 ms) for target selection.
All main effects were statistically significant
(p < .05): age, F(2, 69) = 26.13, MSE = 3 887 582;
hand, F(1, 69) = 106.56, MSE = 563 606; device, F(1, 69) = 148.16, MSE = 558 878; and
trial block, F(4, 276) = 66.61, MSE = 56 858. Three
interactions were statistically significant: Age ×
Device, F(2, 69) = 3.36, MSE = 563 606; Age ×
Hand, F(2, 69) = 6.45, MSE = 558 878; and
Age × Block, F(8, 276) = 4.25, MSE = 56 858.
The Age × Device interaction, displayed in Figure 2A, shows that using a light pen lessens
age differences in performance as compared
with a mouse even for this group of experienced mouse users. The Age × Hand interaction,
shown in Figure 2B, indicates that the older
adults had greater difficulty using any device in
the nonpreferred hand (550-ms disadvantage) as
compared with the middle-aged (364 ms) and
young (311 ms) adults.
The Age × Block interaction indicates that
older adults had the most to gain from practice
(341 ms, as compared with 236 ms for middleaged adults and 157 ms for young adults), as
shown in Figure 3.
The Age × Block interaction is evident only in
the first and second trial blocks, in which older
adults showed a greater gain in performance
(249 ms) than did the middle-aged (120 ms) and
younger adults (72 ms). The interaction effect
disappears if the ANOVA is restricted to Blocks 2
through 5.
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
378
Fall 2004 – Human Factors
A
B
Figure 2. A: Age × Device interaction for mean median reaction time to acquire a menu target. B: Age × Hand
interaction for mean median reaction time to acquire a menu target. Standard errors are shown for each bar.
Laterality Quotient and Manual Dexterity
The Oldfield (1971) Laterality Index ranges
from –100 (extremely left-handed) to 100 (extremely right-handed). There was only 1 strongly left-handed individual (age 51) in the sample
of 72 people, somewhat below expectations
(~11% of the population is left-handed or mixedhanded; Gilbert & Wysocki, 1992). If that person
is excluded from the sample and only righthanded participants are considered (where righthanded is anyone with a score above 0), then
there is a significant trend for older adults to be
more strongly lateralized (r = .32, p < .01).
The stronger lateralization for older adults
probably accounts in part for the interaction of
age and hand in efficiency for acquiring the
target item. The Oldfield (1971) questionnaire
items tap behavioral activities such as writing,
brushing teeth, and striking a match. The use
of the nonpreferred hand for such activities is
reported here as less frequent for older adults
than younger ones. A more accurate behavioral
check on effectiveness with the preferred and
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
LIGHT PEN VERSUS MOUSE FOR MENUS
379
Figure 3. Age × Trial Block interaction for mean median reaction time to acquire a menu target. Standard errors
are shown for each bar.
nonpreferred hands is afforded by the dexterity
indices from the Grooved Pegboard task. This
task requires people to fill a board with 25 small
pegs as quickly as possible. Both time to complete the task and noticed errors are recorded.
Error rates were very low (M = .32) and did
not vary by age, hand, or their interaction (all
p values > .15). For time to complete filling the
pegboard, there were main effects of age, F(2,
69) = 4.1, MSE = 895, p < .01, and of hand
F(1, 69) = 20.2, MSE = 68.9, p < .01. Time to
complete the task increased with age (82.4, 84.7,
and 98.6 s, respectively, for young, middle-aged,
and older adults). Time to complete the task
was greater for the left hand than for the right
hand (91.7 vs. 85.4 s). There was no interaction between age and time to complete the task,
F(2, 69) = 1.5, p > .2.
When manual dexterity in the nonpreferred
hand is entered as a covariate in the analysis of
menu target acquisition performance, the interaction between age and hand for mean median
time is no longer significant. That is, manual
dexterity mediates, in part, the differential slowing seen in using an input device in the nonpreferred hand for older adults.
Individual Differences and Performance
To what extent are the age-related differences
in performance with the light pen and mouse
mediated by individual differences in basic abilities? We regressed a mean manual dexterity
measure (averaging times across hands), together with two spatial ability measures (paper
folding test and cube comparison test), and age
on four measures of performance: mean light pen
response time across blocks for the preferred
and nonpreferred hands and mean mouse response time averaged across blocks for the preferred and nonpreferred hands. Results are
presented in Table 1.
Age was the only significant predictor for the
preferred-hand performance measures, although
there was an apparent trend for mean manual
dexterity (p = .061, p = .082, respectively). However, manual dexterity contributed independently of age for nonpreferred-hand performance
with both mouse and light pen. Consistent with
the earlier ANOVA, age was a less-strong predictor of performance for the light pen than for
the mouse. Also, manual dexterity was a stronger
predictor of response time for the light pen
than for the mouse, perhaps because people
were experienced mouse users and the extent
of physical movement was greater for the light
pen. This analysis is inconsistent with the view
that translation and mapping operations, considered to require spatial ability, are responsible
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
380
Fall 2004 – Human Factors
TABLE 1: Individual Difference Predictors of Performance
Beta
SE
β
p
(Constant)
Age
Cube comparison
Paper folding
Dexterity
1613.6
15.1
–10.9
–15.9
4.9
354.79
3.22
9.12
16.56
2.57
—
.496
–.1340
–.1000
.177
.000
.000
.237
.339
.061
Preferred hand/light pen
F(4, 67) = 10.6
R2 = .389
(Constant)
Age
Cube comparison
Paper folding
Dexterity
1746.77
8.16
–8.88
–19.94
3.77
294.82
2.68
7.58
13.76
2.14
—
.355
–.1440
–.1650
.181
.000
.003
.245
.152
.082
Nonpreferred hand/mouse
F(4, 67) = 18.4
R2 = .524
(Constant)
Age
Cube comparison
Paper folding
Dexterity
1755.75
17.23
–13.88
–12.52
9.21
415.09
3.77
10.67
19.38
3.01
—
.470
–.1410
–.0650
.277
.000
.000
.198
.520
.003
Nonpreferred hand/light pen
F(4, 67) = 10.1
R2 = .376
(Constant)
Age
Cube comparison
Paper folding
Dexterity
945.09
9.43
–17.95
6.12
13.94
487.52
4.43
12.53
22.76
3.53
—
.251
–.1780
.031
.408
.057
.037
.157
.789
.000
Dependent Variable
Predictors
Preferred hand/mouse
F(4, 67) = 16.3
R2 = .494
for older adults’ difficulty with using a mouse
efficiently.
Ease of Use and Acceptability Ratings
Ease of use and acceptability judgments made
on a 0–10 rating scale were essentially equivalent, with the two variables (aggregated across
conditions) correlated at r = .94. ANOVA was
used to evaluate the aggregated (summed) mean
rating. The aggregate produced significant main
effects of hand and block. The main effect of
hand, F(1, 69) = 98.08, MSE = 15.37, p < .0001,
indicated that participants considered the preferred hand to be easier and more acceptable
to use than the nonpreferred hand. The main
effect of block, F(4, 276) = 8.55, MSE = 0.56,
p < .0001, indicated that ratings were more
favorable with practice over blocks, such that
the combined ratings were lowest following
Block 1 and highest following Block 5.
Significant interactions were apparent for
both Hand × Device and Hand × Age. The significant interaction of Hand × Device, F(1, 69) =
9.32, MSE = 67.2, p < .003, showed that these
experienced mouse users rated the mouse as
easier to use in their preferred hand; however,
as seen in Figure 4A, they rated the light pen as
easier to use in their nonpreferred hand. Be-
cause the light pen (a direct pointing device)
does not require the user to deal with translation
and mapping operations, it appears to be easier
to use in a novel situation, such as using the
nonpreferred hand for a point-and-click task.
The Hand × Age interaction, F(2, 69) = 3.37,
MSE = 9.58, p < .04, shown in Figure 4B, indicated that all three groups rated the preferred
hand similarly; however, both the younger and
middle-aged groups rated the nonpreferred hand
more highly than did the older group. This result
is consistent with the earlier results on menu
performance and on general lateralization showing that older adults are less effective with their
nondominant hand.
When a correlation is computed within subjects by using 20 pairs of scores (for the 5 blocks ×
2 devices × 2 hands), the mean correlation with
ease of use in the sample is r = –.43, and an
ANOVA shows that the mean correlation is significantly different from zero, F(1, 69) = 91,
MSE = 0.146, p < .01, and that there are no differences among age groups, F(2, 69) = 0.13. A
similar result is evident for the mean correlation
between acceptability and median response
time, with the sample correlation being r = –.30
and ANOVA showing this to be significantly different from zero, F(1, 69) = 34.8, MSE = 0.189,
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
LIGHT PEN VERSUS MOUSE FOR MENUS
381
A
B
Figure 4. A: Device × Hand interaction for mean aggregated ease of use and acceptability ratings. B: Age × Hand
interaction for mean aggregated ease of use and acceptability ratings. Standard errors are shown for each bar.
p < .01, and again, no age group differences
are observed, F(2, 69) = 0.90. The moderate
correlation between performance and ease of use
(or acceptability) implies that participants may
have been monitoring their own performance
and using it in part to assign usability ratings.
NASA-TLX Workload Analysis
Following each condition, participants rated
the amount of perceived workload in each of the
four Hand × Device conditions using the NASA-
TLX Workload Rating Scale. ANOVA revealed
significant main effects of age, F(2, 69) = 4.664,
MSE = 1116.24, p < .01; hand, F(1, 69) = 17.88,
MSE = 118.47, p < .0001; and device, F(1, 69) =
4.493, MSE = 177.364, p < .04. The older group
perceived the task as having a greater workload
(M = 60) than did the other two groups, which
were not significantly different from each other as
tested by LSD contrasts (M = 45 for the middleaged and M = 49 for the young participants). Not
surprisingly, the nonpreferred hand was rated as
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
382
Fall 2004 – Human Factors
creating a greater workload (M = 54) than the
preferred hand (M = 48). Finally, the mouse was
perceived as creating less workload (M = 49)
than the light pen (M = 53). There was also a
significant interaction of Hand × Device, F(1,
69) = 6.361, MSE = 84.91, p < .01. The mouse
was rated as creating the least amount of workload in the preferred hand (M = 45), and all
other conditions were not significantly different
(light pen/preferred hand M = 51; light pen/nonpreferred hand M = 54; mouse/nonpreferred
hand M = 53).
Benefit-Cost Analysis
At the time of this writing (the year 2003),
a mouse cost about $10 U.S. A light pen (or
touch screen-equipped monitor) cost about
$500 U.S. Although there were significant differences in performance favoring the light pen,
particularly for older adults, it is useful to try
to estimate the practical impact. That is, can we
quantify the Gray and Boehm-Davis (2000) view
that “milliseconds matter”? We attempted to
answer the question, How long would it take a
firm to recover the initial cost difference between the two devices for young, middle-aged,
and older adults?
As noted earlier, the practice block effect
lines for devices became parallel for all age
groups by the second trial block. Therefore the
mean performance time differences for the devices for the different age groups can be used
to estimate the efficiency of a device. We needed estimates of the average industrial wage and
of the number of mouse clicks that users perform in a day in order to estimate a payback
period. The average hourly private earnings in
the United States in February 2003 was approximately $15 U.S. The sole study we were able
to locate concerning mouse use was that by
Liu, Conlon, Lee, Wang, and Chu (2001). They
estimated a mean of 1045 mouse clicks per
day with a standard deviation of 940 for male
and female engineers, suggesting a very skewed
distribution. That sample is not likely to be representative of general computer users and may
not be particularly representative of older adult
samples, in comparison with the group we
tested. However, given the very broad range of
mouse use implied by those data, we may adequately bound more typical populations. More
research is needed on this topic.
As shown in Table 2, using that mean and
standard deviation, payback comes within a
broad range. Assuming the mean number of
mouse clicks, the cost differential for a light pen
or touch screen over a mouse would be eliminated after about 23 months for young adults,
18 months for middle-aged adults, and10 months
for older adults. However, the high standard
deviation for the estimate of mouse clicks indicates that in the worst case, 105 clicks/day, it
might take 19 years for young adults, 15 years
for middle-aged adults, and 8.5 years for older
adults to recover the cost of using a light pen.
In the latter case, it would not be cost effective
to buy the more expensive input device. In the
two more favorable conditions (assuming 1985
and 1045 clicks/day) it would be appropriate.
A number of factors could affect the sensitivity
of this analysis. First, we lack solid information
on the distribution of the number of clicks per
day for a broad range of computer tasks (and
what type of click: single vs. double). Second,
older workers are generally paid more than
younger workers (hence shortening their recovery period). Finally, we do not know the likelihood of substitution of workers for equipment
TABLE 2: Time to Recover the Difference in Cost Between a Light Pen and a Mouse
Light pen target acquisition time (ms)
Mouse target acquisition time (ms)
Gain/target for the light pen, given task had 2 targets (s)
Gain/target using the average industrial wage ($U.S.)
Targets required to equal $500
Days to equal $500 if 1045 clicks/day
Days to equal $500 if 105 clicks/day
Days to equal $500 if 1985 clicks/day
Young
Middle-Aged
Older
1852
2184
0.166
0.0007
722 892
692
6885
364
2176
2601
0.2125
0.0009
564 706
540
5378
284
2534
3268
0.367
0.0015
326 975
313
3114
165
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
LIGHT PEN VERSUS MOUSE FOR MENUS
in larger firms (possibly lengthening the payback period, as compared with an assumption
of a constant-sized workforce).
DISCUSSION
Practical Implications
The observed main effects together with the
three interactions for menu task performance
can be translated into practical guidelines.
1. Older adults particularly would benefit
from using a direct positioning device such as a
light pen on computer tasks that require pointing as the main operation. Even experienced
younger mouse users were faster when using
the light pen.
2. The Age × Device interaction suggests
that older adults using a mouse may experience translation problems from gain, which are
not present with the light pen. Evidence for
this interpretation is indirect. The light pen,
which exhibits no gain, minimized age differences in performance. We rule out general spatial ability as a contributing factor because
spatial ability (as measured by paper folding
and cube comparison) does not seem to mediate
age-related performance differences. However,
manual dexterity, independent of age, was a
significant individual difference predictor of
menu task performance. To the extent that older
adults are less able to control fine movement, as
others have shown in the analysis of micromovements involved in general movement control, such difficulties may be exacerbated when
learning to control a device with gain. However,
more direct tests of the gain hypothesis are
needed. Nonetheless, our results support the
guideline that when designing for older user
populations, one should be cautious about using
devices with gain.
3. The Age × Hand interaction indicates that
older adults, who may need to switch to their
nonpreferred hand for controlling an input device (e.g., because of injury or arthritis), will initially have much more difficulty adjusting than
will younger adults.
4. The Age × Trial Blocks interaction indicates that older adults have the most to gain
from practice with an input device. Hence,
older adults should be encouraged (e.g., via in-
383
structions accompanying the product) to persist
with practice to ensure optimal performance.
Caveats. It may also be the case that other
input devices would yield a differential benefit
for older adults. As has long been recognized
in the human-computer interaction literature,
the relative advantage of a device depends on the
type of task being performed (e.g., Card, Moran,
& Newell, 1983). For instance, when young
adults performed a tracking task, a trackball
led to lower tracking error and lower workload
ratings than a mouse did (Hancock, 1996). For
some types of interface controls (sliders, up/
down buttons, scrolling), a rotary encoder device was found to be superior to a touch screen
(Rogers, Fisk, McLaughlin, & Pak, in press),
particularly for older adults. For drop-down
list boxes, the touch screen generally yielded
faster performance. For data entry tasks, automated speech recognition is superior to keyboard and mouse only when the user types
fewer than 45 wpm with text entry, and it is not
superior for numerical data entry (Mitchard &
Winkles, 2002).
Thus an important caveat for these generalizations is that the task we investigated was a
pure pointing task using drop-down menus.
Many computer tasks involve interleaved use
of two input devices: a pointing device and a
keyboard. Interleaving creates problems for
skilled touch typists because one hand must
leave the home row key position to reach for a
pointing device and then return to that position for further typing: the homing problem.
We might expect a different pattern of results
with mixed tasks because a light pen requires
more extensive movements to access the screen
and to move the cursor position than does a
mouse. Further, we did not investigate clickand-drag activities (used to move objects to
new positions on the screen) with the pointing
devices. It may be more efficient and less effortful to click and drag with a mouse than with a
light pen.
ACKNOWLEDGMENTS
This research was supported in part by the
National Institute on Aging via Grant NIA1 PO1
AG17211-04, Project CREATE. We thank the
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
384
Fall 2004 – Human Factors
many departmental independent study undergraduates for helping with the project.
REFERENCES
Benson, V., & Marano, M. A. (1998). Current estimates from the
National Health Interview Survey, 1995. In Vital and health
statistics, Series 10: Data from the National Health Survey
(No. 199, pp. 79–80). Washington, DC: National Center for
Health Statistics. Retrieved January 21, 2003, from http://
www.cdc.gov/nchs/data/series/sr_10/sr10_199acc.pdf
Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of
human-computer interaction. Hillsdale, NJ: Erlbaum.
Cerella, J., Poon, L. W., & Williams, D. M. (1980). Age and the
complexity hypothesis. In L. W. Poon (Ed.), Aging in the 1980s:
Psychological issues (pp. 332–340). Washington, DC: American
Psychological Association.
Chaparro, A., Bohan, M., Fernandez, J. E., Choi, S. D., & Kattel, B.
(1999). The impact of age on computer input device use: Psychophysical and physiological measures. International Journal
of Industrial Ergonomics, 24, 503–513.
Charness, N. (2003). Commentary: Access, motivation, ability, design, and training: Necessary conditions for older adult success
with technology. In N. Charness & K. W. Schaie (Eds.) Impact
of technology on successful aging (pp. 28–41). New York:
Springer.
Charness, N., & Campbell, J. I. D. (1988). Acquiring skill at mental
calculation in adulthood: A task decomposition. Journal of
Experimental Psychology: General, 117, 115–129.
Charness, N., Kelley, C. L., Bosman, E. A., & Mottram, M. (2001).
Word processing training and retraining: Effects of adult age, experience, and interface. Psychology and Aging, 16, 110–127.
Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S., Rogers,
W. A., et al. (2004). Factors predicting the use of technology:
Findings from the Center for Research and Education on Aging
and Technology Enhancement (CREATE). Manuscript submitted for publication.
Francis, K. L., & Spirduso, W. W. (2000). Age differences in the
expression of manual asymmetry. Experimental Aging Research,
26, 169–180.
Gilbert, A. N., & Wysocki, C. J. (1992). Hand preference and age
in the United States. Neuropsychologia, 30, 601–608.
Gray, W. D., & Boehm-Davis, D. A. (2000). Milliseconds matter:
An introduction to microstrategies and to their use in describing
and predicting interactive behavior. Journal of Experimental
Psychology: Applied, 6, 322–335.
Greenstein, J. L. (1997). Pointing devices. In M. V. Helander, T. K.
Landauer, & P. V. Prabhu (Eds.), Handbook of human-computer
interaction (2nd ed., pp. 1317–1348). Amsterdam: Elsevier.
Hancock, P. A. (1996). Effects of control order, augmented feedback, input device and practice on tracking performance and
perceived workload. Ergonomics, 39, 1146–1162.
Hart, S. G., & Staveland, L. E. (1988). Development of NASATLX (Task Load Index): Results of experimental and theoretical
research. In P. A. Hancock & N. Meshkati (Eds.) Human mental
workload (pp. 139–183). Amsterdam: North Holland.
Jagacinski, R. J., Liao, M.-J., & Fayyad, E. A. (1995). Generalized
slowing in sinusoidal tracking by older adults. Psychology and
Aging, 10, 8–19.
Kabbash, P., MacKenzie, I. S., & Buxton, W. (1993). Human performance using computer input devices in the preferred and
non-preferred hands. In Proceedings of the INTERCHI ’93
Conference on Human Factors in Computing Systems (pp.
474–481). New York: Association for Computing Machinery.
Liao, M., Jagacinski, R. J., & Greenberg, N. (1997). Quantifying
the performance limitations of older and younger adults in a
target acquisition task. Journal of Experimental Psychology:
Human Perception and Performance, 23, 1644–1664.
Liu, W., Conlon, C., Lee, S., Wang, J., & Chu, M. (2001, June). VDT
keyboard and mouse usage by male and female engineers.
Presented at the 2001 American Industrial Hygiene Conference
and Exposition, New Orleans, LA.
Mitchard, H., & Winkles, J. (2002). Experimental comparisons of
data entry by automated speech recognition, keyboard, and
mouse. Human Factors, 44, 198–209.
Newburger, E. (2001). Home computers and Internet use in the
United States: August 2000. Washington, DC: U.S. Census
Bureau. Retrieved January 22, 2003, from http://www.census.
gov/prod/2001pubs/p23-207.pdf
Oldfield, R. C. (1971). The assessment and analysis of handedness:
The Edinburgh inventory. Neuropsychologia, 9, 97–113.
Rainie, L., & Packel, D. (2001). More online, doing more: 16 million
newcomers gain Internet access in the last half of 2000 as
women, minorities, and families with modest incomes continue
to surge online. Washington, DC: Pew Internet & American
Life Project. Retrieved September 25, 2001, from http://www.
pewinternet.org/pdfs/PIP_Changing_Population.pdf
Rogers, W. A., Fisk, A. D., McLaughlin, A. C., & Pak, R. (in press).
Touch a screen or turn a knob: Choosing the best device for
the job. Human Factors.
Salthouse, T. A. (1992). What do adult age differences in the digit
symbol substitution test reflect? Journal of Gerontology:
Psychological Sciences, 47, P121–128.
Salthouse, T. A. (1996). The processing-speed theory of adult age
differences in cognition. Psychological Review, 103, 403–428.
Salthouse, T. A., Mitchell, D. R. D., & Palmon, R. (1989). Memory
and age differences in spatial manipulation ability. Psychology
and Aging, 4, 480–486.
Smith, M. W., Sharit, J., & Czaja, S. J. (1999). Aging, motor control,
and the performance of computer mouse tasks. Human Factors,
41, 389–396.
Walker, N., Millians, J., & Worden, A. (1996). Mouse accelerations
and performance of older computer users. In Proceedings of
the Human Factors and Ergonomics Society 40th Annual
Meeting (pp. 151–154). Santa Monica, CA: Human Factors
and Ergonomics Society.
Walker, N., Philbin, D. A., & Fisk, A. D. (1997). Age-related differences in movement control: Adjusting submovement structure
to optimize performance. Journal of Gerontology: Psychological
Sciences, 52B, P40–P52.
Ward, J. P., Milliken, G. W., Dodson, D. L., Stafford, D., & Wallace,
M. K. (1990). Handedness as a function of sex and age in a
large population of lemur. Journal of Comparative Psychology,
104, 167–173.
Neil Charness is professor of psychology and an associate of the Pepper Institute on Aging and Public
Policy at Florida State University. He obtained his
Ph.D. in psychology from Carnegie Mellon University
in 1974.
Patricia Holley is a postdoctoral fellow at Florida
State University. She received her Ph.D. in psychology
from the University of South Florida in 1999.
Jeffrey Feddon is a software support engineer at Aculab USA Inc., Panama City, Florida. He received his
M.A. in psychology at the University of West Florida
in 1996.
Tiffany Jastrzembski is a doctoral student in the Psychology Department at Florida State University. She
received her M.A. in psychology in 2003.
Date received: April 30, 2003
Date accepted: March 8, 2004
Downloaded from hfs.sagepub.com at UNIV OF CALIFORNIA SANTA CRUZ on November 26, 2014
Download