Research Note 85-39

A CORRELATIONAL ANALYSIS OF THE PRELIMINARY MACS M16AI

EVALUATION: A REDIRECTION FOR MACS TRAINING SOFTWARE

James E. Schroeder

for

ARI Field Unit at Fort Benning, Georgia

Seward Smith, Chief

TRAINING RESEARCH LABORATORY

Harold F. O'Neil, Jr., Director

Reproduced From

Best Available Copy

CT 3 0 V

-, U.S. Army

AResearch Institute (or the Behavioral and Social Sciences

* September l')5

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1. REPORT N4UMAOR 2. GOVT ACCESSION NO. 1. RECIPIENT'S CATA .0G NUMBER

Research N4ote 85-89 ~,~ /6 qeq

4. TITLE (si Suote S. TfPE OF REPORT A PERIOD COVERED

A CORRELATIONAL ANALYSIS OF THE PRELIMINARY

MACS M16AI EVALUATION: A REDIRECTION FOR MACSJa. 98-1ch95

TRAINING SOFTWARE 6. PERFORMING 01G. REPORT NUMBER

7. AuTmOft(.) S. C01NTRACT OR GRANT NUMBER(s)

l;ames E. Schroeder

3. PERVOPwING ORGANIZATION NAME AND ADDRESS I0. PROGRAM ELEMENT. PROjECT, TASK AREA & U3RK UNIT NIUBERS

ARI Field Unit at Fort B 2nn ing, Georgiaj

P.O. Box 2086

Fort Ben'iing, GA 31905-0686 2Q263743A794

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* I9. SUPPLEMENTARY NOTES

3 KE WORDS fCo-gI-* on r.so *.e ,do it nocessay and td*,,(Ify Sy block tv bw

XI6A1 rifle trainer

Marksmanship

simulator

20. ASS"ACT :Cogtw ou ,m " If **.* I..u b, ftcck -bin ~ ~ p r~i2nr vvrt l' u.it ion ~ X~ u1 t i pl'r -~ Arcade Cornh~it

mula;tor .~..p, itive t ran zf er was td f or th - 'f 4renade . aunchvtr

*and tlit 17-'A2 I i~ht antit-nk weapon. iioweovor . the're vx ,no :ippreciable

positive t ran s v r t o a iv-f ire takfrchet ec n'onc-urrent

train in,; on the MI 6A1 r i, " .M~: Ther. purpose 01 the present et fort

was' to provide *-I ror, no] e-ulkr- anail v~i ; of t! he d at ., - liec ted in that

e\'aluation in order to idontifv. promi-;i-n new directionz for development of the

-tAtS -,AOther pur;ov wse ds to ',t imat e the rlih itvand externa.

DO~ 103 CEOfT 0Ogf I NOV SIS ODOLETE

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SECURITY CLASSFICATION O T14S PAOG(Wqen Dot* itnt.e"N

validity of the current MACS training programs. In general, the correlational

approach indicated low MACS reliability (correlations of MACS scores with MACS

scores), low MACS external validity (correlations of MACS performance with

*live-fire performance), and generally acceptable live-fire reliability (correlations of live-fire measures with live-fire measures). The most significant

finding was that MALL measures of variability of light-pen readings ( a measure

of how steady the weapon was held) showed highly significant reliability and

generally acceptable external validity. This was in sharp contrast to the MACS

point-of-aim scores. Two hypotheses are proposed for future MACS development:

that the MACS software be changed in a way that slight movements of the weapon

during trigger manipulation be included in the determination of the MACS

point-of-aim scores; and, that measures of variability be exploited in the MACS

'oftware in order to provide prediction of marksmanship performance,. feedback

for training marksmanship, and diagnosis and remediation of marksmanship

problems.

Unclassif ied

SECURITY -L A tIv C A ' r

T"IS P AGE'W~e" Ver. Fn o.,dw
TABLE OF CONTENTS

Introduction ........................... ...... 1

Results ............................. ... .4

Discussion................................................................ 9

References ............................................................... 13
I

t.

-INTRODUCTION

The Multipurpose Arcade Combat Simulator (MACS) is a low cost, part-task .. simulator/trainer.being developed by the US Army Research Institute's Fort

* Benning Field Unit.'-Using off-the-shelf components, MACS provides the shooter

with valuable training feedback about location of bullet strike at various

ranges, effects of wind and gravity, etc. The key component in MACS is a specially designed light pen which can be removed from one weapon system (e.g.,

* the Ml6Al rifle) and attached to another weapon system (e.g.,,the M203 grenade

* launcher or M72A2 light antitank weapon). Hence, MACS is a trainer which can

train a variety of tasks on a variety of weapon systems.(see Figure 1). For a

more detailed description of MACS, see Schroeder (1983a).>

,Recently, preliminary experimental evaluations of the MACS MI6Al rifle,

M203 grenade launcher, and M72A2 light antitank weapon wera conducted.(see

Perkins, Selby, Broom, & Osborne, 1984)., The results of .heseeva-luaions -. showed positive, transfer to live fire for the grenade launcher and LAW, but

minimal transfer for the MACS t ±ie application. Although there were some problems enco,:ntered in the field ealuation (see Perkins et al. for details ,

the lack of significant posit .a transfer was surprising given the extensive • concurrent training provided on te ft, Perkins et al. report concentrated on " the experomenta're -. Ho . ."there was little correlational analysli

discussed and such analyses couid ,rrove useful in discovering new directions,

corrections, and software improv:'irits for HACS. The purpose of this report is

to provide additional correlatio,ial analyses in an attempt to identify promtsing new avenues of research and developm,'nt for the HACS system.

PROCEDURE

In general, the experimental approach of Perkins et al. was to provide , concurrent training on MACS for one platoon (experimental group) and no MACS . concurrent training for a second platoon (control group). The subsequent - analyses focused on comparisons between live-fire scores for the two groups.

The correlational analyses in the present paper concentrate on the experimental

*group only because a vast amount of MACS performance data was collected on those

subjects. Figure 2 (from Perkins et al.) shows the various Basic Rifle

Marksmanship (BRM) periods of instruction and corresponding MACS training

programs which were presented to the experimental subjects during concurrent

* training for those live-fire periods (see Perkins et al. for descriptions of the

* training in each of the BRM and :ACS programs). 0

The following results and discussion sections will cover three different

* "topics: the correlations of MACS measures with otner MACS measures, The

correlations o( live-fire measures with other, live-fire measures, and the -: correlations of MACS measures with live-fire measures. By analyzing the

correlations of MACS measures with other RACS measures, the reliability of MACS

3can be assessed. Similarly, by addressing the correlations of live-fire measures with other live-fir, measures, the correlational "ceiling" can be,

identified (i.e., how can a simulator predict live fire better than an identical

m"1

U_ /
Microcomputer,

Disk Drives,

and Monitor

Dummy Weapon with -,: i, ., ..

Light Pen'

FIGURE 1. Figure i shows the hardware configuration for the MACS

simulator/trainer for the MI6A1 Rifle.

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live-fire task). By analyzing the correlations of MACS with live fire, the

strergths and weaknesses of MACS can be identified. More specifically, in the

development of a simulator/trainer, the first goal should be to identify

simulator measures which correlate with the measures that the simulator is

trying to train (i.e., live-fire marksmanship scores in this case). The

correlational approach provided in the present paper provides a develcpment aid

by identifying the nACS variables with the highest potential. Once promising

simulator measures/programs are identified, then efforts can be concentrated to

improve those measures whtle less promising measures/programs can be discarded.

RESULTS

The Correlation of MACS Measures with MACS Measures

For the purpose of this analysis, nine independent measures of MACS

performance were included from all stages of IACS training (see Figure 2). A

list of these nine measures along with a brief explanation of each is presented

in Table 1. Table 2 shows the resulting correlation matrix. There are three

general classes of measurement shown in Table 2: the amount of time it took

soldiers to fire at targets in the early pretest and posttest (Pretest Latency

and Posttest Latency); the variability of weapon point-of-aim prior to firing

(Pretest Steady Position and Posttest Steady Position); and accuracy scores

(Down-Range Feedback, Field Fire I, Field Fire II, and Record Fire). The

latency measures did not correlate significantly with each other indicating very

low reliability. In addition, the latency measures did not correlate

qignificantly with the other measures with the exception of a significant

correlation between Pretest Latency with Field Fire II scores (T <.05). There

was high test-retest reliability for the t'wo variability scores (p <.001), but

low correlations between those steady position scores and other MACS measures

(except for the significant correlation between Posttest Steady Position and.

Field Fire II scores (p <.05). Finally, there were no significant correlations

among the virious accuracy measures with the exception of Field Fire II and MACS

Record Fire (a <.01).

In summary, the correlatioual analysis indicated a general lack of

reliability among the three general classes of MACS measures. In addition, only

the variability measures (Steady.Postition scores) showed significant

within-class reliability.

The Correlations of Live-Fire Measures with Live-Fire Measures

Table 3 provides a list of the various live-fire measurements included in

the current analysis. One measure was included from each period of instruction

from Period 4 to Period 10 and two measures were included for Period 11 (Combat

Fire and Record Firc). All measures used were obtained directly from standard

company performance records except Shot-Group Size from Period 6. Shot-Group

Size from Period 6 was calculated from actual target measurements and represents

the mean radial distance from the center of the shot group to the various shot

locations. Table 4 shows the correlation matrix within the various live-fire

measures. The Record Fire score correlated significantly with all other

measures except Number of Round3 to Zero in Period 4. The lack of correlation

.4
Table 1

MACS Variables and Descriptions

Variable Description

Pretest Latency Median time to fire the weapon in MACS Aiming

pretest.

Posttest Latency Median time to fire the weapon in MACS Aiming

posttest.

Pretest Steady Position Mean sum of standard deviations for X and Y

for 5 light-pen readings before 6 shots

in MACS Aiming pretest.

Posttest Steady Position Mean sum of standard deviations for X and Y

for 5 light-pen readings before 6 shots

in MACS Aiming posttest.

Down-Range Feedback Mean median score on 5 exposures to MACS Down-Range

Feedback program.

Field Fire i Mean mediAn score on 2 exposures to MACS Field

Fire I program.

Field Fire II Mean median score on 2 exposures rt MACS Field

Fire II program.,

MACS Record Fire Mean median score on 2 exposures to MACS Record

Fire program.

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Table 3

Live-Fire Variables and Descriptions

Variable Description

Rounds to Zero, Period 4 Number of rounds to zero in Period 4.

Hits in Period 5 Number of hits on scaled silhouette targets

in Period 5.

Shot-Group Size, Period 6 Mean radial error from shot-group center

on the 75 m target in Period 6.

Hits in Perind 7 Number of hits on field-fire killable

targets in Period 7.

Hits in Period 8 Number of hits on field-fire kiliable

targets in Period 8.

Hits in Period 9 Number of hits on timel-fire scaled

silhouette targets in Period 9.

Practice Rerord Fire Number of hits in Practice Record Fire

(Period 10).

Combat Fire Number of hits in Combat Fire (Period lla).

Record Fire Number of hits in Qualification Record Fire

(Period lib).

7

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between Period 4 scores and Record Fire scores is not surprising given the

amount of time that elapsed between the two measurements and the large number of

uncontrollable variables which can influence the Period 4 measure (e.g., lack of

familiarity with the weapon, bad initial sight settings which-throw rounds

completely off the paper, etc.). According to the present results, the best

single predictor of Record Fire score was Practice Record Fire followed by

Shot-Group Size in Period 6, Combat Fire, and then a cluster of measures which

correlated about the same (Hits in Period 5, Hits in Period 9, Hits in Period 7,

and Hits in Period 8). In terms of magnitude of correlation coefficient, the

best single early predictor of Record Fire was Shot-Group Size in Period 6.

However, since Hits in Period 5 can be obtained a day earlier and with less

difficulty, it may be the most functional early predictor of Record Fire scores.

In conclusion, the inter-correlations of various live-fire scores was

generally high. Although there were not consistently significant correlations

* among the various periods of instruction leading to record fire, there were

statistically significant correlations between alllive-fire periods and record

fire except for the number of rounds to zero in period 4.

The Correlations of MACS measures with Live-Fire Measures

Table 5 shows the correlations between the various MACS m-asures and the

various'live-fire measures. There was a general lack of correlation between tne

various MACS scores and live-fire scores. With the exception of a significant

correlation between the MACS Field Fire I scores and the Shot-Group Size in

Period 6, the only significant correlations involved the MACS measures of

variability (Posttest Steady Position with Rounds to Zero in Period 4, Pretest

Steady Position with Hits in Period 7, Posttest Steady Position with Hits in.

Period 7, Pretest Steady Position with Practice Record Fire, Post test Steady

Position with Practice Record Fire, and Posttest Steady Position with Record

Fire).

In conclusion, the correlations among MACS scores and live-fire scores were

generally low. The exceptions were the two MACS measure of variability of

light-pen readings (measuring how steady the weapon was held just prior to

shooting). In those two measures (Pretest Steady Position and Posttest Steady

Position), 6 of 18 correlations were statistically significant.

DISCUSSION

In general, the UtACS system was found to be low in reliability (as measured

by correlations of MACS measures with other MACS measures) atu low in external

validity (as measured by correlations of Y.aCS measures with live-fire measures).

In comparison, there were generally acceptable correlations among the various

live-fire measures.

On the surface, these results seen to indicate that not only :an't MACS

train marksmanship skills (i.e., Perkins et al.) but in addition, MACS can't

predict live-fire performance. However, there was one potentially interesting

.trend in the present data. More specifically, the high reliability shown in the

variability measures (as opposed to point-of-aim measures) and the external

validity shown in the significant correlations of those variability measures

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with 5 of the live-fire measures indicate a promising new measure. is it

possible that point-of-aim measures of accuracy are not the best predictors of

marksmanship in the .4ACS system? Accuracy measures were used in the original

evaluation (Perkins et &l.) because they represented the most intuitive and

traditional measure of performance in a marksmanship task. However, perhaps

electronic simulators like MACS provide addftional and more powerful measures of

marksmanship ability which are not utilized b.cause they have not been

traditionally available.

There is another interesting feature in the present data. Although this

study was the first time that HACS has been correlated with live-fire, in an

earlier study (Schroeder, 1983b) 11ACS point-of-aim scores were found to

significantly correlate with scores on another marksmanship simulator - the

Moving Target Rifle Marksmanship Trainer (MTRMT). Other research has shown that

MTR T scores correlate significantly with live fire (Schendel and Heller, 1982;

and Schendel, Heller, Finley, and Hawley, 1983). This trend of resilts is

incongruous uith the generally low correlations of MACS point-of-aim scores with

live-fire scores found in the present study. This apparant inconsistency caused

the present author to review the MACS development effort to determine if any

hardware and/or software changes could have resulted in the discrepancy between

early MACS results and the present results. Hardware changes were unlikely to

.ave caused the discrepancy since the reliability and accurracy of the light pen

had actually been improved over the development effort. Iowever, there, was a

seemingly insignificant software change that may have resulted in lower

correlations in the present study. The change involves the MACS software and

how it determines point-of-aim. In the original MACS software that the present

author wrote, 5 light-pen readings were collected. afte; the trigger-switch was

closed. Since the software can provide readings at the rate of about 20 per

sec, this represented a measurement window of about .25 sec. After the 5

light-pen readings were taken, both -X" readings (horizontal) and "Y" readings

(vertical) were sorted and the medians were determined. This approach was taken

to stabilize the light-pen readings and eliminate any potential deviant scores.

It was this procedure that was used in the study which found significant

correlations with the MTRMT. Subsequently, software contractors were brought in

to make improvements in the software. The present author directed the software

developers to change the above procedure in order to increase the speed of

operation of the MACS system. In the new procedure, the light pen continuously

. took readings and updated a fixed stack. When the trigger-svitch was ciosed,

the last 5 X and Y readings before trigger-pull were sorted and medians

. calculated., The two procedures were functionally the same, except for the time

window. Could a difference of .25 sec in the time window have resulted in the

apparently large difference in results? Perhaps the answer is "yes" if small

changes in the position of the weapon due to trigger manipulation affect both

point-of-aim scores on a simulator and in live fire. More specifically, one of

the most important principles in marksmanship is that the trigger should be

squeezed because rapid trigger manipulation typically results in movement of the

weapon and error in the subsequent shot. Using the 5 readings before trigger

manipulation is less likely to pick up distortions in the point-of-aim than

using the 5 readings after trigger manipulation for those poor shooters who

quickJy -jerk" the trigger. This is especially true given the median routine in

the software that presumably sorts out deviant scores due to the light pen, but

in this case, may be sorting out deviant scores that are due to shooting errors.

Because this process would make poor shooters appear better than they really

are, the range of the MACS scores would be restricted and the magnitudes of the

*. correlations would be limited. In addition, this may explain why such a high

percentage of the experimental MACS subjects in Perkins et al. scored very high

!,• 11
on the MACS system (I.e., if holding the weapon steady during trigger

maniptulation is akey com. onent to marksmanship and the second generation HACS

soitware was not looking at that compone-t, then perhaps MACS was measuring the

ability to achieve a proper initial aiming point - a fairly easy task).

The two hypotheses discussed above suggest an Interesting redirection for

software development of the MACS system. First, studies need ta be done to

determine the optimum time window and statistical method for determining

point-of-aim. This should be done using correlation with live fire as a

criterion. Second, the high test-retest reliability shown in the variability

measure reflecting steadiness in the weapon should be investigated as a possible

'meaningful measure for both the prediction of marksmanship ability and as a

feedback variable for training marksmanship. In general, what is suggested by

the present author is a correlational time-series type analysis that empirically

determines the best window(s) and statistical method(s) for: predicting

marksmanship, diagnosing and remediating marksmanship problems, and providing

feedback for marksmanship training. The current expertise in marksmanship

training should help guide this development effort. For example, in the Army's

Basic Rifle Marksmanship Program of Instruction, the four fundamentals are:

steady position, trigger squeeze, aiming,. and breath control. If these are

indeed fundamentals of marksmanship, then why not design a simulator/trainer

that measures thise f o,. components (or as many as feasible). The new

*redirection in MACS software development will be to establish time windows and

statistical methods which will hopefully measure at least three of these four

areas: steady position, trigger squeeze, and aiming. Measures of variance for

selected time windoits should provide diagnostic, remedial, predictive, and

training information for the steady position and trigger squeeze variables.

Measures of central tendency for a selected time window should provide

diagnostic, remedial, predictive, and training information for the aiming

variable.

In the near future, research will be conducted which will identify the

optimal windows and statistical measures. The next generation of MACS software

will incorporate this informa on. Different programs will be written for the

purpose of prediction of marks.anship ability, diagnosis and remediation of

marksmanship problems, and for the general training of marksmanship skills on

the MI6Al rifle and other HACS weapons.

.o2
References

Perkins, .. N., Selby, R. D., Broom, J. M., & Osborne, A. S., (1985).

Preliminary evaluation of the Multipurpose Arcade Combat' Simulator (MACS)

for the M16, M203, and M72A2 (ARI Research Note 85-60). Alexandria,

Virginia: U.S. Army Research Institute for the Behavioral and Social

Sciences.

Schendel, J. D., & Heller, F. II. (1982). Use of Weaponeer marksmanship trainer

in predicting M16A1 qualification. Proceedings of the Eighth Symposium

Psychology in the Department of Defense (pp. 106-110).

Schendel, J. D., & Heller, F. H., Finley, D. L., & Hawley, J. K. (1983). Use of

Weaponeer marksmanship trainer in predicting M16A1 rifle qualification

performarce (ARI Research Report 1370). Alexandria, Virginia: U.S. Army

Research Institute for the Behavioral and Social Sciences.

Schroeder, J. E. (1983a). A Multipurpose Arcade Combat Simulator (MACS). ARI

Technical Report 629. Alexandria, Virginia: U.S. Army Research Institute

for the Behavioral and Social Sciences.

Schroeder, J. E. (1983b). The relationship between the Moving Target Rifle

Marksmanship Trainer (MTRMT) and the Multipurpose Arcade Combat Simulator

(MACS). ARI Research Note. Alexandria, Virginia: U.S. Army Research

Institute for the Behavioral and Social Sciences.

13