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1. CREATING A LIKERT SCALE IN WORD UPDATE
2. CREATING A LIKERT SCALE IN WORD FULL
3. CREATING A LIKERT SCALE IN WORD SOFTWARE

Therefore, we also excluded measurement topics related the use self-report scales, like identifying and controlling for response biases. This is a review of the initial development and validation of self-report Likert scales.

This is not a review of psychometrics, which would be impossibly broad, or advances in self-report in general, which would also be unwieldy (e.g., including measurement techniques like implicit measures and forced choice scales). The first limitations are with regard to scope.

However, given that scale development is such an extensive topic, the limitations of this review should be made very explicit. The present paper seeks to serve as such a resource by reviewing developments in Likert scale creation from the past 25 years.

Instead, it would focus on more recent advances and serve as a complement to these broader tutorials. This paper would not be a general tutorial in scale development like Clark and Watson (1995, 2019), Hinkin (1998), or others. Therefore, a contribution to psychology would be a paper that provides a review of advances in Likert scale development since classic tutorials were published. The authors were quick to note this themselves several times, e.g., “e have space only to raise briefly some key issues” and “unfortunately we do not have the space to do justice to these developments here” (p.

CREATING A LIKERT SCALE IN WORD FULL

However, scale creation-from defining the construct to testing nomological relationships-is such an extensive process that it is challenging for any paper to give full coverage to each of its stages.

CREATING A LIKERT SCALE IN WORD UPDATE

Recently, Clark and Watson (2019) gave an update to their 1995 article, integrating some newer topics into a general tutorial of Likert scale creation. Notably, however, it has been roughly 25 years since these seminal papers were published, and specific best-practices have changed or evolved since then. Likert scales provide a convenient way to measure unobservable constructs, and published tutorials detailing the process of their development have been highly influential, such as Clark and Watson (1995) and Hinkin (1998) (being cited over 6,500 and 3,000 times, respectively, according to Google scholar). However, in modern times, one of the most common measurement methods is the self-report Likert scale ( Baumeister et al., 2007 Clark and Watson, 2019). Psychological data are diverse and range from observations of behavior to face-to-face interviews. This paper is intended to be a resource for psychological researchers to be informed about more recent psychometric progress in Likert scale creation.

CREATING A LIKERT SCALE IN WORD SOFTWARE

The Supplementary Material provides further technical details on these advances and offers guidance on software implementation. We supplemented this review with an in-depth discussion of five particular advances: (1) conceptions of construct validity, (2) creating better construct definitions, (3) readability tests for generating items, (4) alternative measures of precision, and (5) ant colony optimization (ACO) for creating short forms. We reviewed six major measurement journals (e.g., Psychological Methods, Educational, and Psychological Measurement) between the years 1995–2019 and identified key advances, ultimately including 40 papers and offering written summaries of each. To address this, this current paper offers a selective review of advances in Likert scale development that have occurred over the past 25 years.

However, it is hard for psychologists to remain apprised of best practices as methodological developments accumulate. Developing self-report Likert scales is an essential part of modern psychology.