PERCEPTIONAL DIFFERENCES IN ARCHITECTURAL FACADE PERCEPTION DUE TO ARCHITECTURAL EDUCATION

2021 ◽  
Vol 0 (23) ◽  
pp. 0-0
Author(s):  
Emine YILDIZ KUYRUKÇU ◽  
Tuğba ÖZDEMİR ERDOĞAN
Keyword(s):  
Social Sciences ◽  
Software Package ◽  
Architectural Education ◽  
Data Sets ◽  
Behavioral Performance ◽  
Statistical Software ◽  
The Social ◽  
Package Program ◽  
Semantic Differentiation ◽  
Differentiation Scale

Aim: In this study, the façades of tourism buildings designed with different currents (universalism, regionalism, syncreticism, contextualism, neovernakularism) on the Antal-ya coastline, in terms of 'taste', 'chaos', 'affection', 'familiarity', by groups of architects and non-architects. It is aimed to examine how it is perceived. Method: For this purpo-se, the fronts of 20 tourism buildings from 5 different styles, designed with a modern and postmodern approach, were analyzed by 60 people through a questionnaire. In the survey, adjective pairs such as impressive / ordinary, original / imitation, coarse / elegant, modest / flamboyant, complex / plain, familiar / unfamiliar, modern / outdated, questio-ning the façade features for tourism buildings were evaluated with a five-digit semantic differentiation scale. The analysis of the data sets obtained through the questionnaire was performed with the IBM Statistical Package For The Social Sciences (SPSS) 23 For Windows statistical software package program. At the beginning of the study, it was thought that the subjects would have perception-behavioral performance differences depending on the architectural education. Results: Unlike the non-architect profession, the architect group liked the universalism, neo-natalism and regionalism movements, found it impressive and original; It was determined that he did not like the synchterism and contextualism movements as complex, rude and outdated. Conclusion: In the analysis, it was seen that the differences between architects and non-architects are statis-tically significant. As a result of the study, it was determined that there are significant differences between architects and non-architects in evaluating the adjectives of expres-siveness, complexity, familiarity and originality. İndividuals who do not have an archi-tectural education are familiar with the traditional inspired structures, and they find the-se structures impressive It is for the individuals who study architecture to find modern and regionalist structures impressive and original.

scholarly journals PSYCHOMETRIC PROPERTIES OF TEN ITEM PERSONALITY IVENTORY (TIPI)

2016 ◽  
Vol 1 (2) ◽  
pp. 115 ◽  
Author(s):  
Sonia Brito-Costa ◽  
Ana Moisão ◽  
Hugo De Almeida ◽  
Florencio Vicente Castro
Keyword(s):  
Social Sciences ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Psychometric Properties ◽  
Software Package ◽  
Factor Model ◽  
Elite Athletes ◽  
Five Factor Model ◽  
Statistical Software ◽  
The Social

This study based on the Five Factor Model of Costa and McCrae (1987) sought to determine theninternal consistency and the psychometric properties of the Ten Item Personality Inventory (TIPI) of Gosling, Rentfrow, & Swann (2003), Lima and Castro (2009) Portuguese version. The sample consisted of 170 male soccer athletes whose average age stood at 18.50 years, with a minimum of 13 and a maximum of 33 years. Statistical analysis was performed by the Statistical Software Package for the Social Sciences (SPSS) in its 19 version for Windows. The TIPI showed low internal consistency (=0.462) and factor analysis that meets the criteria postulated by the instrument authors, so considered it valid to evaluate the personality rapidly in samples with little time available, for example elite athletes.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

On the Number and Nature of Common Factors Extracted by the Eigenvalue-One Rule Using BMDP VS SPSSX

1989 ◽  
Vol 65 (1) ◽  
pp. 155-160 ◽  
Author(s):  
Raymond Hubbard ◽  
Stuart J. Allen
Keyword(s):  
Factor Analysis ◽  
Empirical Data ◽  
Software Package ◽  
Common Factor ◽  
Common Factors ◽  
Computer Programs ◽  
Data Sets ◽  
Statistical Software ◽  
Common Factor Analysis ◽  
Psychology Literature

Given nuances in the computer programs, unwary researchers performing a common factor analysis on the same set of data can be expected to arrive at very different conclusions regarding the number and nature of extracted factors if they use the BMDP, as opposed to the SPSSx (or SAS), statistical software package. This is illustrated using six well-known empirical data sets from the psychology literature.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA

2016 ◽  
Vol 46 (2) ◽  
pp. 242-251 ◽  
Author(s):  
Bear F. Braumoeller
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Qualitative Comparative Analysis ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Aggregation Bias ◽  
Data Sets ◽  
The Social ◽  
Necessary And Sufficient ◽  
Causal Complexity

Fuzzy-set qualitative comparative analysis (fsQCA) has become one of the most prominent methods in the social sciences for capturing causal complexity, especially for scholars with small- and medium- N data sets. This research note explores two key assumptions in fsQCA’s methodology for testing for necessary and sufficient conditions—the cumulation assumption and the triangular data assumption—and argues that, in combination, they produce a form of aggregation bias that has not been recognized in the fsQCA literature. It also offers a straightforward test to help researchers answer the question of whether their findings are plausibly the result of aggregation bias.

scholarly journals Restructuring the Social Sciences: Reflections from Harvard's Institute for Quantitative Social Science

2013 ◽  
Vol 47 (01) ◽  
pp. 165-172 ◽  
Author(s):  
Gary King
Keyword(s):  
Social Sciences ◽  
Public Policy ◽  
Social Science ◽  
Sparse Data ◽  
Data Sets ◽  
Research Teams ◽  
Academic Fields ◽  
The Social ◽  
New Type ◽  
Sparse Data Sets

AbstractThe social sciences are undergoing a dramatic transformation from studying problems to solving them; from making do with a small number of sparse data sets to analyzing increasing quantities of diverse, highly informative data; from isolated scholars toiling away on their own to larger scale, collaborative, interdisciplinary, lab-style research teams; and from a purely academic pursuit focused inward to having a major impact on public policy, commerce and industry, other academic fields, and some of the major problems that affect individuals and societies. In the midst of all this productive chaos, we have been building the Institute for Quantitative Social Science at Harvard, a new type of center intended to help foster and respond to these broader developments. We offer here some suggestions from our experiences for the increasing number of other universities that have begun to build similar institutions and for how we might work together to advance social science more generally.

Application of a statistical software package for analysis of large patient dose data sets obtained from RIS

2010 ◽  
Vol 139 (1-3) ◽  
pp. 455-458 ◽  
Author(s):  
J. Fazakerley ◽  
P. Charnock ◽  
R. Wilde ◽  
R. Jones ◽  
M. Ward
Keyword(s):  
Software Package ◽  
Patient Dose ◽  
Data Sets ◽  
Statistical Software ◽  
Large Patient
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