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匿名使用者 發問時間: 社會與文化語言 · 1 0 年前

急~~!!麻煩英文達人幫我翻譯\!!

TASA, TAGGAR, AND SEIJTS

Rate, Morale, Productivity, Quality Index, Unit Cost, Percentage

of Female Employees, Percentage of Minority Employees, and

Grievances. Each indicator had its own index, and each team

started the simulation at the same point on each indicator. For

example, the Morale index began at a score of 50 and could go as

high as 100. The Turnover index was expressed as a percentage,

麻煩各位大大了~"~

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with higher percentages reflecting poorer results. The 10 indicators

were, on average, highly correlated (r .56). The simulation

creates an omnibus measure of performance that can range between

0 and 100, based on equal weighting of the 10 indicators.

可能有點多

我分段po

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This measure was reported to each team, along with the overall

average score, after each weekly decision period. This method

drew a participant’s attention to the overall performance of the

HRM department, rather than performance on only one or two

indicators.

麻煩了

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We refer to the omnibus Week 2 measure as initial

performance and the Week 10 measure as team performance.

In addition to the omnibus measure of performance, a rank

ordered measure of performance was publicly presented to all the

teams during class time

.

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(using an overhead projector) at Weeks 2

and 10. During Week 2, this rank ordered measure was presented

several days before team members were asked to assess their

perceptions of collective efficacy.

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This measure was helpful in

forming accurate efficacy perceptions because it provided stronger

social comparison information regarding performance than an

overall average (see Wood & Bandura, 1989).

.

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The rank measure

correlated highly with the omnibus measure at both time periods

(r .82, p .01 at Week 2; r .83, p .01 at Week 10).

Data Aggregation Issues

Two variables in this study, teamwork behavior and collective

efficacy,

.

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were measured at the individual level and subsequently

aggregated to represent a group-level construct. To justify our

procedure for treating variables measured at the individual level as

having group-level properties, we relied on Chan’s (1998) typology

of composition models.

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Below we explain for each variable

the relevance of Chan’s typology to our data, and the procedures

we used to illustrate the group-level properties of the aggregated

measures.

.

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其實後面還有~"~

還沒po完!!

再麻煩一下下面兩位英文達人了~"~

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Teamwork behaviors. Chan (1998) argued that an additive aggregation

model (using the mean of individual-level variables as a

group-level variable) is appropriate when the theoretical interest is the

magnitude of an effect at the group level. Following Chan,

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we used

the average of individual teamwork behaviors to represent team-level

teamwork behavior. This is because we assumed that the amount of

teamwork behavior exhibited by each individual

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member increases

the collective pool of that behavior. Characterizing a team as high in

teamwork behavior would mean that, taking the team as a unit,

members would display high amounts of teamwork behavior. This

does not imply that all team members display high teamwork behavior,.

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but merely that there are at least some members whose scores

elevate the average for the team (e.g., Neuman & Wright, 1999, p.

384). As suggested by Bliese (2000), we computed intraclass correlations

(ICCs) to determine the reliability of the team-level teamwork

behavior measure.

.

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The ICC(1) coefficient, which represents the degree

of variability in responses at the individual level that is attributed

to team membership, was .30. The ICC(2) coefficient, which represents

.

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the reliability of the team-level means, was .65. These results

support the aggregation of teamwork behavior.

Collective efficacy. According to Chan (1998), a direct consensus

model is a composition model in which individual ratings

of a higher level phenomenon are used

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to represent the higher level

construct. To be theoretically valid, direct consensus models must

contain a suitable degree of within-group agreement (Chan, 1998;

Gibson, Randel & Earley, 2000). The ICC(1)

.

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coefficients for the

collective efficacy measures were .38 at Week 2 and .46 at Week

7, and the ICC(2) coefficients were .71 and .77, respectively.

These results show that collective efficacy displayed an appropriate

degree of within-group agreement relative to between-group

variance,

.

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and thus support aggregation of individual-level data to

the team level (Klein & Kozlowski, 2000).

Results

Table 1 presents descriptive statistics and bivariate correlations

among study variables at the individual and team levels. Each of

.

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the reported correlations is in the expected direction.

Table 1

Descriptive Statistics, Correlations, and Reliability Coefficients

Variables M SD 1 2 3 4 5

Individual-level measures

1. Teamwork self-efficacy 5.23 0.72 (.80)

.

2 個解答

評分
  • 最佳解答

    率,士氣,生產力,品質指標,單位成本的百分比

    女僱員,佔少數民族職工,

    冤情。每項指標都有自己的指標,每隊

    開始模擬在同一點上每一個指標。為了

    例如,士氣指數在一開始的50分可以去的

    高達100。在營業額指數以百分比表示,

    具有較高的百分比反映較差的結果。在10項指標

    人,平均高度相關(r 0.56)。仿真

    創建一個綜合性措施的性能,它可以通過與

    0和100的基礎上,平等的比重10個指標。

    這項措施是向每個團隊,隨著整體

    平均分,在每個星期的決定時期。此方法

    吸引了與會者的注意整體表現的

    人力資源管理部門,而不是性能,只有一兩個

    指標。

    我們指的是綜合性措施,因為第2週開始

    性能和第10週措施,因為球隊的表現。

    除了綜合衡量業績,職級

    下令衡量業績,公開向全體

    球隊在上課時間

    (使用投影儀)在2週

    10。在第2週,這支隊伍的命令,提出措施

    數天前,隊員們被要求評估其

    集體效能知覺。

    這項措施有助於

    形成準確的效果的看法,因為它提供了強大

    社會比較資料,性能比

    總平均(見原木,班杜拉,1989)。

    職級措施

    高度相關的綜合措施,在這兩個時間段

    (註冊商標.82,p .01在第2週與r 0.83,p .01 10週)。

    數據聚合問題

    兩個變量在這項研究中,團隊精神和集體行為

    功效,

    測量在個人層面,隨後

    匯總,以代表小組層面的結構。為了證明我們的

    治療過程測量變量在個人水平

    有組級屬性,我們依靠陳(1998)類型學

    成分模型。

    下面我們解釋為每個變量

    陳的相關性的分類辦法,以我們的數據,程序

    我們用來說明組級屬性的集合

    措施。

    參考資料: me
  • 1 0 年前

    TASA,TAGGAR 和SEIJTS

    比率,士氣,生產力,質量指數,單位成本,百分比

    在女雇員中,少數雇員的百分比,和

    冤情。 每個指標有它自己的索引和每個隊

    在每個指標上在相同的點開始類比。 因為

    例子,士氣索引在一種50的理由開始並且能去作為

    象100一樣高。 營業額索引以百分比表示,

    由於更高的百分比反映出更不夠的結果。 10個指標

    平均非常相關(r。 56). 類比

    建立一個能範圍在之間的性能的選集措施

    基於相等的10個指標的加重,0 和100。

    這個度量標準隊,跟一起總起來

    平均得分,在每個每周決定時期之後。 這種方法

    把性能拉participant s注意給那些罩衫

    HRM部門,而不是性能在只一兩個上

    指標。

    我們稱選集周2個度量標準最初

    作為隊性能的性能和周10個度量標準。

    除性能,一個行列的選集議案之外

    預訂對性能的議案被公開提出對全部

    隊上課時時間

    .

    (使用一台高架式放映機) 在周2

    以及10。 在周2,這職位預訂措施被提出

    在組員被要求評價他們的之前幾天

    集體效力的知覺。

    這度量標準有幫助在裡

    形成準確效力知覺提供堅固

    社會比較比A關於性能的訊息

    總的平均(見木頭和Bandura,1989) .

    職位測量

    用選集非常相關在兩個時間段測量

    (r . 82,便士。 在周2 01點; r . 83,便士。 在周10 01點) .

    數據集中問題

    二變量在這研究,和集體的協作行為內

    效力,

    在個別的水準和後來被測量

    總計描述一組步建造。 證明我們的是正確的

    把在個別的水準被測量的變量當作的程式

    有組水準特性,我們倚賴Chan s ( 1998 ) 類型學

    組成模型。

    (我ㄉ翻譯就到這裡!!!!!!!!!!!!!!!!!!)

    參考資料: Dr.eye
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