Jonah Berger, Stijn M. J. van Osselaer, Chris Janiszewski
{"title":"Casting a Wider Net: Using Automated Content Analysis to Discover New Ideas","authors":"Jonah Berger, Stijn M. J. van Osselaer, Chris Janiszewski","doi":"10.1177/09637214251315716","DOIUrl":null,"url":null,"abstract":"Psychology has made great strides in how researchers collect, analyze, and report data, but there has been less attention to improving hypothesis generation. Some researchers still rely on intuition, serendipitous observations, or a limited reading of the literature to come up with a single idea about a relationship between constructs. Although this approach has led to valuable insights, it can constrain thinking and often fails to generate a full picture of what is going on. New approaches, however, allow researchers to cast a wider net. Specifically, by reducing the cost and effort of examining a broader set of potential variables, automated content analysis (i.e., computer-assisted methods for extracting features from unstructured data) can uncover new insights and help develop new theories. We describe how these techniques can be applied to various research questions and outline methods and criteria that can be used to gain a wider perspective. In sum, automated content analysis is a powerful tool for identifying new and important phenomena, building (and sharpening) theory, and increasing impact.","PeriodicalId":10802,"journal":{"name":"Current Directions in Psychological Science","volume":"2 1","pages":""},"PeriodicalIF":7.4000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Directions in Psychological Science","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/09637214251315716","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Psychology has made great strides in how researchers collect, analyze, and report data, but there has been less attention to improving hypothesis generation. Some researchers still rely on intuition, serendipitous observations, or a limited reading of the literature to come up with a single idea about a relationship between constructs. Although this approach has led to valuable insights, it can constrain thinking and often fails to generate a full picture of what is going on. New approaches, however, allow researchers to cast a wider net. Specifically, by reducing the cost and effort of examining a broader set of potential variables, automated content analysis (i.e., computer-assisted methods for extracting features from unstructured data) can uncover new insights and help develop new theories. We describe how these techniques can be applied to various research questions and outline methods and criteria that can be used to gain a wider perspective. In sum, automated content analysis is a powerful tool for identifying new and important phenomena, building (and sharpening) theory, and increasing impact.
期刊介绍:
Current Directions in Psychological Science publishes reviews by leading experts covering all of scientific psychology and its applications. Each issue of Current Directions features a diverse mix of reports on various topics such as language, memory and cognition, development, the neural basis of behavior and emotions, various aspects of psychopathology, and theory of mind. These articles allow readers to stay apprised of important developments across subfields beyond their areas of expertise and bodies of research they might not otherwise be aware of. The articles in Current Directions are also written to be accessible to non-experts, making them ideally suited for use in the classroom as teaching supplements.