the DV and an MV affecting the rela

the DV and an MV affecting the relationship between an IV and the DV. For example, one might hy-
pothesize that in an offi ce situation:

The introduction of a four-day working week (IV) will lead to higher productivity (DV), especially among younger workers (MV).

In this case, there is a differential pattern of relationship between the four-day week and productivity

that results from age differences among the workers. Hence, after introduction of a four-day working

week, the productivity gain for younger workers is higher than that for older workers. It should be

noted that the effect of the moderating or interaction variable is the “surplus” of the combined occur-
rence of introducing a four-day working week and being a younger worker. For example, let’s assume

that the productivity of younger workers is 12 percentage points higher than that for older workers,

and that the productivity of workers having a four-day working week is 6 percentage points higher

than those of workers having a fi ve-day working week. If the productivity of a younger worker having

a four-day working week is only 18 percentage points higher than the productivity of a older worker

with a fi ve-day working week, there is no interaction effect, because the 18 percentage points are the

sum of the main effects. There would be an interaction effect if the productivity of the younger worker

on a four-day week was, say, 25 percentage points higher than the productivity of the older worker on

a fi ve-day week.

Whether a given variable is treated as an independent or moderating variable depends on the hy-
pothesis under investigation. If you were interested in studying the impact of the length of the working

week, you would make the length of week the IV. If you were focusing on the relationship between age

of worker and productivity, you might use working week length as an MV.

Extraneous Variables

An almost infi nite number of extraneous variables (EVs) exists that might conceivably affect a

given relationship. Some can be treated as IVs or MVs, but most must either be assumed or excluded

from the study. Fortunately, an infi nite number of variables has little or no effect on a given situa-
tion. Most can safely be ignored because their impact occurs in such a random fashion as to have

little effect. Others might infl uence the DV, but their effect is not at the core of the problem we in-
vestigate. Still, we want to check whether our results are infl uenced by them. Therefore, we include

them as control variables (CVs) in our investigation to ensure that our results are not biased by not

including them. Taking the example of the effect of the four-day working week again, one would

normally think that weather conditions, the imposition of a local sales tax, the election of a new

mayor, and thousands of similar events and conditions would have little effect on working week and

offi ce productivity.

Extraneous variables can also be confounding variables (CFVs) to our hypothesized IV–DV re-
lationship, similar to moderating variables. You may consider that the kind of work being done might

have an effect on the impact of working week length on offi ce productivity. This might lead you to in-
troducing time spent in a meeting to coordinate the work as a confounding variable (CFV). In our offi ce

example, we would attempt to control for type of work by studying the effect of the four-day working

week within groups attending meetings with different intensity. In Exhibit 3-3b , weather is shown as

an extraneous variable; the broken line indicates that we included it in our research because it might

infl uence the DV, but we consider the CV as irrelevant for the investigation of our research problem.

Similarly we included the type of work as a CFV.

Intervening Variables

The variables mentioned with regard to causal relationships are concrete and clearly measurable—that

is, they can be seen, counted, or observed in some way. Sometimes, however, one may not be com-
pletely satisfi ed by the explanations they give. Thus, while we may recognize that a four-day working

week results in higher productivity, we might think that this is not the whole story—that working

week length affects some intervening variable (IVV) that, in turn, results in higher productivity.

Hasil (Bahasa Indonesia) 1:

DV dan MV mempengaruhi hubungan antara IV dan DV. Sebagai contoh, salah satu mungkin hy-pothesize bahwa dalam keadaan ce offi:Pengenalan seminggu bekerja empat hari (IV) akan menyebabkan produktivitas yang lebih tinggi (DV), terutama di kalangan pekerja muda (MV).Dalam hal ini, ada pola diferensial hubungan antara empat hari seminggu dan produktivitashasil dari perbedaan usia antara para pekerja. Oleh karena itu, setelah pengenalan empat hari kerjaMinggu, gain produktivitas untuk pekerja muda lebih tinggi daripada pekerja yang lebih tua. Itu harusdicatat bahwa efek variabel moderator atau interaksi terjadi "kelebihan" dari gabungan-rence memperkenalkan seminggu bekerja empat hari dan menjadi seorang pekerja muda. Sebagai contoh, mari kita asumsikanproduktivitas pekerja muda yang 12 poin persentase yang lebih tinggi daripada pekerja yang lebih tua,dan bahwa produktivitas pekerja yang mengalami empat hari kerja Minggu 6 poin persentase yang lebih tinggidaripada pekerja yang mengalami fi ve-hari kerja minggu. Jika produktivitas pekerja muda memilikibekerja empat hari Minggu adalah hanya 18 poin persentase yang lebih tinggi daripada produktivitas pekerja anakdengan fi ve-hari kerja minggu, ada tidak ada efek interaksi, karena poin persentase 18jumlah efek utama. Akan ada efek interaksi jika produktivitas pekerja mudapada empat hari Minggu adalah, mengatakan, 25 poin persentase yang lebih tinggi daripada produktivitas pekerja anak dia fi ve-day week.Whether a given variable is treated as an independent or moderating variable depends on the hy-pothesis under investigation. If you were interested in studying the impact of the length of the workingweek, you would make the length of week the IV. If you were focusing on the relationship between ageof worker and productivity, you might use working week length as an MV.Extraneous VariablesAn almost infi nite number of extraneous variables (EVs) exists that might conceivably affect agiven relationship. Some can be treated as IVs or MVs, but most must either be assumed or excludedfrom the study. Fortunately, an infi nite number of variables has little or no effect on a given situa-tion. Most can safely be ignored because their impact occurs in such a random fashion as to havelittle effect. Others might infl uence the DV, but their effect is not at the core of the problem we in-vestigate. Still, we want to check whether our results are infl uenced by them. Therefore, we includethem as control variables (CVs) in our investigation to ensure that our results are not biased by notincluding them. Taking the example of the effect of the four-day working week again, one wouldnormally think that weather conditions, the imposition of a local sales tax, the election of a newmayor, and thousands of similar events and conditions would have little effect on working week andoffi ce productivity.Extraneous variables can also be confounding variables (CFVs) to our hypothesized IV–DV re-lationship, similar to moderating variables. You may consider that the kind of work being done mighthave an effect on the impact of working week length on offi ce productivity. This might lead you to in-troducing time spent in a meeting to coordinate the work as a confounding variable (CFV). In our offi ceexample, we would attempt to control for type of work by studying the effect of the four-day workingweek within groups attending meetings with different intensity. In Exhibit 3-3b , weather is shown asan extraneous variable; the broken line indicates that we included it in our research because it mightinfl uence the DV, but we consider the CV as irrelevant for the investigation of our research problem.Similarly we included the type of work as a CFV.Intervening VariablesThe variables mentioned with regard to causal relationships are concrete and clearly measurable—thatis, they can be seen, counted, or observed in some way. Sometimes, however, one may not be com-pletely satisfi ed by the explanations they give. Thus, while we may recognize that a four-day workingweek results in higher productivity, we might think that this is not the whole story—that workingweek length affects some intervening variable (IVV) that, in turn, results in higher productivity.