M. LOUREL ET AL.

632

Lokou, 1999). whereas there were greater deviations between

the two groups (See Table 1): 33.3% (44.4% - 11.1) for one

(Harris, 1972) compared with 14.2% (42.5 - 28.3) for the others

(op.cit., 1999). Measuring the effect-size merely consists in

translating the deviation and no longer the probability of ob-

taining said devia- tion on the basis of the size of the sample.

Determining Effect-Sizes

The ideal would consist in identifying an indicator that is in-

dependent of the sample size so that the surveys can be com-

pared with each other and, above all, so that we can assess the

effect-size observed. Indeed, sample size sensitivity is one of

the first reasons that led statisticians to work on the concept of

the effect-size.

The contingency coefficient Phi (written

) is one of the in-

dicators used to quantify this significance of deviations between

our two proportions. It is easily applied because we only need

to calculate

2n

(n being the total number of individuals

tested).

Accordingly, if we take the data provided by the two contin-

gency tables above,

equals

72.383280 or 0.15 in the case

of the Guéguen and Fischer-Lokou experiment (1999).

Interpretation

What is the next step once this coefficient

has been calcu-

lated and the effect-size assessed? Cohen (1988) put forward

coefficient values used to measure the effect-size. Three cate-

gories were identified: the low effect (0.1); the medium effect

(.03) and the high effect (0.5)

The effect is qualified as low/medium in the survey [2]. Thus,

therefore, we can be allowed to question the scope of the analy-

sis from the mere statistical significance viewpoint or as an

assessment of the value for ² (the opposite of the importance

obtained).

The Various Effect-Size Indicators

In order to be able to calculate the various indicators used for

measuring effect-size on the basis of the types of variable or of

the analyses carried out, we have separated the headings so that

you can quickly identify the appropriate procedure. We have

used standard indicators for quantifying the effect-size (see

Cohen, 1988 for a more in-depth review). As far as we are

aware, there are many others (e.g. comparison of a mean with a

standard; comparison of 2 means from separate samples; com-

parison of 2 means for linked samples; comparison of frequent-

cies in a contingency table etc.) (see Rosenthal & Rosnow,

1999 for a more in-depth review). In the case of some indica-

tors, it is clear that the effect-size is easily calculated because

the indicator had been produced from earlier analyses (e.g. the

linear correlation coefficient). Furthermore, when the analysis

is performed, most statistics processing software offer options

allowing the user to access these indicators. We should also

note that there are online statistical resources available on the

Internet for calculated these various “effect-sizes” such as:

http://www.uccs.edu/~faculty/lbecker/

http://cognitiveflexibility.org/effectsize/

Conclusions

Measuring effect-size is an approach that the social psycho-

logy researcher must now include, whenever possible, into his

data analysis. This becomes all the more important when exten-

sive samples are used because they encourage the effect to be

revealed even when these are limited. Foreign psychology re-

views and especially the Anglo-Saxon reviews increasingly

tend to demand that these indicators be presented on the same

footing as the various strategic inferential tests used. Some

research used to the meta-analysis computation based an ad-

justed variance and/or upon a pooled variance of effect size.

Berk and Freedman (2003) are skeptical regarding the effect-

tiveness of the meta-analysis. The authors questioned the as-

sumed independence of studies and to randomization for forced

inclusion of studies. It’s a very important problem for scientific

research. Further, the authors are skeptical about the social

dependence (and financial) between the some pool of peer-

review journals and then taken to a subsequent meta-analysis

by the scientific community. For authors: “In the present state

of our science, invoking a formal relationship between random

samples and populations is more likely to obscure than to clar-

ify.”

In this article, we have attempted to present the principle of

this quantification and the way in which customary indicators

are calculated. Obviously, there are presently a great many

indicators that refer to specific analysis cases and that take

various utilisation criteria into account. However, determining

these indicators can help the social psychology researcher to

break free from the classic inferential model used in statistical

analysis and to opt for a method for assessing his data based on

a more equitable assessment of the effects. Many researchers

expose the imperialism of the inferential method and the .05

value as objectionable and recommend that these indicators be

imposed (Thompson, in press). Therefore, if we use these indi-

cators, we would be led to view some of our theoretical analy-

ses and interpretations in a different light.

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