Research Methodology: Data Analysis Module

A process of summarizing data, organizing it to answer research questions, or generating new research questions; also involves looking for patterns and relationships in the data.

Preparing data for analysis by concentrating, recasting, and handling it in a way that makes it ready for analysis.

Finding relationships or differences in data, testing hypotheses, and drawing conclusions from data based on the research questions and theories.

Understanding the meaning of analyzed data in relation to research questions and existing theories. Subjective analysis.

Research that focuses on non-numerical data and seeks to understand concepts and experiences.

Research based on numerical data and statistical analysis.

The process of research consists of gathering data and then analyzing it, from which valid and meaningful conclusions can be extracted.

A testable statement about the expected relationship between variables in a study.

A table that shows the frequency of different attributes. The attributes are listed, and their occurrences are counted for each.

A table that analyzes multiple variables (like comparing aspects of two different groups).

A concise and informative heading for a table.

Descriptions of table columns and rows.

Categories in a table that do not overlap.

Summaries of data in a table's columns indicating the overall count for each category or group.

Explanatory notes providing essential details about specific data, columns or sources.

Describes the distribution of one variable, providing profiles of subjects (companies, people, etc.) across various characteristics.

Uses tests to determine the validity of conclusions from collected data and estimates population values.

A framework for organizing data, including describing facts, classifying data, and interpreting patterns.

Quantitative and qualitative research methods influence how data is analyzed.

Recording observed 'facts' after removing irrelevant information.

Grouping data based on similarities to create categories and headings.

Identifying important patterns and features linked to research questions.

Data that can be recreated or verified by others using the same methods, ensuring reliability and accuracy of the research.

Data that can be analyzed using statistical methods, focusing on numerical measurements.

A well-established framework that explains a phenomenon or concept, providing a broader context for understanding the data.

Analyzing data without predetermined hypotheses, exploring patterns and relationships emerging from the data itself.

Using mathematical tools, statistics help researchers analyze, interpret, and draw meaningful conclusions from numerical data.

Statistics helps to simplify large datasets into manageable summaries, making it easier to analyze and understand.

Statistics allows researchers to compare different data sets, revealing differences and similarities.

Using statistics, researchers can draw conclusions and make inferences about populations based on analyzed data.

A measure representing the typical or average value in a dataset. Examples include mean, median, and mode.

Indicates how spread out or varied the data is around the central tendency. Common measures are standard deviation, mean deviation, and range.

The average of a dataset, calculated by summing all values and dividing by the number of values.

Measures how much data points typically deviate from the mean. Higher standard deviation means greater spread.

Measures the asymmetry of a distribution, indicating whether it is skewed to the left or right.

Measures the peakedness or flatness of a distribution, indicating if it is sharply peaked or relatively flat.

Measures the strength and direction of a linear relationship between two variables.

Numbers that measure changes in a variable over time, allowing comparisons between periods.

A sequence of data points collected at different points in time, ordered chronologically.

A statement that assumes no relationship or difference between variables.

Computer programs designed for data analysis, like SAS and SPSS.

Data analysis focused on verifying or refuting pre-defined hypotheses.

Understanding the bigger context when analyzing data to avoid narrow interpretations.

Taking into account all significant elements when analyzing data to ensure accurate conclusions.

Acknowledging potential flaws in the research process that could affect the results.