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Prediction and Semantic Trained Scales: Examining the Relationship Between Semantic Responses to Depression and Worry and the Corresponding Rating Scales

Chapter in book
Authors Oscar N. E. Kjell
Katarina Kjell
Danilo Garcia
Sverker Sikström
Published in Statistical Semantics - Methods and Applications. Sikström, Sverker, Garcia, Danilo (Eds.)
ISBN 978-3-030-37250-7
Publisher Springer
Place of publication Cham, Switzerland
Publication year 2020
Published at Department of Psychology
Language en
Keywords Semantic Trained Scales, Laten semantic Algorithm, Depression, Worry
Subject categories Psychology, Psychiatry

Abstract

This chapter focuses on using the semantic representations, consisting of a number of semantic dimensions, in multiple linear regressions to predict a numerical outcome variable. We examine whether there is a statistically significant relationship between texts and numerical values. Thus, we post the question with what certainty we can predict numerical (or categorical) data from text data? We describe how to use leave-n-out cross-validation to avoid using the same data when both the training data and the evaluation data is in the same dataset. Furthermore, we discuss how to avoid overfitting models. We also describe how trained models can be used to predict numerical values from a new set of text data. Subsequently, research using semantic trained scales in different ways is briefly described to give an idea of how they can be used in different research studies. In particular, we focus on examining the relationship between individuals’ word responses to the semantic questions of depression and worry and their responses to corresponding numerical rating scales using the data from Kjell, Kjell, Garcia and Sikström (2018) as described in Chapter 4.

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