scholarly journals Reframing Educational Leadership Research in the Twenty-First Century

2021 ◽  
pp. 107-135
Author(s):  
David NG
Keyword(s):  
Instructional Leadership ◽  
Linear Models ◽  
Social Science Research ◽  
Science Research ◽  
Complexity Science ◽  
The Social ◽  
Adaptive Processes ◽  
Viable Approach ◽  
Leadership Research ◽  
Analytical Tools

AbstractFor the last three decades, reviews on instructional leadership have pointed to studies that predominantly adopted conventional social science research methodologies, specifically analytical tools, such as descriptive, causal factor, correlational and advanced modelling. These methods have constraints and limitations, which include that variable-based linear models measures are treated as ‘rigorously real’ measures of social reality, that individuals use rational deduction (ignoring the value premise of decision-making), and that individuals are treated as independent and individualized. This paper proposes and illustrates how research approaches of complexity science can be applied within the social system to address complex instructional leadership questions. Consequently, reframing instructional leadership research through the lens of complexity science provides the most viable approach to understand the adaptive processes and the dynamic system of schools.

A Tale of Two Cultures

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Social Sciences ◽  
Quantitative Research ◽  
Social Science Research ◽  
Measurement Data ◽  
Science Research ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social ◽  
Scientific Worldview ◽  
Different Cultures

Some in the social sciences argue that the same logic applies to both qualitative and quantitative research methods. This book demonstrates that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. The book identifies and discusses major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, the book also seeks to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. The book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Designing and Evaluating Survey Instruments for Research in Human Issues in Horticulture

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 554c-554
Author(s):  
Sonja M. Skelly ◽  
Jennifer Campbell Bradley
Keyword(s):  
Survey Research ◽  
Survey Methodology ◽  
Human Subjects ◽  
Social Science Research ◽  
Reliability And Validity ◽  
Science Research ◽  
Survey Instrument ◽  
Measurement Tool ◽  
The Social ◽  
Survey Instruments

Survey research has a long precedence of use in the social sciences. With a growing interest in the area of social science research in horticulture, survey methodology needs to be explored. In order to conduct proper and accurate survey research, a valid and reliable instrument must be used. In many cases, however, an existing measurement tool that is designed for specific research variables is unavailable thus, an understanding of how to design and evaluate a survey instrument is necessary. Currently, there are no guidelines in horticulture research for developing survey instruments for use with human subjects. This presents a problem when attempting to compare and reference similar research. This workshop will explore the methodology involved in preparing a survey instrument; topics covered will include defining objectives for the survey, constructing questions, pilot testing the survey, and obtaining reliability and validity information. In addition to these topics some examples will be provided which will illustrate how to complete these steps. At the conclusion of this session a discussion will be initiated for others to share information and experiences dealing with creating survey instruments.

An Introduction to Statistical Models for Networks

2020 ◽  
pp. 218-233
Author(s):  
Valentina Kuskova ◽  
Stanley Wasserman
Keyword(s):  
Social System ◽  
Exponential Family ◽  
Behavioral Science ◽  
Social Science Research ◽  
Science Research ◽  
Uniform Distributions ◽  
The Social ◽  
Special Cases ◽  
Social And Behavioral Science ◽  
Network Methods

Network theoretical and analytic approaches have reached a new level of sophistication in this decade, accompanied by a rapid growth of interest in adopting these approaches in social science research generally. Of course, much social and behavioral science focuses on individuals, but there are often situations where the social environment—the social system—affects individual responses. In these circumstances, to treat individuals as isolated social atoms, a necessary assumption for the application of standard statistical analysis is simply incorrect. Network methods should be part of the theoretical and analytic arsenal available to sociologists. Our focus here will be on the exponential family of random graph distributions, p*, because of its inclusiveness. It includes conditional uniform distributions as special cases.

scholarly journals Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

2021 ◽  
Vol 7 ◽  
pp. 237802312110244
Author(s):  
Katrin Auspurg ◽  
Josef Brüderl
Keyword(s):  
Social Sciences ◽  
Causal Reasoning ◽  
Research Question ◽  
Social Science Research ◽  
Science Research ◽  
Skin Tone ◽  
Data Set ◽  
The Social ◽  
The Many ◽  
Definition Of

In 2018, Silberzahn, Uhlmann, Nosek, and colleagues published an article in which 29 teams analyzed the same research question with the same data: Are soccer referees more likely to give red cards to players with dark skin tone than light skin tone? The results obtained by the teams differed extensively. Many concluded from this widely noted exercise that the social sciences are not rigorous enough to provide definitive answers. In this article, we investigate why results diverged so much. We argue that the main reason was an unclear research question: Teams differed in their interpretation of the research question and therefore used diverse research designs and model specifications. We show by reanalyzing the data that with a clear research question, a precise definition of the parameter of interest, and theory-guided causal reasoning, results vary only within a narrow range. The broad conclusion of our reanalysis is that social science research needs to be more precise in its “estimands” to become credible.

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