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This document discusses key concepts in scholarly inquiry and research design including measurement, variables, data analysis, and establishing causation. It addresses measuring phenomena, designing research to reveal relationships, analyzing data to discover useful information, defining variables, and ensuring measures are reliable and valid. It also outlines conditions needed to establish causation including covariation between variables, temporal precedence, and ruling out alternative explanations. Finally, it discusses using sample statistics to estimate population parameters.
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This document outlines the key components of a research design, including establishing a theory and hypotheses to test, determining appropriate units of analysis and measurements, collecting data, analyzing the data statistically, and establishing causation rather than just correlation between variables. It emphasizes that a research design must account for potential third variables and alternative explanations in order to draw valid causal conclusions.
Data analysis
This document discusses various techniques for analyzing quantitative and qualitative data. It describes editing, coding, classification, and tabulation as methods for processing qualitative data. For quantitative data, it covers univariate analyses like measures of central tendency and dispersion. It also discusses bivariate analyses like correlation and regression, as well as multivariate techniques including multidimensional analysis, factor analysis, and cluster analysis. The goal of data analysis is to discover useful information and support decision making.
You Want Me to Measure What?
Why limit ourselves to traditional quantitative metrics like visitor count, page weight, conversion, and revenue when there is so much valuable qualitative data available? We can turn qualitative data into quantitative data and use the same rigorous analysis techniques to help lead us to better designs, products, services, and experiences.
Factor anaysis scale dimensionality
The document discusses various methods for scaling and measuring constructs, including unidimensional and multidimensional scaling. It describes exploratory factor analysis and confirmatory factor analysis as two common methods for analyzing multidimensional data. Exploratory factor analysis is used to uncover the underlying factor structure without prior hypotheses, while confirmatory factor analysis tests a hypothesized factor structure.
Lesson 6 chapter 4
Data analysis involves understanding known facts or assumptions to draw conclusions about research questions. There are two main types of data analysis: qualitative and quantitative. Qualitative analysis examines subjective data like thoughts, feelings, and attitudes expressed in words, collected through interviews and observations. Quantitative analysis deals with numerical data, using statistical techniques to summarize relationships between variables. Both types of analysis require coding, organizing, and interpreting large amounts of data to understand the relevant information.
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Wfed550
- 1. Scholarly Inquiry in WF ED 11 November 2013
- 2. Measurement – Assigning numbers to phenomena according to rules Research Design – Collecting and organizing data to reveal relationships and causation Data Analysis – Inspecting, cleaning, transforming, and modeling data to discover useful information
- 3. Variables – Attributes that can take on two or more mutually exclusive values Variables describing entities are measured Measurement involves the interaction between a measurement instrument and the entity measured Measures can be manifest or latent Measures must be reliable (accurate) and valid (have meaning)
- 4. A and B must covary A must precede B A, and only A, must be the cause of B Covariation –Changes in cause must be changes in the effect Temporal Precedence — Changes in the cause must precede changes in the effect No plausible alternatives — Changes in cause are only explanation for effect
- 5. Information is desired about a populati0n But, data is usually from a sample Problem is to estimate population parameters from sample statistics
- 6. Level – What is typical? Dispersion – How much do entities vary? Relationship – Do variables covary?