Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Data analysis experiences: self-produced vs. Alien Data
Date
2017-01-14
Author
Sevinç, Şerife
Metadata
Show full item record
Item Usage Stats
195
views
0
downloads
Cite This
Data analysis is one of the core phases of qualitative research. This presentation aimed to share data analysis experiences of a team of researchers in a mathematics education study using three- tiered modeling research as a methodologic orientation. This study had three tiers: Tier 1-Pre-service mathematics teachers, Tier 2- A team of researchers, and Tier 3-Principal researcher. The team of researchers in Tier 2 composed of four researchers having different interests and teaching experiences as well as different knowledge of and skills in qualitative inquiry, which brought different perspectives to the data analysis. Furthermore, their researcher positions vary on the insider-outsider continuum with respect to the data. While one researcher collected the data, two of them produced the data which was going to be analyzed. On the other hand, the data was alien to the fourth researcher; that is, his first interaction with the data was at analysis process. In this study, I will present the experiences these researchers by addressing the following research question: “How is it different to analyze your own data, the data you collected and an alien data that you just saw at the analysis phase?” Both advantages and disadvantages of being various proximities to the data are going to be discussed and exemplified with researchers’ narratives. I believe that it is important to discuss individual and collaborative data analysis experiences of researchers having various positions and interactions with the data in order to enhance our understanding of researchers’ caring each other in a qualitative research.
URI
http://nsuworks.nova.edu/tqrc/eighth/day2/12/
https://hdl.handle.net/11511/75013
Conference Name
The Qualitative Report Eighth Annual Conference, (12 - 14 Ocak 2017)
Collections
Department of Mathematics and Science Education, Conference / Seminar
Suggestions
OpenMETU
Core
Continuous optimization applied in MARS for modern applications in finance, science and technology
Taylan, Pakize; Weber, Gerhard Wilhelm; Yerlikaya, Fatma (2008-05-23)
Multivariate adaptive regression spline (MARS) denotes a tool from statistics, important in classification and regression, with applicability in many areas of finance, science and technology. It is very useful in high dimensions and shows a great promise for fitting nonlinear multivariate functions. The MARS algorithm for estimating the model function consists of two subalgorithms. We propose not to use the second one (backward stepwise algorithm), but we construct a penalized residual sum of squares for a ...
PROGRESSIVE CLUSTERING OF MANIFOLD-MODELED DATA BASED ON TANGENT SPACE VARIATIONS
Gokdogan, Gokhan; Vural, Elif (2017-09-28)
An important research topic of the recent years has been to understand and analyze manifold-modeled data for clustering and classification applications. Most clustering methods developed for data of non-linear and low-dimensional structure are based on local linearity assumptions. However, clustering algorithms based on locally linear representations can tolerate difficult sampling conditions only to some extent, and may fail for scarcely sampled data manifolds or at high-curvature regions. In this paper, w...
Multiresolution analysis of S&P500 time series
KILIC, Deniz Kenan; Uğur, Ömür (2018-01-01)
Time series analysis is an essential research area for those who are dealing with scientific and engineering problems. The main objective, in general, is to understand the underlying characteristics of selected time series by using the time as well as the frequency domain analysis. Then one can make a prediction for desired system to forecast ahead from the past observations. Time series modeling, frequency domain and some other descriptive statistical data analyses are the primary subjects of this study: i...
Uncertainty models for vector based functional curves and assessing the reliability of G-Band
Kurtar, Ahmet Kürşat; Düzgün, H. Şebnem; Department of Geodetic and Geographical Information Technologies (2006)
This study is about uncertainty medelling for vector features in geographic information systems (GIS). It has mainly two objectives which are about the band models used for uncertainty modelling . The first one is the assessment of accuracy of GBand model, which is the latest and the most complex uncertainty handling model for vector features. Some simulations and tests are applied to test the reliability of accuracy of G-Band with comparing Chrisman’s epsilon band model, which is the most frequently used b...
Multiresolution analysis of S&P500 time series
Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2015)
Time series analysis is an essential research area for almost all people who are dealing with scientific and engineering problems. Main aim is to understand the underlying characteristics of the time series by using time as well as frequency domain analyses. Then one can make a prediction for the desired system to forecast observations ahead. Time series modeling, frequency domain analysis and some descriptive statistical analysis are main subjects of this thesis. Choosing an appropriate model is the main f...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
Ş. Sevinç, “Data analysis experiences: self-produced vs. Alien Data,” presented at the The Qualitative Report Eighth Annual Conference, (12 - 14 Ocak 2017), 2017, Accessed: 00, 2021. [Online]. Available: http://nsuworks.nova.edu/tqrc/eighth/day2/12/.