Personality Test Examples as Data: 5 Item Formats, Their JSON, and the Scoring Code

Personality test examples typically refer to sample questions that provide a glimpse into the overall structure of a test. For developers tasked with storing answers and calculating results, the format of these test examples is equally important. This article explores five different personality test examples as data, including a plain Likert item, a reverse-keyed Likert item, a bipolar semantic-differential item, a forced-choice block, and an open response item that should never be scored.

While four of these formats are provided with TypeScript types and scoring code, the fifth format is discussed in terms of why it should remain outside the scoring process. All scored examples are drawn from a public-domain item pool that can be legally reused without any licensing concerns.

The IPIP (International Personality Item Pool) offers a valuable resource for developers seeking legitimate personality test examples. With a large collection of items that are in the public domain, the IPIP allows for copying, editing, translating, or using the items for any purpose without needing permission or incurring fees. This makes it an attractive option for developers looking for personality test examples to legally incorporate into their products.

The Big-Five factor markers within the IPIP include a 10-item Extraversion scale, with five positively keyed statements and five negatively keyed ones. These items have been validated with an alpha of .87, indicating strong reliability. The response set for all IPIP items is consistent: 1 (Very Inaccurate), 2 (Moderately Inaccurate), 3 (Neither Accurate Nor Inaccurate), 4 (Moderately Accurate), and 5 (Very Accurate).

For developers working with personality test examples, formats 1 and 2 involve Likert scale items and reverse keying. A Likert scale item consists of a statement followed by an ordered response set. The scoring rule for Likert items is straightforward: positively keyed items retain the answer value as is, while negatively keyed items are reversed (e.g., "Very Inaccurate" becomes 5 and "Very Accurate" becomes 1). The final scale score is the sum of the individual item scores.

The concept of reverse keying is crucial because respondents often drift towards consistent answers, which can skew results. By alternating the keying of items, developers can reduce this drift and maintain variance in the data. This simple adjustment of one integer per item can significantly impact the quality of the collected data.