Quantitative Data Analysis Approaches

2015 ◽  
Author(s):  
Babek Taheri ◽  
Catherine Porter ◽  
Christian Konig ◽  
Nikolaos Valantasis-Kanellos
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Quantitative Data ◽  
Quantitative Research ◽  
Descriptive Analysis ◽  
Statistical Techniques ◽  
Statistical Tools ◽  
Quantitative Data Analysis ◽  
Key Issues

In order to understand data and present findings in an accurate way, researchers and managers need to develop an awareness of statistical analysis techniques. This chapter focuses on two sets of the most widely used statistical tools – exploring relationships and comparing groups. It also briefly explains the nature of Big Data. This chapter explores the data preparation approach, how to conduct preliminary (descriptive) analysis as well as introducing some of the main statistical techniques used in quantitative research in business management. The key issues that should be related back to your study question are a) how to prepare your data; b) how to conduct preliminary data analysis; c) what analyses are appropriate for comparing groups; d) what techniques are most appropriate for exploring relationships.

Approaches to Quantitative Data Analysis and Evaluation

2016 ◽  
Author(s):  
Stephen Rae ◽  
Ahmed Salhin ◽  
Babak Taheri ◽  
Catherine Porter ◽  
Christian König ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Quantitative Data ◽  
Statistical Techniques ◽  
Statistical Tools ◽  
Analysis And Evaluation ◽  
Quantitative Data Analysis ◽  
Analyse Data

To understand data and present findings appropriately, researchers need awareness of statistical techniques. This chapter discusses the statistical tools used to analyse data collected. It focuses on two sets of the most widely used statistical tools, as shown in the ‘Deductive’ section in the data analysis area of the Methods Map (see Chapter 4): (1) exploring relationships and (2) comparing groups. In addition, we briefly explain ‘Big Data’.

Quantitative Data Analysis

2021 ◽  
pp. 184-202
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Undergraduate Students ◽  
Quantitative Data ◽  
Quantitative Research ◽  
Research Projects ◽  
Quantitative Data Analysis ◽  
Non Parametric

This chapter discusses the analysis of data in quantitative research and the most common strategies to follow. Undergraduate students in most programmes may not necessarily need to use advanced statistical analysis. This chapter takes the readers through parametric and non-parametric tests. Readers will be able to grasp measurement tests that are appropriate for several quantitative data analysis in research projects. The differences between parametric and non-parametric tests are examined and explored in some detail. The assumptions for parametric and non-parametric measures are also highlighted and relevant examples provided and should enable the reader to select appropriate measures for their research projects.

scholarly journals Graphical Data Analysis

2021 ◽  
Vol 2020 (1) ◽  
pp. 1
Author(s):  
Paul Collett
Keyword(s):  
Data Analysis ◽  
Quantitative Data ◽  
Quantitative Research ◽  
Statistical Significance ◽  
Academic Disciplines ◽  
Ongoing Debate ◽  
Statistical Significance Testing ◽  
Graphical Data ◽  
Quantitative Data Analysis

Limitations in statistical significance testing are an issue of ongoing debate in many academic disciplines. Moving away from a reliance on their use in quantitative research is seen as an important step towards improving the quality of such research. One area that can help here is graphical data analysis, both as an exploratory and explanatory tool. This paper presents an overview of graphical techniques for quantitative data analysis. After outlining the rationale for the use of graphical data analysis, consideration of the appropriate types of graphs to use is provided. A number of useful graphs, created using the R statistical package, are introduced, along with a link to the full code to reproduce the examples. Suggestions are presented for how graphical techniques can help with both the exploration and conformation stages in research. 統計的有意差検定の限界は、多くの学術分野で継続的に議論されている問題である。量的研究における統計的有意差検定への使用依存から脱却することは、研究の質を向上させるための重要なステップであると考えられる。ここで役立つのが、探索的・説明的なツールとしてのグラフデータ分析である。本稿は、定量的なデータ分析のためのグラフ技術の概要を説明する。グラフィカルなデータ分析を使用する理由を説明した後、使用するグラフの適切な種類について検討する。R統計パッケージを用いて作成された便利なグラフの数々を紹介し、例題を再現するための完全なコードへのリンクも掲載する。本稿は、研究における探索と適合の両方の段階で、グラフ技術がどのように役立つかを提案するものである。

Integrating Active Learning & Quantitative Skills into Undergraduate Introductory Biology Curricula

2011 ◽  
Vol 73 (8) ◽  
pp. 454-461 ◽  
Author(s):  
Jessica Goldstein ◽  
Dan F. B. Flynn
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Quantitative Data ◽  
Thinking Skills ◽  
Introductory Biology ◽  
Quantitative Skills ◽  
Quantitative Data Analysis ◽  
Testing Data ◽  
Core Components ◽  
Biology Laboratory

Analytical and quantitative thinking skills are core components of science but can be challenging to teach in introductory biology courses. To address this issue, modest curriculum modifications, including methods of hypothesis testing, data collection, and statistical analysis, were introduced into existing exercises in an introductory biology laboratory course. After completing the updated course, students demonstrated improved ability to understand and interpret statistical analyses. Furthermore, students were more likely to understand that hypothesis development and quantitative data analysis are important parts of biology. This study indicates that small changes to laboratory curricula can effect important changes in student learning and attitudes.

Practical Data Analysis

2015 ◽  
pp. 191-216
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Multivariate Data Analysis ◽  
Quantitative Data ◽  
Multivariate Data ◽  
Statistical Techniques ◽  
Analysis Techniques ◽  
Data Coding

In this chapter, students will learn “what to do” with their quantitative data once it has been collected. The chapter begins with a discussion of data coding, which is the process of preparing one's data for statistical analysis. What follows is a discussion of basic univariate, bivariate, and multivariate data analysis techniques. These techniques are presented in such a way that students with limited statistics backgrounds can understand and employ. Emphasis in this chapter is placed on giving students a working knowledge of statistical techniques that are most widely used when interpreting quantitative data.

Findings and Analysis

2020 ◽  
pp. 273-319
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Literature Review ◽  
Software Package ◽  
Quantitative Data ◽  
Qualitative Data ◽  
Descriptive Analysis ◽  
Statistical Software ◽  
Quantitative Data Analysis ◽  
Cross Tabulation

This chapter presents the results of the data analysis. The data was collected and processed in accordance to the problems posed at the outset of this research, which was to understand which determinants impacted on SPP. The results of both the quantitative and qualitative data are presented in this chapter. The quantitative data analysis comprises of descriptive analysis, cross-tabulation, regression analysis, and correlation analysis. SPSS, a commercially available statistical software package, was used to perform the analysis. In addition, this chapter also reports the findings of the qualitative data analysis where the key themes were noted. The findings are structured in line with the literature review: organisational determinants, personal determinants, and symbiotic determinants. The data is presented in both quantitative (numerical) and qualitative (non-numerical) formats, as per the selected research methodology.

Analisis Pengaruh Kedisiplinan Terhadap Performa Pegawai Kelurahan Rempoa, Kota Tangerang Selatan

2020 ◽  
Vol 4 (2) ◽  
pp. 131-138
Author(s):  
Krisnaldy Krisnaldy ◽  
Veta Lidya Delimah Pasaribu ◽  
Ahmad Syukri Batubara
Keyword(s):  
Data Analysis ◽  
Significant Influence ◽  
Quantitative Data ◽  
Employee Performance ◽  
Descriptive Analysis ◽  
Validity Test ◽  
Performance Variables ◽  
A Value ◽  
Quantitative Data Analysis ◽  
And Performance

The research objective in this study was to determine the effect of discipline on employee performance in Rempoa Village. Methods In this research is quantitative data analysis is an activity of collecting data from sources obtained. In this study, the population is the Rempoa Village employees, amounting to 33 employees. The results of the validity test show that all statement items in the discipline and performance variables obtained a value of rcount> from rtable and a positive value, then the statement item is said to be valid and the value of tcount> ttable or (7,615> 2,040), it is also strengthened by a significance of 0.000 <0 , 05. Thus, H0 is rejected and H1is accepted, this indicates that there is a positive and significant influence between discipline on employee performance Rempoa Village. In conclusion, discipline on the Rempoa Village employees is manifested in the form of obedience to the work and time regulations set. The results of the descriptive analysis show that the level. AbstrakTujuan penelitian dalam penelitian ini adalah untuk mengetahui pengaruh disiplin terhadap performa pegawai pada Kelurahan Rempoa. Metode dalam penelitian ini adalah kuantitatif analisa data merupakan kegiatan pengumpulan data dari sumber-sumber yang diperoleh”. Dalam penelitian ini populasinya adalah Pegawai Kelurahan Rempoa yang berjumlah 33 karyawan. Hasil uji validitas diketahui semua butir pernyataan dalam variabel disiplin dan Performa diperoleh nilai rhitung > dari rtabel dan nilai positif, maka butir pernyataan dikatakan valid dan nilai thitung > ttabel atau (7,615 > 2,040), hal itu juga diperkuat dengan signifikansi 0,000 < 0,05. Dengan demikian maka H0 ditolak dan H1 diterima, hal ini menunjukkan bahwa terdapat pengaruh positif dan signifikan antara disiplin terhadap performa pegawai pada Kelurahan Rempoa. Kesimpulannya disiplin pada pegawai Kelurahan Rempoa diwujudkan dalam bentuk ketaatan pada aturan pekerjaan dan waktu yang telah ditetapkan. Hasil analisis deskriptif menunjukkan bahwa tingkat disiplin kerja pegawai sudah dapat dikatakan baik, meskipun masih terdapat aspek yang belum optimal terutama menyangkut disiplin waktu dalam bekerja. Performa pegawai secara rata-rata dapat dikatakan telah memiliki performa yang cukup baik dilihat dari kualitas, kuantitas, dan pelaksanaan tugas. Performa pegawai yang masih belum optimal ditemukan ditemukan pada aspek tanggung jawab dan komunikasi menyelesaikan pekerjaan dengan rapi. Kata Kunci : Sumber Daya Manusia, Kedisiplinan, Kinerja

DEVELOPING SPEAKING MATERIALS THROUGH TASK – BASED LEARNING FOR VOCATIONAL STUDENTS OF HOSPITALITY STUDY PROGRAM

2017 ◽  
Vol 6 (4) ◽  
Author(s):  
Suchroni Panjaitan And Isli Iriani Indiah Pane
Keyword(s):  
Data Analysis ◽  
Research And Development ◽  
Quantitative Data ◽  
Course Design ◽  
Descriptive Analysis ◽  
Vocational Students ◽  
Study Program ◽  
Need Analysis ◽  
Quantitative Data Analysis ◽  
English Speaking

The objectives of this study are: (1) to know the existing English speaking materials for the students of hospitality study program, (2) to investigate the relevant English speaking materials for the students of hospitality study program, and (3) to develop appropriate English speaking materials through Task – Based Learning for the students of hospitality study program. This research was conducted at SMK Negeri 3 Tebing Tinggi. The research procedures of this study were Research and Development (R & D) through three phrases: 1) evaluation and need analysis, 2) course design, and 3) materials validation. The instruments used in this research were documentation sheet, interview and questionnaire. The data from evaluation and need analysis were analyzed in the descriptive analysis and the data from expert judgment results were using quantitative data analysis. The result of this study was a course book of speaking materials through task – based learning which consist of title, objective, let’s start, language in use, let’s practice, grammar in focus, and language reference. The task – based learning materials consist of three units, they are welcome to Grand Aston Hotel, talking on the phone, and what do they look like. Based on the experts’ judgement result, the score was 86.5 which belongs to good category based on Dirgeyasa (2011), since the range is (70 – 89).

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