2.5 Kammalawa
Big data ne a ko'ina, amma yin amfani da shi da kuma sauran siffofin observational data for zamantakewa bincike da wuya. A na kwarewa akwai wani abu kamar wani free abincin rana dukiya domin gudanar da bincike, idan ba ka sanya a cikin wani yawa aiki tattara bayanai, to, ka yiwuwa za a yi a saka a cikin mai yawa aiki nazarin your data ko a tunanin abin da yake a cikin wani m tambaya da bayanai. Bisa ga ideas a wannan babi, na yi tunanin cewa akwai uku main hanyoyi da cewa babban data kafofin zai zama mafi muhimmanci ga al'umma da bincike:
- empirically adjudicating tsakanin gasar ka'idojin tsinkaya. Misalan irin wannan aiki sun hada da Farber (2015) (New York Taxi direbobi) da kuma King, Pan, and Roberts (2013) (idon a kasar Sin)
- inganta zamantakewa ji for siyasa ta nowcasting. Wani misali na irin wannan aiki ne Ginsberg et al. (2009) (Google Mura Trends).
- kimantawa causal effects da na halitta gwaje-gwajen da matching. Misalan irin wannan aiki. Mas and Moretti (2009) (sa'a effects on aiki) da kuma Einav et al. (2015) (sakamako na farawa price a auctions a eBay).
Mutane da yawa da muhimmanci da tambayoyi a social bincike za a iya bayyana a matsayin daya daga cikin wadannan uku. Duk da haka, wadannan hanyoyin kullum bukatar bincike ya zo da wata yawa ga bayanai. Abin da ke sa Farber (2015) mai ban sha'awa shi ne msar tambayar dalili ga ji. Wannan msar tambayar dalili ya zo daga waje da bayanai. Saboda haka, ga waɗanda suka yi kyau a tambayar wani iri bincike tambayoyi, babban data kafofin iya zama sosai hayayyafa.
A karshe, maimakon ka'idar-kore empirical bincike (wanda ya mayar da hankali a kan wannan babi), za mu iya jefa cikin script da kuma haifar da empirically-kore theorizing. Wannan ne, ta hanyar mai da hankali jari na empirical facts, alamu, da wasanin gwada ilimi, za mu iya gina sabon theories.
Wannan madadin, data-farko tsarin kula da ka'idar ba sabon, kuma aka fi tsananin bugãwa da alaƙa da Glaser and Strauss (1967) da kira ga grounded ka'idar. Wannan data-farko m, duk da haka, ba ya nufa "karshen ka'idar," kamar yadda aka yi iƙirarin da yawa daga cikin aikin jarida a kusa da bincike a cikin digital shekaru (Anderson 2008) . Maimakon haka, kamar yadda data yanayi canje-canje, dole ne mu sa ran wani sake daidaita a dangantaka tsakanin ka'idar da bayanai. A cikin wata duniya inda data tarin yana da tsada, shi ya sa hankali kawai tattara bayanai da bayar da shawarar theories zai zama mafi amfani. Amma, a cikin duniya, inda babban yawa na data riga available for free, shi ya sa hankali ga ma kokarin data-farko m (Goldberg 2015) .
Kamar yadda na nuna a cikin wannan sura, masu bincike za su iya koya da yawa ta hanyar kallon mutane. A na gaba uku da surori, zan bayyana yadda za mu iya koyi da daban-daban abubuwa idan muna tela mu data tarin da kuma hulɗa tare da mutanen da more kai tsaye da tambayar su tambayoyi (Babi na 3), yanã gudãna gwajen (Babi na 4), har ma shafe su a gudanar da bincike tsari kai tsaye (Babi na 5).
