Perceptions of and by Women in a Military Setting:  The One-with-Many Design

Deborah A. KashyMichigan State University

The One-with-Many Design A person is in multiple dyads, but each partner is in a

dyad only with that person

The “One” is the focal person The “Many” are the partners

Blend of the standard dyadic design and a Social Relations Model design

In the intergroup context, the focal person may be a member of one group (e.g., a woman), and the partners may be members of another group (e.g., men)

Distinguishable case: Partners can be distinguished by roles

e.g., family members (Mother, Father, Sibling) Typically assume equal # of partners per focal

person

Indistinguishable case: All partners have the same role with the focal person

e.g., students with teachers or manager with workers

No need to assume equal N

Who provides the data?

1PMT = 1 perceiver, many targets Focal person provides data for each

partner E.g., teacher rates each child on

agreeableness

1PMT: Focal person provides data with respect to the partners

Source of nonindependence: Actor effect: tendency to see all partners in

the same way

Who provides the data?

MP1T = Many perceivers, one target Each partner provides data for the

focal person E.g., each student in a class rates the

teacher

MP1T: Partners provide data

Source of nonindependence: Partner effect - tendency of all partners to see

the focal person in the same way

Who provides the data?

Reciprocal or 1PMT-MP1T Data are collected from both the focal

person and the partners E.g., Teacher rates the students AND

students rate the teacher

Indistinguishable case: All partners have the same role with the focal person

Sources of nonindependence More complex…

Sources of nonindependence in the reciprocal design

Individual-level effects for the focal person: Actor & Partner variance Actor-Partner covariance

Dyadic effects Relationship (plus error) variance Dyadic reciprocity covariance

Data Analytic Approach for estimating variances: 1PMT

FocalID PartID DV

1 1 6

1 2 5

1 3 5

2 1 3

2 2 2

2 3 4

2 4 3

3 1 7

3 2 8

Estimate a basic multilevel model in which There are no fixed effects with a random intercept.

Yij = b0j + eij

b0j = a0 + dj

The variance of the random intercept estimates actor variance

MIXED dv /FIXED = | SSTYPE(3) /PRINT = SOLUTION TESTCOV /RANDOM INTERCEPT | SUBJECT(focalid) COVTYPE(VC) .

SPSS output for 1PMT

Covariance Parameters

Fixed EffectsEstimates of Fixed Effectsa

6.934020 .228724 21.066 30.316 .000 6.458453 7.409587ParameterIntercept

Estimate Std. Error df t Sig. Lower Bound Upper Bound

95% Confidence Interval

Dependent Variable: DV.a.

Estimates of Covariance Parametersa

1.212359 .189978 6.382 .000 .891758 1.648222

.790917 .336679 2.349 .019 .343391 1.821681

ParameterResidual

VarianceIntercept [subject= FOCALID]

Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound

95% Confidence Interval

Dependent Variable: DVa.

So the absolute actor variance is .791, and % is .791/(.791+1.212) = 39.5%

Data Analytic Approach for estimating variances & covariances: The Reciprocal Design

A fairly complex multilevel model…

MIXED motivated BY role WITH focalcode partcode /FIXED = focalcode partcode | NOINT SSTYPE(3) /PRINT = SOLUTION TESTCOV /RANDOM focalcode partcode | SUBJECT(focalid)

covtype(un) /REPEATED = role | SUBJECT(focalid*dyadid) COVTYPE(UN).

(Please see handout)

Example: Perceptions of and by Women in the Texas A&M Corps of Cadets

University/ROTC organization similar in structure to VMI or the Citadel

Established in 1876

Today Includes about 2000 students (about 5% of Texas A&M student body)

Approximately 94% male at time of data collection (1998-1999)

History of Women in the Corps

1974 - Participation in the Corps of Cadets opened to women.

50 women join, organized into an all female unit. The members referred to derisively as "Waggies.“

1978 - Female cadets are allowed to participate in the Bonfire cut.

not allowed to cut any tree bigger than 12 inches in diameter,

worked in a separate area from the men. In past, women were only allowed to work as members

of the "Cookie Crew" or as "Water Wenches." 1984 - Women integrated into all Corps

organizations

The One-with-Many Corps data

Method Participants

Full Study: N = 380 (353 Men & 27 Women)

Today’s data: 21 women with 101 partners Number partners per woman varies from 3

to 19 Procedure

Met with Corps leaders Individual lunches with First Sergeant of

each outfit Data collection at weekly outfit meetings

Measures P’s rated each member of their class and outfit,

including themselves, on 14 dimensions relevant to success in the Corps (9-point scale) Motivation

dedicated, physically fit, diligent, motivated

Leadership good leader, self-confident

Character integrity, selfishness(R), tactful, respects authority,

arrogant(R)

Masculine Masculine, Feminine (R)

Variance partitioning results

Variable Woman as Actor

Variance %

Woman as Partner

Variance %

Motivated .98* 43.9 .73* 32.3

Character 1.14* 36.9 .70* 31.7

Leadership .70* 28.7 1.01+ 29.5

Masculine .20* 31.1 .03 1.0

The woman-as-actor variances indicate significant assimilation in perceptions of their male partners.

The woman-as-partner variances indicate that there was significant consensus on the woman’s attributes.

Reciprocity CorrelationsGeneralized

Women who generally saw men as more motivated (& higher character) were seen by men as more motivated (& higher character).

For leadership, women who saw men as higher in leadership were perceived to be lower in leadership

DyadicIf the woman saw the man as uniquely high in character, he tended to reciprocate.

If the woman saw the man as uniquely high in masculinity, he tended to see her as uniquely low in masculinity (i.e., more feminine).

Variable Generalized Dyadic

Motivated .428 .002

Character .367 .135

Leadership -.132 .077

Masculine .na -.175

Note that none of thesecorrelations are statisticallysignificant

Differences in Mean ratings of “outgroup” perceptions

Women saw the men in their outfits in a more positive manner than the men saw the women.

Variable

Woman’s rating of male

partners

Men’s rating of female

focal person t

Motivated 7.10 5.94 4.11**

Character 6.33 6.34 .02

Leadership 6.62 5.02 4.31**

Masculine 8.18 2.83 30.84**

Differences in Women’s self-ratings and the average of the men’s perceptions of those women

Men, on average, rated women significantly lower in motivation and leadership than women rated themselves. The difference for character was marginally significant.

Variable Men’s rating of female

focal personWoman’s self

ratings t(20)

Motivated 5.94 6.99 3.62**

Character 6.34 6.90 1.90+

Leadership 5.02 6.33 3.98**

Masculine 2.83 2.69 .28

Differences in men’s mean self-ratings and the average of the women’s perceptions of those men

Women, on average, rated men significantly lower in character and leadership than men rated themselves.

BUT the differences are smaller than for women

Variable Woman’s rating of male partners

Men’s self ratings

t

Motivated 7.10 7.42 1.48

Character 6.33 6.85 2.12*

Leadership 6.62 7.30 3.28**

Masculine 8.18 8.15 .36

Self-other slopes: Do men see women as they see themselves (& vice versa)?

Variable Women’s Self predicting men’s

ratings of women Slope

Men’s Self-ratings predicting women’s ratings

of men slope

Motivated .352* .307**

Character .272 .490**

Leadership .392* .394***

Masculine .125 .170*

Both men and women show some self-other agreement.

Discussion The Corps results indicate that Women are not

perceived to be as successful as men in the corps.

Variance partitioning suggests that there is consensus among the men concerning which women are more successful and which are less successful

The correspondence between the women’s self-perceptions and how they are seen by men suggests that women agree with men about their level of success.

What makes the one-with-many design unique Ability to estimate focal person’s behavioral

and perceptual consistency across partners.

Ability to estimate Consensus when partners provide data concerning focal person

Ability to estimate both Generalized and Dyadic reciprocity when both focal person and partners provide data

Thanks!

Thanks also to Jennifer Boldry Wendy Wood The Texas A&M Corps of Cadets