goldstein conflict-cooperation scale for international events

A Conflict-Cooperation Scale for WEIS Events DataAuthor(s): Joshua S. GoldsteinSource: The Journal of Conflict Resolution, Vol. 36, No. 2 (Jun., 1992), pp. 369-385Published by: Sage Publications, Inc.Stable URL: http://www.jstor.org/stable/174480 .Accessed: 08/06/2011 16:00

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unlessyou have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and youmay use content in the JSTOR archive only for your personal, non-commercial use.

Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at .http://www.jstor.org/action/showPublisher?publisherCode=sage. .

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such transmission.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

Sage Publications, Inc. is collaborating with JSTOR to digitize, preserve and extend access to The Journal ofConflict Resolution.

http://www.jstor.org

http://www.jstor.org/action/showPublisher?publisherCode=sage

http://www.jstor.org/stable/174480?origin=JSTOR-pdf

http://www.jstor.org/page/info/about/policies/terms.jsp

http://www.jstor.org/action/showPublisher?publisherCode=sage

A Conflict-Cooperation Scale for WEIS Events Data

JOSHUA S. GOLDSTEIN

University of Southern California

The problem of aggregating WEIS events data, coded as discrete events, into a continuous time series representing conflict or cooperation between two nations is discussed. Past literature on the subject reveals continuing confusion and controversy regarding such a conflict-cooperation scale. A new scale based on a small panel of international relations faculty is presented. Replication of several past studies of great power reciprocity, using the new scale, shows a slight increase in the statistical significance of relationships.

International events data-day-by-day coded accounts of who did what to whom as reported in the open press-continue to offer the most detailed record of diplomatic interactions available in quantitative form to the international relations research community. In recent years a second wave of interest in events data is evident. Researchers are planning for new kinds of events data collection efforts (Alker 1988; Hermann 1988), experimenting with machine coding of news text into events data (Schrodt 1988; Schrodt and Donald 1990), and applying new methods to existing events datasets

(Achen 1987; Dixon 1988; King 1989). In the past decade researchers have used new statistical methods with

events-based time series data to probe such questions as the presence of

reciprocity between nations (Rajmaira and Ward 1990; Goldstein and Freeman

1990; Goldstein 1991). These time series studies must rely on one or another method to convert event-by-event data into aggregate time series representing a country's behavior toward another over time (King 1989). This crucial

AUTHOR'S NOTE: For research support and facilities, the author thanks the USC School of International Relations, the USC College of Letters, Arts, and Sciences, the University of Massachusetts, and the John D. and Catherine T. MacArthur Foundation. The participation of the USC faculty panelists is also gratefully acknowledged.

JOURNAL OF CONFLICT RESOLUTION, Vol. 36 No. 2, June 1992 369-385 ? 1992 Sage Publications, Inc.

369

370 JOURNAL OF CONFLICT RESOLUTION

step has been a difficult one for users of events data, as it turns out, and continues to generate controversy.'

To convert individual events to time series, or to aggregate them in any other way, requires some method of combining different types of events into a single theoretically meaningful measure (in one or more dimensions) of the relationship between two nations.2 Most common has been the conversion of events into a measure of cooperation or conflict - the affect or tension im- plicit in a series of actions taken by one nation toward another during a period of time (see Moses et al. 1967). In the COPDAB events dataset, an officially sanctioned set of weights, originally developed by panels of "judges," specifies the amount of cooperation or conflict thought to be conveyed typically by a type of event such as verbal criticism or a military attack (Havener and Peterson 1975; Azar 1980). COPDAB includes a cooperation scale and a separate conflict scale, which can be conceptually "joined" to form a single dimension of conflict-cooperation (Sloan 1975, 31-32).3

McClelland's (1971) World Events Interaction Survey (WEIS) presents more serious problems for the aggregation of events into conflict-cooperation time series.4 WEIS was constructed within a conceptual framework that explicitly denies the possibility of reducing data to one dimension of conflict- cooperation.5 Rather, the 61 WEIS event types are grouped into 22 verb categories, such as "promise," "accuse," or "threaten." WEIS is considered

1. Other problems with events data, such as source selection, coding rules, and data management, continue to receive attention as well (Howell 1983; McClelland 1983; Vincent 1983; Howell et al. 1987). Some argue that differences in source coverage, more than in scaling, account for differences between the COPDAB and WEIS data (Hoggard 1974; Burrowes 1974; McClelland 1983; Howell et al. 1987; Alker 1988), but Tomlinson claims that the addition of more sources for WEIS makes little difference (see Howell 1983).

2. One can simply count all events equally and thus measure the "volume" of interaction in each time period, but this is theoretically uninteresting.

3. Many researchers have analyzed conflict and cooperation events separately because of doubts about joining the scales. Lebovic (1985, 55) gives a theoretical rationale for combining cooperation and conflict on one scale. King (1989) discusses problems with truncation that occur in a conflict-only or cooperation-only time series; these problems are reduced in a combined conflict-cooperation continuum.

4. Similar issues apply to datasets such as the Behavioral Correlates of War (BCOW) or CREON events data, which derive from the WEIS coding scheme.

5. The concern among COPDAB users about whether conflict and cooperation should be kept as two separate dimensions applies to WEIS as well. For instance, Ward (1981, 1982) analyzes conflict events and cooperation events separately. Havener and Peterson (1975) argue that McClelland actually assumes an underlying conflict-cooperation continuum, which they doubt exists; they find differences in coding across data sets problematical. They suggest that WEIS needs situation codes as well as action codes for each event. Vasquez and Mansbach (1984, 413-14) argue against combining conflict and cooperation, and propose a "drastic reformulation" with three independent dimensions (agreement-disagreement, positive-negative acts, friendship- hostility). Beer and Ringer (1990) propose two scales, one for verbal and one for action events, and urge caution in scaling event data.

Goldstein / A CONFLICT-COOPERATION SCALE 371

a "categorical" dataset, unlike the "scaled" datasets such as COPDAB (Burgess and Lawton 1975, 118; Leng 1975, 1978, 132; Alker 1988, 3). But researchers continue to convert WEIS data to a conflict-cooperation continuum in order to facilitate time series analyses as well as to allow WEIS to serve as a robustness check on COPDAB results.6 Because COPDAB data end in 1978 and WEIS is the only dataset to be continuously collected into the 1980s, researchers increasingly rely on these data in time series studies of international relations.7 Thus the issue of whether and how WEIS data can be weighted for aggregation is still an important one. The rest of this article will review the past efforts to weight WEIS events and then present a new set of conflict-cooperation weights.

SCALING WEIS DATA- A REVIEW

Despite the idea that WEIS data could not be reduced to a conflict-cooperation continuum, McClelland allowed that the categories could be grouped into conflictual, cooperative, and neutral events, the neutral events being "participation" events such as comment or consult (McClelland and Hoggard 1969). The cooperative and conflictual categories could further be grouped into verbal and action types as follows (the percentage of all events in the WEIS dataset for 1966 is shown):

Verbal cooperation (approve, promise, agree, request, propose) 24% Cooperative action (yield, grant, reward) 9% Participation (comment, consult)8 35% Verbal conflict-defensive (reject, protest, deny) 8% Verbal conflict-offensive (accuse, demand, warn, threaten) 16% Conflict action (demonstrate, reduce relationship, expel, seize, force) 8%

This grouping created the possibility of aggregating events by simply counting the number of conflictual or cooperative events (ignoring the

6. McClelland (1983) argues that WEIS must be scaled on a conflict-cooperation continuum for comparison with COPDAB, but he opposes such scaling when analyzing WEIS data themselves.

7. There have been problems of frequent coder changes as well as accessibility of the data to the international relations research community, but the 1980s data are slowly diffusing into the community. WEIS data through 1979 are publicly available through ICPSR. Charles McClelland and his students and associates continue to update the data, with the most complete version now that of Rodney Tomlinson at the U.S. Naval Academy. The story of how WETS moved from academia into the U.S. government policy community, where it failed to gain acceptance, is told by Andriole and Hopple (1984) and Laurance (1990).

8. Participation events were about equally divided between verbal participation and participatory actions.


neutral "participation" events). Some researchers continue to use this unweighted event-count aggregation method. For example, Andriole (1984) uses a simple count of cooperative and hostile events to generate time series for "early warning" of crises and for a study of East-West detente during the period from 1968 to 1977. Mansbach and Vasquez (1981, 862) code U.S. and West German behavior in terms of McClelland's groups above (combining verbal conflict into one group).

Researchers have been inconsistent, however, in how to define the categories of conflict and cooperation within which to count events. For instance, Bobrow (1982, 36-41, 47) rejects any weighting and counts the number of events "pro" and "anti" detente for annual aggregation; but he includes in the "pro" category the "comment" and "consult" categories which McClelland argues should be neutral. Dixon (1983, 1987, 1988) also uses unweighted event counts but within a different set of event category groups based on affect (positive, neutral, negative). His scheme has more neutral categories than McClelland's, omits some categories altogether, and some- times separates event types from within the same WEIS (2-digit) verb. Ward (1981, 1982) bases his event count categories on past factor analyses of nations' annually aggregated behavioral output (an approach rooted in comparative foreign policy).9 Howell's (1983) study of U.S.-Soviet conflict and cooperation includes "consult" events but not "comment" events in the cooperative total-a point that figures in his exchange with McClelland (McClelland 1983; Howell 1984,111-12).

Researchers also do not agree on whether or how these event counts for conflict and cooperation can be combined to measure the level of conflict- cooperation in a country's behavior toward another on a single continuum. Kegley, Richardson, and Richter (1978, 744-45), for instance, calculate a "composite scale of foreign conflict initiated by each nation," which is the result of dividing the number of conflictual events by the total conflictual and cooperative events; such a ratio "controls for national differences in

9. Salmore and Munton (1974) factor analyze the annual event counts for each country's total foreign policy output (actions taken by that country) for the 22 WEIS verbs. They find two dimensions: "inverse commitment" and "conflict-cooperation." They recategorize the 22 WEIS (2-digit) verbs into cooperative action, participation, diplomatic exchange, verbal conflict, nonmilitary conflict, and military conflict. Nearly half the events fall in the participation category. Young (1975) similarly factor analyzes all countries in the WEIS dataset for 1966-1969. He develops three groups (diplomatic exchange, nonmilitary conflict, and military conflict), within which to aggregate events by counting numbers of events. Wilkenfeld, Hopple, and Rossa (1979) find similar factors based on 56 countries in each of 5 years (1966-1970). Their two factors are: (1) "constructive diplomatic behavior" (49% of total variance), which included some of what McClelland would call neutral events (comment, consult) as well as conflictual events (reject, deny, warn, negative sanctions); and (2) "nonmilitary conflict events" (23% of variance). Because use-of-force events did not load on these factors, Ward (1981, 1982) excludes them from his count of conflict events (unfortunately, in my view).


volume of external activity" (which they find useful for comparative foreign policy analysis). In a subsequent exchange, Vincent (1981, 131-32) criticizes, and Richardson and Kegley (1981, 146) defend, this formula for measuring conflict.

The above methods all rely on counting numbers of events within vari- ously defined categories. The alternative is to aggregate events by weighting different event types differently. The issue of weighting versus counting events is central to Vincent's (1990) exchanges with Dixon (1990) and with Kegley and Richardson (Kegley, Richardson, and Richter 1978; Vincent 1981; Richardson and Kegley 1981). It plays a role in Howell's (1983, 1984) exchange with McClelland (1983). Howell (1983) claims that weighted and unweighted aggregations of WEIS give "virtually identical" results in his comparison with COPDAB, but this has not been accepted by other researchers.

Several WEIS weighting schemes have been developed, although none are fully documented in the primary literature. One simple weighting scheme is that of Walker et al. (1984, 40-42), who rank the 22 WEIS verbs according to a variation of the McClelland categories above (physical conflict, offensive verbal conflict, defensive verbal conflict, defensive verbal cooperation, offensive verbal cooperation, and physical cooperation) and then give each verb a weight along an equal-interval scale from -12 to +8, with the two participation categories (comment and consult) weighted at zero. Calhoun (1978, 15) gave 100 State Department employees a semantic differential questionnaire with 14 scales, based on 2-digit WEIS verbs. He then factor analyzed the results to construct two weighting scales for high and low threat situations respectively. Richardson, Kegley, and Agnew (1981) use an earlier, different version of Calhoun's scale in their study of symmetry and reciprocity in international dyads. Vincent (1979) develops another set of weights by having 30 judges assign weights from 1-5 to each of the 22 WEIS verbs. Richardson and Kegley (1981, 148) criticize this scale because judges "presumably were not informed of the variety of acts denoted by each label." They express doubt about any method of scaling WEIS data; nonetheless, they find "somewhat similar ordinal rankings" between Vincent's weights for conflict events and Calhoun's 1972 weighting scheme. Goldstein and Freeman (1990, 39) and Rajmaira and Ward (1990, 462) use the Vincent scale in their respective analyses of U.S.-Soviet-Chinese reciprocity.'0

The Vincent scale appears to be the best weighting system available, but it still has several limitations. First, the construction of the weights has not been documented in the primary (indexed journal) literature. Second, the scale (like the others described above) applies only to the 22 two-digit WEIS

10. Ward and Rajmaira (1992) recode WEIS events into the COPDAB 15-point scale, excluding "neutral" events.

374 JOURNAL OF CONFLICTRESOLUTION

verbs rather than the 61 event types, thus losing some information available in the raw data. For instance, extending economic aid and extending military aid must be weighted equally under the "grant" verb; canceling a ballet performance must be weighted equally with breaking diplomatic relations under the "reduce relationship" verb. Third, the weights for the two-digit verbs are not very differentiated along the scale, ranging from 2.7 to 4.5 for cooperative events and 2.2 to 4.7 for conflictual events. This is counter- intuitive because most observers would agree that the most conflictual (or cooperative) event is much more than twice as conflictual (cooperative) as the least.

Surprisingly, there is no documented set of conflict-cooperation weights for all 61 (three-digit) WEIS event types. The remainder of this article details such a set of weights for use in future events data research. These weights derive from the judgments of a panel of international relations faculty who placed WEIS events along a "net cooperation" scale running from extreme conflict or hostility at one end to extreme cooperation or friendliness at the other.

THE PANEL

I drew the panel from the faculty of the School of International Relations at the University of Southern California (incidentally, this was the home of WEIS in the late 1960s). The 20 international relations faculty at USC span policy-oriented and theoretically oriented research. Eight faculty members including myself agreed to participate."

The panel was relatively young, with five assistant professors, two associate professors, and one full professor.'2 It included two women and six men.

Regional expertise was diverse: one panelist each for Africa, Asia, Europe, the Middle East, the Soviet Union, and North America, and two "globalists." Four of the eight panelists used quantitative data or methods in their research; the other four were nonquantifiers.'3 The panel thus represented the commu-

nity of international relations researchers fairly well.4

11. An additional five faculty members were asked, but declined, to participate as panelists. My own weights were close to the panel mean, so excluding myself would change the mean weights little but would reduce the number of cases (increase the standard deviations).

12. The five faculty who declined to participate were all associate or full professors. 13. Only two of the panelists had contact with McClelland's WEIS program at USC. 14. If this panel of eight showed reasonably strong consensus on the weighting of event types,

I saw no need to employ larger panels, international participants, government policy-maker judges, and so forth.

Goldstein / A CONFLICT-COOPERA TION SCALE 375

PROCEDURES

Each panelist received a shuffled deck of 61 cards, each containing the text of a WEIS event type (but not the numeric event code). Each text was worded as an action taken by country A toward country B; for example, the event type "give warning" was worded "country A gives warning to country B." Panelists also received two scales, one for conflict and one for cooperation, each several feet long. Each scale ran from 0 to 10, and was divided into boxes corresponding with each integer.'5

Panelists were asked first to sort the cards into cooperative (friendly) actions and conflictual (hostile) ones, putting aside "neutral" actions in a third pile.'6 Panelists then placed the most conflictual act in the "10" box on the conflict scale, and sorted the conflict cards into the 10 boxes based on a linear scale representing the amount of conflict typically embodied in the action, with zero as a neutral act and 10 as the most conflictual act. To reinforce the linearity of the scale, panelists were then asked to compare the 3 and 6, 4 and 8, and 5 and 10 boxes to ensure that events in the second box were twice as conflictual as the first (and to adjust items where necessary). Panelists then wrote the number, as a negative number, on each card, starting with -10 for the most conflictual event.'7

This procedure was basically repeated for the cooperative cards. The cooperative scale was laid out next to the conflict scale (which still had the cards in place), and panelists this time were asked to place cooperative events along a scale equivalent to the conflict scale (with equal but opposite items at the same place on the scale). Thus the most cooperative event was not necessarily at + 10 on the scale, but was lined up with a conflict event of equal weight. Panelists were asked to check for linearity as before. The cooperative weights were then written on the cards with a "+." Panelists recorded neutral events as zero. The panelists then returned the cards and I recorded their weights for each item.

RESULTS

Table 1 gives the new weightings for the 61 WEIS events, which are the mean weights assigned to each event type by the eight panelists. The standard

15. The instruction page and text of cards and scales are available from the author on request. 16. Since some studies (e.g., Goldstein and Freeman 1990, 41) refer to a "cooperation-

hostility" continuum, in wording the instructions to panelists I included both the terms "conflict" and "hostility" to describe one end of the scale and the terms "cooperation" and "friendliness" for the other.

17. Two panelists used decimal scores on a few items to convey close distinctions.


TABLE 1

New Weights for WEIS Events

Event Type Weight SD

223 Military attack; clash; assault -10.0 211 Seize position or possessions -9.2 222 Nonmilitary destruction/injury -8.7 221 Noninjury destructive action -8.3 182 Armed force mobilization, exercise, display; military buildup -7.6 195 Break diplomatic relations -7.0 173 Threat with force specified -7.0 174 Ultimatum; threat with negative sanction and time limit -6.9 172 Threat with specific negative nonmilitary sanction -5.8 193 Reduce or cut off aid or assistance; act to punish/deprive -5.6 181 Nonmilitary demonstration, walk out on -5.2 201 Order person or personnel out of country -5.0 202 Expel organization or group -4.9 150 Issue order or command, insist, demand compliance -4.9 171 Threat without specific negative sanction stated -4.4 212 Detain or arrest person(s) -4.4 192 Reduce routine international activity; recall officials -4.1 112 Refuse; oppose; refuse to allow -4.0 111 Turn down proposal; reject protest, demand, threat -4.0 194 Halt negotiation -3.8 122 Denounce; denigrate; abuse -3.4 160 Give warning -3.0 132 Issue formal complaint or protest -2.4 121 Charge; criticize; blame; disapprove -2.2 191 Cancel or postpone planned event -2.2 131 Make complaint (not formal) -1.9 063 Grant asylum -1.1 142 Deny an attributed policy, action, role or position -1.1 141 Deny an accusation -0.9 023 Comment on situation -0.2 102 Urge or suggest action or policy -0.1 021 Explicit decline to comment -0.1 094 Request action; call for -0.1 025 Explain or state policy; state future position 0.0 091 Ask for information 0.1 011 Surrender, yield to order, submit to arrest 0.6 012 Yield position; retreat; evacuate 0.6 031 Meet with; send note 1.0 095 Entreat; plead; appeal to; beg 1.2 101 Offer proposal 1.5 061 Express regret; apologize 1.8 032 Visit; go to 1.9 066 Release and/or return persons or property 1.9

0.0 0.7 0.5 0.6 1.2 1.3 1.1 1.4 1.9 1.4 2.1 1.7 1.4 1.7 1.5 2.3 1.2 1.5 1.5 0.9 1.1 1.3 0.9 1.3 1.5 0.6 2.5 1.0 1.3 0.5 1.5 0.6 1.0 0.0 0.4 7.2 6.6 0.9 1.8 1.9 1.5 2.4 2.7


TABLE 1 Continued

Event Type Weight SD

013 Admit wrongdoing; apologize, retract statement 2.0 2.2 062 Give state invitation 2.5 2.7 054 Assure; reassure 2.8 2.2 033 Receive visit; host 2.8 3.0 065 Suspend sanctions; end punishment; call truce 2.9 3.6 082 Agree to future action or procedure, to meet, or to negotiate 3.0 2.5 092 Ask for policy assistance 3.4 1.1 093 Ask for material assistance 3.4 2.4 041 Praise, hail, applaud, extend condolences 3.4 2.1 042 Endorse other's policy or position; give verbal support 3.6 1.8 053 Promise other future support 4.5 1.6 051 Promise own policy support 4.5 1.7 052 Promise material support 5.2 1.5 064 Grant privilege; diplomatic recognition; de facto relations 5.4 1.4 073 Give other assistance 6.5 1.9 081 Make substantive agreement 6.5 1.4 071 Extend economic aid; give, buy, sell, loan, borrow 7.4 1.0 072 Extend military assistance 8.3 0.9

NOTE: Weight is mean of weights assigned by eight panelists; SD is standard deviation across panelists.

deviations are also shown. The event rated as most conflictual was military attack, whereas that rated most cooperative was extending military assistance; this seems to reflect the continuing emphasis placed on military affairs by international relations scholars.

Panelists generally agreed on the weighting of each item, within 1 or 2 points on the scale. But there was great lack of agreement for two event types in particular- "surrender" and "retreat" - which some panelists rated as highly cooperative and others as highly conflictual. Because the new weightings for these events are near zero, the events will largely drop out in aggregations using these weights. For two events the panel was in perfect agreement- "explain policy" was rated zero, and "military attack" rated -10, by all eight panelists.

Figure 1 shows the standard deviation for each event type as a function of the new weighting for that event. The figure shows that panelists agreed more on weightings near both ends of both the conflict and cooperation scales (i.e., at -10, 0, and +10) than in the middle of either. Also, there was somewhat less agreement about the weighting of mildly cooperative items than of mildly conflictual ones-an effect earlier reported by Azar and Havener


8 - X ---l ___

7 - * (Su1rrender)

* (Retreal)

6 --

o

5 -_

Q 4 -

00 sH *

2- 1 -?* ? -f f f * *

1 * : *: . ?. . .. 0 , . . . .. . . 0 , ,

-10 -5 0 5 10 Conflict Mean Event Weight Cooperation

Figure 1: Agreement on Coding by Scale Value

(1976, 241) and by Beer and Ringer (1990). These rather systematic patterns in the standard deviations suggest that future statistical analyses could gain some power by incorporating assumptions about measurement error (Achen 1987).

Figure 2 represents the squared deviation from the panel mean (across 61 items) for each of the eight panelists, as a function of the age of the panelist. The five panelists in closest agreement overall were younger than 45 years, while the three with greatest differences from the panel mean were over 45. (An age effect is still evident if the two outlier events, "surrender" and "retreat," are excluded from this analysis.) The close agreement in coding among the younger panelists, while only suggestive, might reflect a greater coherence of outlook among those trained more recently.

To compare the new scale with the Vincent scale in one research context, I replicated several analyses of great power reciprocity from Goldstein and Freeman (1990) and Goldstein (1991), using WEIS time series generated with the new weights. Because the earlier results rely on the convergence of

Goldstein / A CONFLICT-COOPERATION SCALE

400 I I *I

0

4 300 -

Q)

2 200 - 0-4

o ? 100 -

0 0 1 , I I , i i , I i , , ,

30 40 50 60

Age

Figure 2: Coder Deviation from Panel as a Function of Age

three different data sets, this replication had a methodological rather than substantive purpose. If the new scale was able to capture fluctuations in net

cooperation (cooperation minus conflict) more accurately than the rougher Vincent scale, the data should be a bit less "noisy" and hence relationships previously "washed out" by measurement error might appear more statisti-

cally significant.'8 As Table 2 indicates, the time series using the new weights correlate only

a bit more closely with the COPDAB series, overall, in terms of month-to- month fluctuations during the COPDAB-WEIS overlap period from 1966 to 1978 (cf. Goldstein and Freeman 1990, 39). This is consistent with the idea that the new weights capture more finely the variations in monthly behavior, as do the weighted COPDAB events. The effect is not dramatic, however.

18. This approach seeks to partially uncover, with the new scale, relationships that exist in the real world but that were masked by noise under the old scale. Such relationships, of course, may or may not actually exist. Also, several important relationships were already significant at .01 in the old studies (despite "noise"), and noise reduction can hardly help in these instances. In general, divergence from the earlier studies could indicate either worse or better measurement (because no one knows the "true" relationships), but with a presumption that greater statistical significance indicates better measurement in a context of known "noise" in the data (see Goldstein and Freeman 1990, 40-41).

379

380 JOURNAL OF CONFLICTRESOLUTION

TABLE 2

Cross-Correlations of WEIS and COPDAB Monthly Time Series Using Vincent Scale and New Scale

Variable Vincent Scale New Scale

United States toward USSR .47 .47 USSR toward United States .57 .56 United States toward China .36 .42 China toward United States .55 .57 USSR toward China .89 .91 China toward USSR .74 .75

NOTE: Vincent scale results are from Goldstein and Freeman (1990, 39). New scale results are from replicated analysis using WEIS time series generated with the new weights.

I then replicated Goldstein and Freeman's (1990, 170) Vector Autoregres- sion analysis of monthly WEIS time series for the period from 1969 to 1982, using the new weights. Table 3 contains the statistical significance levels of the F tests for each variable (indicating a lagged correlation with the dependent variable in the equation). The overall nature of the results did not change, but there were some subtle differences. Two variables were no longer significantly self-driven (Soviet behavior toward China and China toward Soviet), while one bilateral response went from marginally significant to insignificant (the U.S. response toward the Soviets). Most interestingly, among triangular responses the three originally found significant were still significant, but so were four additional responses. Thus the new scale seems to illuminate somewhat more detailed triangular interactions in the U.S.- Soviet-Chinese system (relationships that would be masked by noise), and to reduce somewhat the appearance of self-drivenness or inertia (relationships that could be exaggerated by noise). This result is consistent with the idea that the scale captures somewhat more accurately the fine structure of month-to-month relations, reducing noise.

Finally, I replicated the analysis of bilateral U.S.-Soviet relations in Goldstein (1991) using the new WEIS weights (see Table 4). This analysis . uses weekly data aggregations. The self-driven responses and one bilateral response were already statistically significant at the .01 level, and remain so, whereas the other bilateral response is still insignificant but much closer to being significant. The differences using the new scale are thus marginal but again are in the direction of finding greater detail (slightly more response, slightly less inertia).

Goldstein / A CONFLICT-COOPERA TION SCALE 381

TABLE 3

Significance of F Statistics in U.S.-Soviet-China VAR Model Using Old and New Scale

Dependent Independent Variable Variable Old Scale New Scale Comments

US US .035* .045* UC .035* .064t SU .087t .274 Bilateral response, no longer significant SC .140 .020* New triangular response CU .114 .068t New triangular response CS .569 .291

UC US .774 .755 UC .340 .299 SU .102 .794 SC .157 .178 CU .671 .259 CS .729 .389

SU US .017* .005** UC .098t .055t SU .107 .533 SC .945 .571 CU .503 .856 CS .499 .521

SC US .780 .898 UC .801 .920 SU .061 t .023* SC .047* .177 Self-driven, no longer significant CU .012* .062t CS .010** .049*

CU US .138 .156 UC .004* .008** SU .166 .019* New triangular response SC .385 .255 CU .001*** .002** CS .444 .085t

CS US .172 .059t New triangular response UC .565 .308 SU .003 * * .001 * * SC .673 .415 CU .175 .118 CS .034* .144 Self-driven, no longer significant

NOTE: Significance levels below .001 are listed as .001. N = 157 (months). Statistical significance levels are shown for F tests on effects of lagged independent variables on behavior of dependent variable, in Vector Autoregression model (constants are included but not shown here). Variable name indicates actor and target (e.g., UC is U.S. net cooperation toward China). Vincent scale results are from Goldstein and Freeman (1990, 170). Right column shows reanalysis using new scale. tp < .10; *p < .05; **p < .01; ***p < .001.


TABLE 4

Significance of F Statistics in Weekly Aggregated U.S.-Soviet Model Using Old and New Scale

Dependent Variable Independent Variable Old Scale New Scale

US US .001 .001 SU .391 .134

SU US .001 .001 SU .001 .002

NOTE: Statistical significance levels are shown for F tests on effects of lagged independent variables (eight lags) on dependent variable (constants are included in equations). Old results are from Goldstein (1991, 204). N = 687 (weeks).

CONCLUSION

The WEIS events dataset provides a potentially rich ongoing source of information on international interactions. This detailed information can be

aggregated, given a workable conflict-cooperation scale, into relatively finely grained time series, which in turn can be analyzed using sophisticated time series statistics. Past studies have aggregated WEIS events by count-

ing conflictual and cooperative events, or by using a rough scale such as Vincent's. The replications above do more to corroborate than to challenge the validity of past studies using the Vincent scale, because results were quite similar using the more detailed new scale.

For future research, however, the new conflict-cooperation scale described in this article offers three advantages over past aggregation methods (in addition to being better documented). First, this scale is better grounded conceptually and corresponds more closely to other event scales such as COPDAB. Second, this scale is empirically grounded in the cross-coder consensus on the eight-member panel of international relations faculty. Third, this scale "performs" slightly better than the Vincent scale in distin-

guishing the fine details of structure in the international system in the three

analyses I replicated (correlation with COPDAB slightly higher; more trian-

gularity and less self-drivenness apparent in the U.S.-Soviet-China system with monthly data; replication of U.S.-Soviet weekly results).

International relations researchers continue (for a variety of practical reasons) to use weighted, aggregated WEIS data in time series studies, despite the critics of event scaling. The new scale presented here should help these researchers to extract more information from the raw event database when constructing aggregate time series. It does not solve all problems with


events data or with the aggregation of events, but it improves modestly on past methods.

REFERENCES

Achen, C. 1987. Statistical models for event data: A review of errors in variable theory. Paper presented at the First DDIR (Data Development for International Research) Conference on Event Data, Columbus, Ohio, May.

Alker, H. R., Jr. 1988. Toward globalized event-data research on avoidable war. DDIR-Update 3 (Data Development for International Research), Merriam Laboratory, University of Illinois, Champaign.

Andriole, S. J. 1984. Detente: A quantitative analysis. International Interactions 11:381-95. Andriole, S. J., and G. W. Hopple. 1984. The rise and fall of event data: From basic research to

applied use in the U.S. Department of Defense. International Interactions 10:293-309. Azar, E. E. 1980. The codebook of the conflict and peace data bank (COPDAB). Typescript,

University of North Carolina at Chapel Hill. Azar, E. E., and T. N. Havener. 1976. Discontinuities in the symbolic environment: A problem

in scaling. International Interactions 2:231-46. Beer, F. A., and J. F. Ringer. 1990. Ranking international cooperation and conflict events. Paper

presented at the annual meeting of the American Political Science Association, San Francisco, August.

Bobrow, D. R. 1982. Uncoordinated giants. In Foreign policy: USA/USSR, edited by C. W.

Kegley, Jr. and P. McGowan, 23-49. Beverly Hills, CA: Sage. Burgess, P. M., and R. W. Lawton. 1975. Evaluating events data: Problems of conception,

reliability, and validity. In International events and the comparative analysis of foreign policy, edited by C. W. Kegley, Jr., G. A. Raymond, R. M. Rood, and R. A. Skinner, 106-19. Columbia: University of South Carolina Press.

Burrowes, R. 1974. Mirror, mirror, on the wall: A comparison of event data sources. In

Comparing foreign policies: Theories, findings, and methods, edited by J. N. Rosenau, 383-406. New York: Wiley.

Calhoun, H. L. 1978. Some problems of measurement: Events and situations. Paper presented at the annual meeting of the International Studies Association, Washington, DC, February.

Dixon, W. J. 1983. Measuring interstate affect. AmericanJournalofPoliticalScience 27:828-51. . 1987. A lag sequential approach to the analysis of foreign policy behavior. In New

directions in the study of foreign policy, edited by C. F. Hermann, C. W. Kegley, Jr., and J. N.

Rosenau, 77-95. Boston: Allen & Unwin. . 1988. The discrete sequential analysis of dynamic international behavior. Quality and

Quantity 22:239-54. - . 1990. Interdependence and cooperation in foreign policy behavior: A comment on

Vincent. International Interactions 16:109-14. Goldstein, J. S. 1991. Reciprocity in superpower relations: An empirical analysis. International

Studies Quarterly 35:195-209. Goldstein, J. S., and J. R. Freeman. 1990. Three-way street: Strategic reciprocity in world

politics. Chicago: University of Chicago Press. Havener, T., and A. Peterson. 1975. Measuring conflict/cooperation in international relations: A

methodological inquiry. In Theory and practice of events research: Studies in inter-nation


actions and interactions, edited by E. E. Azar and J. D. Ben-Dak, 57-61. New York: Gordon & Breach.

Hermann, C. F. 1988. Two kinds of event data. DDIR-Update 3 (Data Development for International Research), Merriam Laboratory, University of Illinois, Champaign.

Hoggard, G. 1974. Differential source coverage in foreign policy analysis. In Comparing foreign policies: Theories,findings, and methods, edited by J. N. Rosenau, 353-81. New York: Wiley.

Howell, L. D. 1983. A comparative study of the WEIS and COPDAB data sets. International Studies Quarterly 27:149-59.

. 1984. Comparative analysis with events data: A reply to McClelland. International Studies Quarterly 28:111-13.

Howell, L. D., S. Groves, E. Morita, and J. Mullen. 1987. Changing priorities: Putting the data back into events data analysis. Foreign Policy Analysis Notes 14:22-31.

Kegley, C. W., Jr., N. R. Richardson, and G. Richter. 1978. Conflict at home and abroad: An

empirical extension. Journal of Politics 40:742-52. King, G. 1989. Event count models for international relations: Generalizations and applications.

International Studies Quarterly 33:123-47. Laurance, E. J. 1990. Events data and policy analysis: Improving the potential for applying

academic research to foreign and defense policy problems. Policy Sciences 23:111-32. Lebovic, J. H. 1985. Capabilities in context: National attributes and foreign policy in the Middle

East. Journal of Peace Research 22:47-67. Leng, R. J. 1975. The future of events data marriages: A question of compatibility. International

Interactions 2:45-62. . 1978. Event data validity: Comparing coding schemes. In Measuring international

behavior: Public sources, events and validity, edited by D. Munton, 127-42. Halifax, NS, Canada: Centre for Foreign Policy Studies, Dalhousie University.

Mansbach, R. W., and J. A. Vasquez. 1981. The effect of actor and issue classification on the analysis of global conflict-cooperation. Journal of Politics 43:861-74.

McClelland, C. A. 1971. The management and analysis of international event data. Typescript, University of Southern California, Los Angeles.

- 1983. Let the user beware. International Studies Quarterly 27:169-78. McClelland, C. A., and G. D. Hoggard. 1969. Conflict patterns in the interactions among nations.

In International politics and foreign policy: A reader in research and theory, edited by J.N. Rosenau, 711-24. New York: Free Press.

Moses, L. E., R. A. Brody, 0. R. Holsti, J. B. Kadane, and J. S. Milstein. 1967. Scaling data on inter-nation action. Science 156:1054-59.

Rajmaira, S., and M. D. Ward. 1990. Evolving foreign policy norms: Reciprocity in the superpower triad. International Studies Quarterly 34:457-75.

Richardson, N. R., and C. W. Kegley, Jr. 1981. Cumulation and the domestic-foreign conflict hypothesis. Journal of Politics 43:143-51.

Richardson, N. R., C. W. Kegley, Jr., and A. C. Agnew. 1981. Symmetry and reciprocity as characteristics of dyadic foreign policy behavior. Social Science Quarterly 62:128-38.

Salmore, S. A., and D. Munton. 1974. An empirically based typology of foreign policy behaviors. In Comparing foreign policies: Theories, findings, and methods, edited by J. N. Rosenau, 329-52. New York: Wiley.

Schrodt, P. A. 1988. Experimental results on coding the New York Times and FBIS. DDIR- Update 3 (Data Development for International Research), Merriam Laboratory, University of Illinois, Champaign.

Schrodt, P. A., and C. Donald. 1990. Machine coding of events data. Paper presented at the annual meeting of the International Studies Association, Washington, DC, April.


Sloan, T. 1975. The development of cooperation and conflict interaction scales: An advance in the measurement and analysis of events data. In Theory and practice of events research: Studies in inter-nation actions and interactions, edited by E. E. Azar and J. D. Ben-Dak, 29-39. New York: Gordon & Breach.

Vasquez, J. A., and R. W. Mansbach. 1984. The role of issues in global co-operation and conflict. British Journal of Political Science 14:411-33.

Vincent, J. E. 1979. Project theory. Lanham, MD: University Press of America. . 1981. Internal and external conflict: Some previous operational problems and some

new findings. Journal of Politics 43:128-42. . 1983. WEIS vs. COPDAB: Correspondence problems. International Studies Quarterly

27:160-68. . 1990. Interdependence reexamined. International Interactions 16:91-107.

Walker, S. G., D. Bohlin, R. Boos, D. Cownie, H. Nakajima, and T. Willson. 1984. Evidence of learning and risk orientation during international crises: The Munich and Polish cases. British Journal of Political Science 14:33-51. .

Ward, M. D. 1981. Seasonality, reaction, expectation, adaptation, and memory in cooperative and conflictual foreign policy behavior. International Interactions 8:229-45.

. 1982. Cooperation and conflict in foreign policy behavior. International Studies Quarterly 26:87-126.

Ward, M. D., and S. Rajmaira. 1992. Reciprocity and norms in U.S.-Soviet foreign policy. Journal of Conflict Resolution 36:342-68.

Wilkenfeld, J., G. W. Hopple, and P. J. Rossa. 1979. Indicators of conflict and cooperation in the interstate system, 1966-70. In To augur well: Early warning indicators of interstate

conflict, edited by J. D. Singer and M. D. Wallace, 109-53. Beverly Hills, CA: Sage. Young, R. A. 1975. A classification of nations according to foreign policy output. In Theory and

practice of events research: Studies in inter-nation actions and interactions, edited by E. E. Azar and J. D. Ben-Dak, 175-95. New York: Gordon & Breach.

goldstein conflict-cooperation scale for international events

Documents