How our test assesses Google Ads
In Neuroworx's Google Ads test, candidates will need to answer a range of questions covering a varying degree of difficulty. Someone who is proficient in Google Ads should be able to perform the following tasks which this test helps assess:
Set up successful campaigns through proper identification of ad groups
Develop the right marketing budget based on the advertising goals
Create compositions for various advertising styles
Understand, operate, and use the different tools provided by Google Ads
Analyze reports and data in Google Ads to improve marketing objectives
Perform strategic planning based on emerging trends.
The test questions are designed to indicate how well a candidate knows how to use Google Ads across a broad spectrum of tasks. All questions are multiple-choice: a format familiar to most candidates that allows for immediate, objective scoring.
The response options aside from the correct answer, called distractors, are common mistakes or misconceptions, which enhances the question's ability to accurately measure a candidate's proficiency.
During the development process, this test was rigorously analysed to maximise reliability and validity in line with industry best practices. It was written, examined and revised by Google Ad specialists and psychometric experts, and field-tested with a representative sample of job applicants.
In other words, this test empirically demonstrates consistency and measures Google Ads proficiency as intended. Raw scores are provided and can be compared across candidates to see how they perform relative to each other.
Regarding the candidate's experience, this test has been reviewed by a panel of individuals representing diverse backgrounds to check for any sensitivity, fairness, face validity and accessibility issues. This ensures each candidate has a fair chance of demonstrating their Google Ads proficiency and feels the test is reasonable.
This test is also monitored on an ongoing basis to optimise performance and is analysed regularly for fairness issues.