The importance of considering contextual probabilities in shaping response
patterns within psychological testing is underscored, despite the ubiquitous
nature of order effects discussed extensively in methodological literature.
Drawing from concepts such as path-dependency, first-order autocorrelation,
state-dependency, and hysteresis, the present study is an attempt to address
how earlier responses serve as an anchor for subsequent answers in tests,
surveys, and questionnaires. Introducing the notion of non-commuting
observables derived from quantum physics, I highlight their role in
characterizing psychological processes and the impact of measurement
instruments on participants' responses. We advocate for the utilization of
first-order Markov chain modeling to capture and forecast sequential
dependencies in survey and test responses. The employment of the first-order
Markov chain model lies in individuals' propensity to exhibit partial focus to
preceding responses, with recent items most likely exerting a substantial
influence on subsequent response selection. This study contributes to advancing
our understanding of the dynamics inherent in sequential data within
psychological research and provides a methodological framework for conducting
longitudinal analyses of response patterns of test and questionnaire.

该研究旨在探讨早期回答如何影响测试、调查和问卷调查中后续答案的锚定作用，介绍了来自量子物理学的非交换可观测物的概念，并倡导利用一阶马尔科夫链模型来捕捉测试和调查回答中的顺序依赖关系，以此推进我们对心理研究中序列数据固有动态的理解，并为测试和问卷调查的纵向分析提供了一个方法学框架。

心理测量中响应动态的马尔可夫链模型

Markov chain models for inspecting response dynamics in psychological  testing

Context: Topic modeling finds human-readable structures in unstructured
textual data. A widely used topic modeler is Latent Dirichlet allocation. When
run on different datasets, LDA suffers from "order effects" i.e. different
topics are generated if the order of training data is shuffled. Such order
effects introduce a systematic error for any study. This error can relate to
misleading results;specifically, inaccurate topic descriptions and a reduction
in the efficacy of text mining classification results. Objective: To provide a
method in which distributions generated by LDA are more stable and can be used
for further analysis. Method: We use LDADE, a search-based software engineering
tool that tunes LDA's parameters using DE (Differential Evolution). LDADE is
evaluated on data from a programmer information exchange site (Stackoverflow),
title and abstract text of thousands ofSoftware Engineering (SE) papers, and
software defect reports from NASA. Results were collected across different
implementations of LDA (Python+Scikit-Learn, Scala+Spark); across different
platforms (Linux, Macintosh) and for different kinds of LDAs (VEM,or using
Gibbs sampling). Results were scored via topic stability and text mining
classification accuracy. Results: In all treatments: (i) standard LDA exhibits
very large topic instability; (ii) LDADE's tunings dramatically reduce cluster
instability; (iii) LDADE also leads to improved performances for supervised as
well as unsupervised learning. Conclusion: Due to topic instability, using
standard LDA with its "off-the-shelf" settings should now be depreciated. Also,
in future, we should require SE papers that use LDA to test and (if needed)
mitigate LDA topic instability. Finally, LDADE is a candidate technology for
effectively and efficiently reducing that instability.

研究通过使用 LDADE 方法，对潜在狄利克雷分配进行参数调整，以降低其对话题稳定性的影响，并通过研究 LDA 所引起的序列效应或话题稳定性问题，得出了结果表明使用 LDADE 的结果，其性能更佳，同时使用标准 LDA 的话题不稳定性应该被淘汰。