User:Mariam Shadad/Disaggregated Data

Disgaregated Data is data which has been separated into smaller categories in order to better assess its components and their collective effects. Disaggregateddata is effective due to its ability to "reveal deprivations and inequalities that may not be fully reflected in aggregated data". Contrastingly, aggregate data consists of data compiled into a single source to better track general patterns, a common example would be of standardized testing.

Disagrgegate vs Agregate data

Aggregate data is commonly used within meta-analyses. This refers to the process of "combining aggregate data (AgD) results from publications. As such, meta-regression most commonly consists of conducting linear regression of the study results as a function of an effect modifier, both in the aggregate". The main difference between the two forms of data collection is that one aims to disseminate information (disaggregate) and the other (aggregate), strives to combine it. The uses of these datum will differ dependent upon study and theoretical perspectives, as well as research goals and methods.

Background

Disaggregate data is a concept found within Critical Data Studies and the study of Big Data. Disaggregated data has found its home within many fields of study within CDS. The application of disaggregated Data ranges from policy, women's studies, as well as schools. This form of data collection has historically been used to further understand trends and patterns that differ from the general collections of the populace. This data requires a dissection of big data; by minimizing big data into smaller datum, the span of information may be studied closely. This is commonly noted within forms of standardized testing, race and general theories, as well as modes of statistics. Disaggregated Data looks at the smaller picture of a larger concept, it can be utilized in any study that has multiple variables. Although the term 'disaggregated data' is relatively new, its origin, aggregate data, is well popularized. Discrete data is therefore an opposite of aggregate data, both of which may be employed to represent figures.

Due to its variability, the usage of dissaggregate data in studies is expansive. Big data is essential to the functioning of the modern society; understanding the impact of these datum on individuals, institutions, and individuals is necessary to disseminating data, and further applying it.

Theories and connections:

1:Michael Quinn Patton's Utilization-Focused Evaluation (Possibly will look at this further, may also look at theories upon consult with proefssor)

2. Race theories (specifically, how dividing data (on a large scale) can contribute to this study)

3.Feminist theoriey (how broken-down big data can contribute to this field)

4:Critical disability studies (Looking at how breaking down big data regarding disabilities can further aid this group by showcasing individuals and how they are affected on smaller scales that may be overlooked)

5. Standardized testing (this can be specifically looked at as it can relate to race theory, feminist theories as well as critical disability theories)

6: Data warehouse: This is a form of aggregate data as it refers to " a centralized storage system that allows for the storing, analyzing, and interpreting of data in order to facilitate better decision-making". I can look at the benefits of dividing the stored data (for businesses and other institutions), and how the division process allows for access to specific data and anomalies. I will further look into the specifics of data mining.

7:Data mining: Refers to the 'mining' of data to find subjects which either create patterns, or stand out. This concept can be looked at further in relation to how it contributes to data mining + data warehousing. These 3 topicsa seem to be further connected/ may introduce one to the other.

Limitations of Dissagregated Data

---Willl be updated at the end of this study and upon further consultation