The Accuracy of Data14Although plen

The Accuracy of Data14
Although plenty of data are available for economic research, the quality of
the data is often not that good. There are several reasons for that. First, as
noted, most social science data are nonexperimental in nature. Therefore,
there is the possibility of observational errors, either of omission or commission.
Second, even in experimentally collected data errors of measurement
arise from approximations and roundoffs. Third, in questionnaire-type
surveys, the problem of nonresponse can be serious; a researcher is lucky to get a 40 percent response to a questionnaire. Analysis based on such partial
response may not truly reflect the behavior of the 60 percent who did not
respond, thereby leading to what is known as (sample) selectivity bias. Then
there is the further problem that those who respond to the questionnaire
may not answer all the questions, especially questions of financially sensitive
nature, thus leading to additional selectivity bias. Fourth, the sampling
methods used in obtaining the data may vary so widely that it is often difficult
to compare the results obtained from the various samples. Fifth, economic
data are generally available at a highly aggregate level. For example,
most macrodata (e.g., GNP, employment, inflation, unemployment) are
available for the economy as a whole or at the most for some broad geographical
regions. Such highly aggregated data may not tell us much about
the individual or microunits that may be the ultimate object of study. Sixth,
because of confidentiality, certain data can be published only in highly
aggregate form. The IRS, for example, is not allowed by law to disclose data
on individual tax returns; it can only release some broad summary data.
Therefore, if one wants to find out how much individuals with a certain level
of income spent on health care, one cannot do that analysis except at a very
highly aggregate level. But such macroanalysis often fails to reveal the
dynamics of the behavior of the microunits. Similarly, the Department of
Commerce, which conducts the census of business every 5 years, is not
allowed to disclose information on production, employment, energy consumption,
research and development expenditure, etc., at the firm level. It is
therefore difficult to study the interfirm differences on these items.
Because of all these and many other problems, the researcher should
always keep in mind that the results of research are only as good as the
quality of the data. Therefore, if in given situations researchers find that the
results of the research are “unsatisfactory,” the cause may be not that they
used the wrong model but that the quality of the data was poor. Unfortunately,
because of the nonexperimental nature of the data used in most social
science studies, researchers very often have no choice but to depend on the
available data. But they should always keep in mind that the data used may
not be the best and should try not to be too dogmatic about the results obtained
from a given study, especially when the quality of the data is suspect.

Hasil (Bahasa Indonesia) 1:

Keakuratan Data14Meskipun banyak data tersedia untuk penelitian ekonomi, kualitasdata ini sering tidak baik. Ada beberapa alasan untuk itu. Pertama, sebagaidicatat, sebagian data ilmu sosial nonexperimental di alam. Oleh karena itu,ada kemungkinan kesalahan observasi, salah satu dari kelalaian atau komisi.Kedua, bahkan dalam kesalahan eksperimental dikumpulkan data pengukuranmuncul dari pendekatan dan roundoffs. Ketiga, dalam kuesioner-jenissurvei, masalah nonresponse dapat menjadi serius; seorang peneliti beruntung untuk mendapatkan respon 40 persen untuk kuesioner. Analisis berdasarkan parsial sepertirespons mungkin tidak benar-benar mencerminkan perilaku 60 persen yang tidakmenanggapi, sehingga menyebabkan apa yang dikenal sebagai bias selektivitas (contoh). Kemudianada masalah lebih lanjut bahwa mereka yang menjawab kuesionermungkin tidak menjawab semua pertanyaan, khususnya pertanyaan finansial sensitifalam, sehingga mengarah ke tambahan selektivitas bias. Keempat, samplingmetode yang digunakan dalam memperoleh data dapat bervariasi begitu banyak bahwa hal ini sering sulituntuk membandingkan hasil yang Diperoleh dari berbagai sampel. Kelima, ekonomidata umumnya tersedia pada tingkat yang sangat agregat. Sebagai contoh,yang paling macrodata (misalnya, GNP, Ketenagakerjaan, inflasi, pengangguran)tersedia bagi perekonomian secara keseluruhan atau sebagian untuk beberapa luas geografisdaerah. Data sangat gabungan tersebut mungkin tidak memberitahu kita banyak tentangthe individual or microunits that may be the ultimate object of study. Sixth,because of confidentiality, certain data can be published only in highlyaggregate form. The IRS, for example, is not allowed by law to disclose dataon individual tax returns; it can only release some broad summary data.Therefore, if one wants to find out how much individuals with a certain levelof income spent on health care, one cannot do that analysis except at a veryhighly aggregate level. But such macroanalysis often fails to reveal thedynamics of the behavior of the microunits. Similarly, the Department ofCommerce, which conducts the census of business every 5 years, is notallowed to disclose information on production, employment, energy consumption,research and development expenditure, etc., at the firm level. It istherefore difficult to study the interfirm differences on these items.Because of all these and many other problems, the researcher shouldalways keep in mind that the results of research are only as good as thequality of the data. Therefore, if in given situations researchers find that theresults of the research are “unsatisfactory,” the cause may be not that theyused the wrong model but that the quality of the data was poor. Unfortunately,because of the nonexperimental nature of the data used in most socialscience studies, researchers very often have no choice but to depend on theavailable data. But they should always keep in mind that the data used maynot be the best and should try not to be too dogmatic about the results obtainedfrom a given study, especially when the quality of the data is suspect.