Neuroscience is a fascinating discipline – its dynamic progress has led to the emergence of new interdisciplinary research programmes with great potential. One of these research areas is neuroeconomics. As will be shown in this article, this discipline, which is diffi cult to clearly characterize and defi ne, is faced with many problems. Th is paper argues that social scientists should be interested in the problems and tendencies in social neuroscience for several reasons. Neuroeconomics, and other disciplines inspired by neuroscience, will compete with their parent disciplines in many fi elds of interest. On one hand it will be necessary for scientists to defi ne and defend the irreplaceable roles of their disciplines, but also critically evaluate the potential of new approaches on the other. In the context of this discussion, which reopens questions about the scientifi c status of economics and its roles, this paper introduces the main problems related to neuroeconomics. Th is paper concludes that these problems represent a wide domain for social scientists and methodologists of science. and Neurověda je fascinující disciplínou – její dynamický rozvoj podněcuje vznik nových interdisciplinárních výzkumných programů s velkým potenciálem. Jednou takovou oblastí je i neuroekonomie. Jak se ukáže v článku, tato disciplína, kterou je obtížné jednoznačně vymezit a určit její defi nici, se potýká se spoustou problémů. Článek y jj fi argumentuje, že by se společenští vědci měli těmito problémy a tendencemi v sociální neurovědě zabývat, a to hned z několika důvodů. Neuroekonomie, a také další neurovědou inspirované disciplíny, budou svým mateřským oborům konkurovat v mnoha oblastech, přičemž bude nezbytné, aby vědci byli schopni na jedné straně defi novat a obhájit nezastupitelné role svých disciplín, na straně druhé kriticky vyhodnocovat potenciál nových přístupů. V kontextu této diskuze, která znovu otevírá otázky ohledně vědeckého statusu ekonomie a jejích rolí, článek vymezuje základní problémy, s nimiž se neuroekonomie potýká. Práce dospívá k závěru, že tyto problémy představují široké pole působnosti pro společenské vědce a metodology vědy.
Příspěvek upozorňuje na metodologický přístup autorů k vlastnímu výzkumu. V sedmi vybraných českých časopisech (6 recenzovaných za obor kinantropologie a jeden s IF /impakt faktorem/ za obor psychologie) byla posuzována správnost postupu zjišťování statistické významnosti pomocí testování nulové hypotézy. Výsledky ukazují na závažný nedostatek při užití tohoto statistického nástroje – randomizace výzkumného souboru. Je zřejmá i chybná interpretace pojmů „hladina statistické významnosti“ a „chyba I. druhu“, což lze přičíst nejasné koncepci celého postupu testování statistické významnosti. U převážné většiny studií, které užívají tento statistický nástroj, jsou vyslovené závěry opřeny o chybnou metodiku a jejich průkaznost je tedy nízká. Autoři zdůrazňují přístup redakcí odborných časopisů ke kvalitě příspěvků (požadavky na metodickou a diskusní část, posun kritérií recenzní činnosti na světovou úroveň). and Problems of research sample and testing the null hypothesis
The paper points to the methodological approach of authors to the research. In seven selected Czech journals (6 peer-reviewed kinanthropological ones and one psychological journal with an impact factor), the statistical testing of null hypothesis was assessed. The results show a serious defect in fulfillment the condition for the use of this statistical instrument – the randomization of the sample. The incorrect interpretation of concepts „level of statistical significance“ and „type one error“ is evident being the result of unclear conception of statistical significance testing. In many studies using this statistical instrument the results are based on wrong methodology and their conclusiveness is thus low. The authors emphasize the approach of editorial boards of scientific journals to the quality of manuscripts: requirements concerning the „method“ and „discussion“ parts, and shifting review criteria to the worldwide accepted level.
The aim of this article is to present a trend in research on measurement error in survey data and to suggest some problematic aspects of this approach. The article describes the Multitrait Multimethod experimental design and its modifi cation into a 2 Split-ballot Multitrait Multimethod (2 SB MTMM), which is used for experimental data collection in the European Social Survey. The text shows how to analyze 2 SB MTMM data to obtain estimates of construct validity, reliability and common method variance for a single questionnaire item, and how to make use of these estimates. It also points to some problems encountered in 2 SB MTMM data analysis., Johana Chylíková., and Obsahuje bibliografii
This article will introduce Isaac Newton’s fundamental methodological concepts applied for a solution concerning a question of gravity in his Principia Mathematica. The method of deduction of propositions from phenomena can be described as demonstrative induction. The main aim is to show that this method proposed by Newton explicitly contains a criticism of hypothetico-deductive methodology as an inadequate approach to the study of nature. As opposed to hypothetico-deductive method, demonstrative induction is capable of producing theories with much richer empirical and epistemological value. Delimitation against hypothetically deduced theories is closely connected with criticism of mechanical materialism, most notably in a form proposed by René Descartes. Consequently, it has led Newton to not only reject the universality of mechanical premise but also to certain level of immunization against all competitive hypothetically based theories. and V tomto článku budou představeny základní aspekty metodologie Isaaca Newtona aplikované v Principia Mathematica na řešení otázky povahy gravitační síly. Metoda vyvozování propozic přírodních jevů může být označena jako demonstrativní indukce. Cílem článku je ukázat, že tato metoda, jak ji zastával Newton, v sobě explicitně obsahovala kritiku hypoteticko-deduktivní metody jako zcela neadekvátního přístupu ke zkoumání přírody. Oproti hypoteticko-deduktivní metodě je demonstrativní indukce schopna produkovat teorie empiricky i epistemologicky mnohem bohatší. Vymezení se vůči hypoteticky založeným teoriím je úzce spjato s kritikou mechanického materialismu především v podobě, v jaké ji zastával René Descartes. Ve svých důsledcích to Newtona dovedlo nejen k odmítnutí univerzálnosti mechanické premisy, ale také k určité imunizaci vůči veškerým konkurenčním hypoteticky založeným teoriím.
The present paper deals with the Brno Social Study, a rather extraordinary questionnaire survey given its extent and time (1947). Data analysis was forestalled by the political transformation after 1948, but the questionnaires were preserved. We have inherited a unique set of data for a historical-sociological analysis focusing both on the population of industry workers and on the social structure of Czech society in the advent of the communist coup. The Brno Social Study is contextualized in the state of post-war sociology, and the avenues toward its inception and implementation are mapped. The central part of the paper analyses the survey data from a contemporary analytical perspective, discussing the dataset’s representativeness. The primary objective of the paper is to propose, and initiate scholarly debate about, a feasible methodology for analysing the archived data today. The methodology serves to construct a representative sample through a combination of purposive, quota and random sampling; to determine the respondents’ socio-economic status using both ISCO and an original conceptualization of working class status; and to present certain data on respondents’ lifestyles that might be of interest for future analyses., Dušan Janák, Martin Stanoev a Petr Hušek., and Obsahuje bibliografii
This contribution, which in a brief, succint and almost aphoristic way, critically brings forward to the reader a number of problems of today’s corpus and computational linguistics as well as their unsatisfactory solutions, is trying, at the same time, to do away with a number of myths and simplified opinions in the field. and Příspěvek ve stručné a téměř aforizované podobě připomíná řadu kritizovaných problémů a jejich neuspokojivých řešení v dnešní korpusové a komputační lingvistice a snaží se tak odstranit řadu mýtů a zjednodušujících představ.
The goal of this article is to inform social scientists, especially those of a quantitative orientation, about the basic characteristics of Big Data and to present the opportunities and limitations of using such data in social research. The paper informs about three basic types of Big Data as they are distinguished in contemporary methodological literature, namely administrative data, transaction data and social network data, and exemplifies how they can be utilized by quantitative social research. According to many, questionnaire-based sample survey as the dominant method of quantitative social research has found itself in a crisis, especially as response rates have decreased in most developed countries and public confidence in opinion polling has declined. The author presents the characteristics and specifics of Big Data compared to survey research - a method whose primary distinguishing characteristic is the capacity to quantify individual behaviour, social action and attitudes at the level of populations. In this context, the article draws attention to the differences between Big Data and survey data typically presented in scholarly literature, namely that datasets are not representative of known populations, the values of observed variables are systematically biased, there is a limited number of variables in Big Data sets, there is uncertainty about the meaning of observed values, and social environment has direct influence on the behaviours captured by Big Data. Attention is also paid to such characteristics of Big Data that pose an obstacle to smooth integration of this type of data in the social scientific mainstream. First, the collection, processing and analysis of Big Data is extremely demanding in terms of programming skills, something social scientists typically do not have. Second, the availability of Big Data is limited as they are normally possessed by private corporations, some of which (Facebook, Google) have undoubtedly come to form data oligopolies - and their management is mostly unwilling to share their data with traditional academics. Based on the above-mentioned specifics, differences and limitations, it is argued that Big Data currently do not have the potential of becoming a full-fledged source of social science data and replacing sample surveys as the dominant research method. Finally, the article draws attention to the specifics of different types of Big Data as they are primarily generated for purposes other than social research and result from specific situations framed by existing social relations - and it is from this perspective that Big Data should be viewed by social researchers., Johana Chylíková., and Obsahuje bibliografické odkazy