data analysis in research google scholar

Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.

For many researchers unfamiliar with qualitative research, determining how to conduct qualitative analyses is often quite challenging. Part of this challenge is due to the seemingly limitless approaches that a qualitative researcher might leverage, as well as simply learning to think like a qualitative researcher when analyzing data. From framework analysis (Ritchie & Spencer, 1994) to content ...

This article is a practical guide to conducting data analysis in general literature reviews. The general literature review is a synthesis and analysis of published research on a relevant clinical issue, and is a common format for academic theses at the bachelor's and master's levels in nursing, physiotherapy, occupational therapy, public health and other related fields.

The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data. Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable.

Data science is typically a "concept to unify statistics, data analysis, and their related methods" to understand and analyze the actual phenomena with data. ... This research contributes to the creation of a research vector on the role of data science in central banking. In , ... [Google Scholar] 8. Ankerst M, Breunig MM, Kriegel H-P ...

Thousand Oaks, CA: SAGE Publications, Inc. [Google Scholar] Ryan GW, & Bernard HR (2000). Data management and analysis methods In Denzin NK & Lincoln YS (Eds.), Handbook of qualitative research (2nd ed.). Thousand Oaks, CA: SAGE Publications, Inc. [Google Scholar] Ryan GW, & Bernard HR (2003). Techniques to Identify Themes.

Teaching quantitative data analysis is not teaching number crunching, but teaching a way of critical thinking for how to analyze the data. The goal of data analysis is to reveal the underlying patterns, trends, and relationships of a study's contextual situation. Learning data analysis is not learning how to use statistical tests to crunch ...

Few studies report on comparisons of the manual and technological approaches to qualitative data analysis. In one such study, Basit (2003) compares the use of the manual and technological approach to qualitative data analysis drawing on two research projects. She argues that the approach chosen is dependent on the size of the project, the funds ...

Introduction. Secondary data analysis is a valuable research approach that can be used to advance knowledge across many disciplines through the use of quantitative, qualitative, or mixed methods data to answer new research questions (Polit & Beck, 2021).This research method dates to the 1960s and involves the utilization of existing or primary data, originally collected for a variety, diverse ...

Qualitative methods are critical for implementation science as they generate opportunities to examine complexity and include a diversity of perspectives. However, it can be a challenge to identify the approach that will provide the best fit for achieving a given set of practice-driven research needs. After all, implementation scientists must find a balance between speed and rigor, reliance on ...

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if ...

Data analysis is complex, but one data-handling method, "Big Data Analytics" (BDA)—the ... Another research on BDA to improve data utilization and decision-support qualities. ... a systematic literature review. J Big Data. 2019;6(1):1-30. Google Scholar Jha AK, Agi MA, Ngai EW. A note on big data analytics capability development in ...

For example, key term searches in Google Scholar for "entrepreneurial intention" and "firm diversification" resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ).

The launch of Google Scholar ( ) marked the beginning of a revolution in the scientific information market. This search engine, unlike traditional databases, automatically indexes information from the academic web. Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently ...

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science ...

While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...

Features are generated from data that are used as input for data analysis. Data are provided by all data sources that can be made available in the data collection. Furthermore, new features can also be generated from meta-descriptions of data (e.g. simulated data). Another influencing factor is the data analysis model (Analytical Models).

The earliest reference to the use of secondary data analysis in the nursing literature can be found as far back as the 1980's, when Polit & Hungler (1983 ), in the second edition of their classic nursing research methods textbook, discussed this emerging approach to analysis. At that time, this method was rarely used by nursing researchers.

data analysis, the process of systematically collecting, cleaning, transforming, describing, modeling, and interpreting data, generally employing statistical techniques. Data analysis is an important part of both scientific research and business, where demand has grown in recent years for data-driven decision making.Data analysis techniques are used to gain useful insights from datasets, which ...

A general inductive approach for analysis of qualitative evaluation data is described. The purposes for using an inductive approach are to (a) condense raw textual data into a brief, summary format; (b) establish clear links between the evaluation or research objectives and the summary findings derived from the raw data; and (c) develop a framework of the underlying structure of experiences or ...

Early access to new features. Access to GPT-4, GPT-4o, GPT-3.5. Up to 5x more messages for GPT-4o. Access to advanced data analysis, file uploads, vision, and web browsing

The cancer care experiences of gay, lesbian and bisexual patients: A secondary analysis of data from the UK Cancer Patient Experience Survey. European Journal of Cancer Care, 26 (4). 10.1111/ecc.12670 [Google Scholar] Johnston M. P. (2014). Secondary data analysis: A method of which the time has come.

This research synthesizes various relevant references such as articles, books, and other documents to answer research errors. Data collection techniques are carried out through articles sourced from Google Scholar and then analyzed. Qualitative data analysis techniques are used that consist of data reduction, data presentation, and conclusions.

INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...

SDA involves investigations where data collected for a previous study is analyzed - either by the same researcher(s) or different researcher(s) - to explore new questions or use different analysis strategies that were not a part of the primary analysis (Szabo and Strang, 1997).For research involving quantitative data, SDA, and the process of sharing data for the purpose of SDA, has become ...