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several example input and output files illustrating typical formats. Availability. Toobtain a complete copy of the CSPRED program (i.e., executable file, source code, program documentation, and example files), send a 3.5-in. IBM diskette and self-addressed and post-paid disk mailer to the first author. Alternatively,these materials can be obtained from the COMPSYCH archive. COMPSYCH can be reached on the Web at htp://www.plattsburgh.edu/compsych, and files can be retrieved from the COMPSYCH archive by anonymous ftp to gluon.hawk.plattsburgh.edu. REFERENCES BANZHAF, 1. E, III (1965). Weighted voting doesn't work: A mathematical analysis. Rutgers Law Review, 19, 317-343. BORLAND INTERNATIONAL, INC. (1992). Turbo pascal (Version 7.0) [Language Guide, User's Guide, Programmer's Reference, software]. Scotts Valley, CA: Author. CROTT, H. w., & ALBERS, W. (1981). The equal division kernel: An equity approach to coalition formation and payoff distribution in N-person games. European Journal ofSocial Psychology, 11, 285-305. DAVIS, M., & MASCHLER, M. (1965). The kernel of a cooperative game. Naval Research Logistics Quarterly, 12,223-259. KAHAN, J. P., & RAPOPORT, A. (1984). Theories ofcoalition formation. Hillsdale, NJ: Erlbaum. KOMORITA, S. S., & CHERTKOFF, 1. M. (1973). A bargaining theory of coalition formation. Psychological Review, 80,149-162. KOMORITA, S. S., HAMILTON, T. P., & KRAVITZ, D. A. (1984). Effects of alternatives in coalition bargaining. Journal ofExperimental Social Psychology, 20,116-136. KOMORITA, S. S., & TuMONIS, T. M. (1980). Extensions and tests of some descriptive theories of coalition formation. Journal ofPersonality & Social Psychology, 39, 256-268. KRAVITZ, D. A., & WALKER, J. A. (1989). COALPRED: A BASIC program for computing predictions of five coalition theories. Behavior Research Methods, Instruments, & Computers, 16,69-70. MYERSON, R. B. (1977). Graphs and cooperation in games. Mathematics ofOperations Research, 2, 225-229. ROTH, A. E. (1977). The Shapley value as a von Neumann-Morgenstern utility. Econometrica, 45, 657-664. SAKURAI, M. M., & BRENNAN, J. M. (1988). Computing the von Neumann-Morgenstern characteristic function v(S) for cooperative n-person transferable utility normal form games: LP and saddlepoint solutions. Behavior Research Methods, Instruments, & Computers, 20,367-371. SAKURAI, M. M., & BRENNAN, J. M. (1990). Computing the constrained game function CGF(S) for n-person cooperative transferable utility normal form games. Computers in Human Behavior, 6, 323-335. Note-The CSPRED program was developed in part under Grant SES9208525 from the National Science Foundation and Grant 950463 from the Graduate School of the University of Wisconsin, Madison. The authors express appreciation to David M. Wolfe and Wing Tung Au for their contributions toward the development of this software.
H. Andrew Michener and Daniel 1. Myers University of Wisconsin, Madison
(Manuscript received October 23, 1995; revision accepted for publication December 14, 1995.)
Generating Correlated Variables for Experimental Tasks Investigations of multiattribute decision choice (Hogarth, 1987; Pious, 1993; von Winterfeldt & Edwards, 1986) often require stimulus materials consisting of a number of cases about which participants are required to make holistic judgments on the basis of several variables. In practice, many judgment analysis studies employ randomly generated variables that are essentially mutually orthogonal. These data sets are easy to generate, and they simplify the linear modeling of judgments; but participants in such studies tend to notice unrealistic combinations of values that can occur when a particular pair of variables is uncorrelated. A solution to this problem is provided by CVER, an interactive computer program that generates data sets consisting of integer variables (cues) sampled from a multivariate normal population with user-specified correlations among the cues. CVER is written in IBM Personal Computer BASIC, which is compatible with Microsoft GW-BASIC, and uses a method presented by Johnson (1987). The user is asked to specify desired cue intercorrelations and, for each cue, a minimum, maximum, and increment between integer scale points. The user can save a set of specifications in a disk file for later use. If input from a disk file is selected, the user is given an opportunity to modify the specifications. The user is also prompted for other output file names and for a random number seed. During operation of the program, output provided for monitoring the characteristics of the cues generated includes cue means and standard deviations and correlations among the cues. At various points, the user can elect to continue, return to the beginning (to generate a new data set because, for example, the sample correlations differ too much from the desired correlations), or terminate the session. Other screen output includes various prompts for user input and advisory (progress reporting) messages. The program attempts to prevent inadvertent destruction of existing disk files due to name duplication. The following output may be saved in user-specified disk files: (I) a cases-by-cues matrix of initial cue values in which each column has been sampled from a population that is N(O,1), using the modified polar method of Marsaglia and Bray (1964); (2) a cases-by-cues matrix that has been transformed via the Cholesky factorization (Bock, 1975) with the desired intercorrelations as targets but retains the means and standard deviations of the previous matrix; and (3) a cases-by-cues matrix offinal cue values that have been rounded to integers in the desired range and with the desired increments between adjacent cue values. Correlations among the columns of matrix (2) will differ from the desired intercorrelations because of sampling error, and correlations among the columns of
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matrix (3) will further deviate from the desired intercorrelations because of the rounding to integers. The present version of CUER can generate up to 20 cues for up to 200 cases. System memory permitting, these limits can easily be changed. Availability. A copy ofCUER as stored by the BASIC interpreter, an ASCII listing of the program, and documentation may be obtained by sending a blank 3.5-in. disk to James H. Hogge, Box 512, Peabody College, Nashville, TN 37203. The same material may also be obtained by e-mail (
[email protected]). REFERENCES BOCK. R. D. (1975). Multivariate statistical methods in behavioral research. New York: McGraw-HilI. HOGARTH, R. (1987). Judgment and choice (2nd ed.). New York:Wiley. JOHNSON, M. E. (1987). Multivariate statistical simulation. New York: Wiley. MARSAGLlA, G., & BRAY, T. A. (1964). A convenient method for generating normal variables. SIAM Review, 6, 260-264. PLOUS, S. (1993). The psychology ofjudgment and decision making. New York: McGraw-HilI. VON WINTERFELDT, D., & EDWARDS, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.
James H. Hogge Peabody College of Vanderbilt University John Murrell Homerton College, University ofCambridge (Manuscript received January II, 1994; revision accepted for publication January 4, 1996.)
Sound Sequencing Software for the Amiga Microcomputer The Amiga microcomputer is an attractive piece of hardware for experimental psychologists (Anstis & Paradiso, 1989; Coney, 1989). The attraction lies in the Amiga's custom-designed chips, which speed graphics processing and enhance input and output performance. Early models (the A500, AIOOO, or A2000) can be purchased cheaply, and they possess most of the features of later models. In the auditory domain, all Amigas contain four 8-bit digital-to-analog converters with stereo output, a feature that only the latest Macintosh or PC-compatible systems possess or can attain with add-on hardware. Previous work has extensively discussed the suitability of this system for research in auditory perception (see Cohen & Mieszkowski, 1989, for an overview of the machine's auditory assets and limitations). Unfortunately, the Amiga's features come at a price of overhead in programming effort. The multitasking operating system can be quite complicated and unforgiving, especially to the novice programmer. The present article
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describes a freely distributable sound sequencer, called MultiSound, designed to assist in the creation and delivery of auditory experiments using Amiga microcomputers. Sequencer software overview. With a text editor, the user creates an ASCII event file to specify the source of sound waveforms and the sequence of sound stimuli to be delivered in a given experiment. The sequencer program then interprets this file to arrange for the delivery of the stimuli. This method does not require Amiga programming knowledge, but does require that the user create a specialized event file for each experimental application. The first major component of the event file is a list of waveforms that can be played over any of the Amiga's four sound channels. There are different ways to specify a waveform. Waveform values can either be generated algorithmically (see Cohen & Mieszkowski, 1989), or sampled from analog sound sources. The sequencer program can generate a number of different waveforms, load and play any IFF8SVX sound file (the popular format for digitized sound files on the Amiga), or read a text file of integers indicating the desired waveform. This flexibility allows waveforms to be generated with the use of aids such as conventional statistics packages, digital sound samplers, or programs written in any computer language. The second major component of the event file is a list of event-action pairs. The sequencer software cycles through this list waiting for the appropriate event to occur and responds with the associated action. Events can occur immediately, after the expiration ofa millisecond-accurate timer (Wright, 1986), or after some prespecified value arrives over the serial port. Actions may consist of assigning different waveforms to be played through one of the Amiga's four sound channels, or turning sounds on or off. Possible actions also include pausing, resuming, or changing the period or volume of already playing sounds with values supplied in the event file, or with values that arrive over the serial port. Program directives also allow waiting for a variable period of time, getting or sending a byte via the serial port, and, where hardware permits, toggling the Amiga's low-pass hardware filter on or off. The serial port sequencer directives allow the Amiga to be controlled by another computer that directs the playing of specific sounds in the sequence of events constituting the experiment. Because the values arriving over the serial port can be used to control qualitative aspects of the sounds, adaptive psychoacoustic experiments are possible. Since the sequencer has the ability to both send and receive values via the serial port, multiple Amigas, each equipped with specialized event files, can be used simultaneously to generate more than two independent sound channels. Such a feature is useful in experimental setups where more than two separate auditory sources are desired (i.e., for a multiple location auditory localization experiment in which a single channel feeds sound to a single
