How to evaluate AoK Human Sciences
When writing the Theory of Knowledge (ToK) Essay students are asked to evaluate their main knowledge claims. Today’s post looks at the main evaluation points that can be applied to most knowledge claims made in Area of Knowledge (AoK) Human Sciences. Obviously, these evaluations points would need to be considered in terms of the specific claims made in an essay. This is the second in a five part series looking at evaluation points for each of the 5 Areas of Knowledge. The first post, How to evaluate in AoK Natural Sciences, is linked here.
This blogpost (evaluation in Human Sciences) can be watched as a video at this link.
The human sciences consider the complexities of human behaviour and societies. Evaluating human science knowledge involves understanding the unique challenges and limitations inherent in studying humans using the scientific method. Here, we explore four critical evaluation points: researcher and participant reactivity, operationalising and measuring human behaviours, establishing causation, and applying models in real-world environments.
1. Problems of Researcher and Participant Reactivity
One significant challenge in human sciences is the issue of reactivity, which can impact both researchers and participants. Researcher reactivity occurs when researchers’ expectations or behaviours influence the study’s outcome. This can introduce bias, undermining the study’s validity. Participant reactivity, often known as the Hawthorne effect, happens when participants alter their behaviour because they know they are being observed. This can skew the results, making it difficult to draw accurate conclusions about natural behaviours. To mitigate these effects, researchers use techniques like double-blind studies and unobtrusive observation, but completely eliminating reactivity remains challenging.
2. Problems of Operationalising and Measuring Human Behaviours
Operationalising and measuring human behaviours, particularly emotions, presents another set of challenges. Human emotions are complex and multifaceted, making them difficult to quantify accurately. Researchers must develop operational definitions that translate abstract concepts into measurable variables. For instance, defining and measuring happiness might involve self-report surveys, physiological measures, or behavioural observations. Each method has limitations, such as subjective biases in self-reports or the potential invasiveness of physiological measures. Ensuring reliability and validity in these measurements is an ongoing struggle, as capturing the full essence of human experiences in empirical terms is inherently challenging.
3. Problems of Establishing Causation
Establishing causation in the human sciences is particularly problematic, especially when correlation designs are used. Correlational studies identify relationships between variables but do not establish cause and effect. For example, a study might find a correlation between social media use and anxiety, but this does not prove that social media use causes anxiety. There could be underlying variables influencing both factors. Experimental designs, which can better establish causation, are often difficult to implement due to ethical considerations and the complexity of controlling variables in human contexts. Consequently, many findings in the human sciences remain tentative and open to interpretation.
4. Problems of Applying Models in a Real-World Environment
Applying theoretical models to real-world environments also poses significant challenges. Models in the human sciences are simplifications of complex realities, often based on controlled experimental conditions. When these models are applied to the real world, the variability and unpredictability of human behaviour can lead to unexpected outcomes. For example, economic models predicting consumer behaviour might fail to account for cultural differences or individual psychological factors. This discrepancy highlights the limitations of models and the need for continuous adaptation and refinement to better reflect real-world complexities.
In conclusion, evaluating human science knowledge requires a nuanced understanding of these critical points. By examining researcher and participant reactivity, the difficulties in measuring human behaviours, the challenges of establishing causation, and the limitations of applying models in real-world environments, you can develop more sophisticated evaluations of knowledge and knowledge production in the human sciences.
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