A New Lens on High School Dropout: Use of Correspondence Analysis and the Statewide Longitudinal Data System

Ziemer, Kathryn Schaefer; Pires, Bianica; Lancaster, Vicki; Keller‐McNulty, Sallie; Orr, Mark; Shipp, Stephanie

doi:10.1080/00031305.2017.1322002

Cited by 3 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data sources : Kentucky Statewide Longitudinal Data Systems (school years: 2008–2014), and geographic boundaries of Appalachia, defined by the Appalachian Regional Commission ( https://www.arc.gov/appalachian_region/TheAppalachianRegion.asp). Reprinted with permission from Ziemer et al ()…”

Section: Case Study I: Why Do Students Drop Out Of High School?mentioning

confidence: 99%

“…The boundaries of the two geographic regions are defined by the Appalachian Regional Commission (procedural data). Using the geographic-based student SLDS data, we constructed an explanatory model to explore dropout reasons in the two regions (Ziemer et al, 2017). Student-level data from the SLDS included gender, race and ethnicity, grade, dropout status, dropout reasons, and county.…”

Section: Case Study I: Why Do Students Drop Out Of High School?mentioning

confidence: 99%

“…Data sources: Kentucky Statewide Longitudinal Data Systems (school years: 2008-2014), and geographic boundaries of Appalachia, defined by the Appalachian Regional Commission (https://www.arc.gov/appalachian_region/TheAppalachianRegion.asp). Reprinted with permission fromZiemer et al (2017) House values in Arlington,Virginia. The left map shows median house values based on American Community Survey, and individual local property tax assessment values for the area highlighted by the yellow circle is illustrated on the right map.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Harnessing the power of data to support community‐based research

Keller‐McNulty

Shipp

Korkmaz

et al. 2018

WIREs Computational Stats

Self Cite

View full text Add to dashboard Cite

Volumes of data are generated at every moment as we go through the paces of our daily lives. Many of these data flows are routinely captured through administrative records, social media, and surveys. Historically, agencies at different levels of government have been responsible for curating and reporting statistics about our social, economic, and health conditions associated with these data flows. Recently, the U.S. government has proposed the use of data derived from administrative records at the federal level to support social policy and program evaluation. Why not consider parallel activities at state and local levels? Harnessing local data sources and integrating them with state and federal sources will provide timelier and more geographically specific analyses to support local insights and policy development. Leveraging community‐based participatory research, researchers and civic leaders can work together to identify the questions and execute rigorous, yet flexible, processes for building local sustainable community learning cultures based on data‐driven discovery. In the process of conducting research with local civic leaders, we have observed that issues can be classified into 3 categories: locating and describing a population within a community; estimating a statistic and a measure of variability; and evaluating a program, policy, or procedure. Through a series of case studies, this paper demonstrates the unexpected value in liberating and repurposing local data. This article is categorized under: Applications of Computational Statistics > Organizations and Publications

show abstract

Section: Case Study I: Why Do Students Drop Out Of High School?mentioning

confidence: 99%

Section: Case Study I: Why Do Students Drop Out Of High School?mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Harnessing the power of data to support community‐based research

Keller‐McNulty

Shipp

Korkmaz

et al. 2018

WIREs Computational Stats

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, the problems caused by network security are getting more and more attention. Therefore, network logs that record network security and user events have become more and more important [3][4] . Because the information in the log file allows us to find out the reasons for incident handling, network intrusion systems, system resource monitoring, and helping to restore the system, it is of great value [4] .…”

Section: Introductionmentioning

confidence: 99%

Design of Log Analysis System Based on Multi-Object Clustering Algorithm

Hao

Gao

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the rapid development of the information age and the continuous expansion of data, all walks of life have begun to collect and analyze massive amounts of data to extract information that is valuable to them. Clustering technology in data mining is an important means of data analysis. Because different data sets have different distribution characteristics, traditional single-object clustering cannot adapt to the effective processing of different data sets, so multi-object clustering has gradually become a research hotspot. The purpose of this paper is the design of log analysis system based on a multi-objective clustering algorithm. This article first determines the main goals of the log analysis system and performs a detailed analysis of the system’s demanding functional analysis and demanding non-functionality respectively. The log analysis system is mainly divided into five modules for the detailed design of the log analysis system. After the system design is completed, various functional modules are completed through corresponding technologies, the entire log system is tested for attacks, and the system is further improved. Finally, the system can completely analyze the attack type through log analysis and carry out relevant early warnings to meet the needs of users. When the data set is 1.8MB, the convergence time of the K-medoid algorithm is 3678.49, and the convergence time of the algorithm in this paper is 2536.42. When the data reaches 165MB, the convergence time of the algorithm in this paper is 4326.28, and the convergence time of the K-medoid algorithm is 8184. This shows that as the scale of data continues to increase, the processing time difference changes and the processing speed of this algorithm has obvious advantages.

show abstract

“…A graphical representation of the results known as the Correspondence Analysis (CA) proves to be very useful in this kind of study [11]. Recently, CA was applied for studies on a variety of problems, for example, on high school dropouts [12], and also in archeology [13], in food science [14], etc. In the CA the information about the whole system is stored on maps in which objects under consideration are represented by points located in a specific way.…”

Section: Introductionmentioning

confidence: 99%

Classification Maps in Studies on the Retirement Threshold

2020

View full text Add to dashboard Cite

The aim of this work is to present new classification maps in health informatics and to show that they are useful in data analysis. A statistical method, correspondence analysis, has been applied for obtaining these maps. This approach has been applied to studies on expectations and worries related to the retirement threshold. For this purpose two questionnaires formulated by ourselves have been constructed. Groups of individuals and their answers to particular questions are represented by points in the classification maps. The distribution of these points reflects psychological attitudes of the considered population. In particular, we compared structures of the maps searching for factors such as gender, marital status, kind of work, economic situation, and intellectual activity related to the attendance the University of the Third Age, which are essential at the retirement threshold. Generally, in Polish society, retirement is evaluated as a positive experience and the majority of retirees do not want to return to their professional work. This result is independent of the kind of work and of the gender.

show abstract

A New Lens on High School Dropout: Use of Correspondence Analysis and the Statewide Longitudinal Data System

Cited by 3 publications

References 21 publications

Harnessing the power of data to support community‐based research

Harnessing the power of data to support community‐based research

Design of Log Analysis System Based on Multi-Object Clustering Algorithm

Classification Maps in Studies on the Retirement Threshold

Contact Info

Product

Resources

About