The recent five years have seen an increase in publications applying digital algorithms in psychological methodology. Digital algorithms can, in many cases, predict up to 90% of the correlations of survey statistics and offer more precise ways to measure attitudes than the usual Likert scales. The same method has also been shown to diagnose mental disorders. Moreover, digital algorithms have added important insights into how common method variance may skew statistics.

Digital algorithms have also been shown to demonstrate the occurrence of jingle/jangle problems in construct development, i.e., the proliferation of constructs with different names but similar content.

Overall, these developments suggest that the use of digital text algorithms in Social Psychology can open new important perspectives on the conceptualization of attitudes, measurement of personality, diagnostics in abnormal psychology and meta-theoretical developments in our field.

We call for a range of contributions aiming to display the pioneering nature of the text algorithm across psychology.

The goal of this Research Topic is to advance the application of digital text algorithms to inform scale development and analysis. Contributions may span from personality and social psychology, philosophy of science, through methodology, to diagnostic applications.

We aim to receive contributions addressing the following, but not limited to, topics:

1) Semantic algorithms applied to survey research, as explanations for and characteristics of response patterns. Herein included the presentation of algorithms and tools to access the algorithms in conjunction with survey data.

2) Analysis of open semantic responses as alternatives to the use of fixed response scales.

3) Semantic algorithms in diagnostics of mental health, including schizophrenia and thought disorders.

4) Methodological innovations in the field of measurement and cross-cultural psychology. We are also looking for specific topics such as psychometric properties, common method variance, and spurious effects. Computational approaches to unstructured text data are also welcome.

5) ) Experimental designs to determine if the individual propensity to respond semantically can be handled procedurally through randomizing items, longitudinal design etc.

6) ) Contributions to the philosophy of science . Specifically, what text algorithms can tell us about constructs, construct formation and social constructionism. There are also possibilities of using text algorithms to explore the border between philosophy, theory and empirical science

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Frontiers in Psychology

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