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Related Concept Videos

Qualitative Analysis01:10

Qualitative Analysis

297
Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
There are two main approaches to qualitative analysis:...
297
Structuralism01:26

Structuralism

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Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
327
Case Studies01:22

Case Studies

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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Humanistic Psychology01:24

Humanistic Psychology

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Humanistic psychology emerged in the mid-20th century as a response to the deterministic and pessimistic nature of behaviorism and psychoanalysis. While behaviorism focused on observable behaviors influenced by the environment and psychoanalysis delved into unconscious motivations, both theories suggested that human actions lacked free will. In contrast, humanistic psychology offers a perspective that emphasizes the innate potential for goodness and growth within every individual.
This approach...
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Realist Approach to Qualitative Data Analysis.

Arcellia Farosyah Putri, Colin Chandler, Jennifer Tocher

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    This study introduces a four-step realist data analysis for qualitative research. This method enhances understanding of causal interactions and program outcomes, aiding complex decision-making.

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    Area of Science:

    • Evaluation Research
    • Qualitative Inquiry
    • Critical Realism

    Background:

    • Realist approach popular in evaluation research for understanding program causality.
    • Focuses on how programs work, contextual factors, and outcomes.
    • Enhances theoretical explanations of causal interactions in qualitative studies.

    Purpose of the Study:

    • To present a realist-informed data analysis approach for qualitative data.
    • To explore causal interactions within qualitative datasets.
    • To demonstrate a method for robust program evaluation.

    Main Methods:

    • Employed a four-step realist approach rooted in critical realism.
    • Utilized retroductive theorizing for qualitative data analysis.
    • Steps included: category identification, context-mechanism-outcome configuration, demi-regularities identification, and generative mechanism refinement.

    Main Results:

    • The four-step analysis reveals underlying causal mechanisms and interactions.
    • Generates comprehensive causal explanations of program functioning.
    • Identified patterns, pattern fluctuations, and confirmed mechanisms.

    Conclusions:

    • Realist data analysis, through retroductive theorizing, underpins causal understanding.
    • The four-step method facilitates identification and confirmation of generative mechanisms.
    • Provides valuable insights for complex decision-making affecting communities.