Compare and contrast the following methods of job evaluation, and include examples that are not the same as in the textbook to demonstrate understanding: Lott, Benge, and Multiple Regression. Identify the key differences.
To compare and contrast the methods of job evaluation - Lott, Benge, and Multiple Regression - let's first understand what each method entails.
1. Lott Method:
The Lott Method is a subjective method of job evaluation, often used in small organizations or when a limited number of jobs need to be evaluated. It involves collecting information about job characteristics, such as skill level, job complexity, and responsibility, and assigning point values based on the importance of each characteristic. The points are then totaled to determine the job's value or level within the organization.
Example: Let's consider a small marketing agency that uses the Lott Method to evaluate jobs. They assign point values for characteristics like creativity, communication skills, and project management. A junior graphic designer might receive fewer points, while a senior marketing manager might receive more points based on their levels of responsibility and skills.
2. Benge Method:
The Benge Method is another subjective method that focuses on job characteristics. It assigns each job a score based on a series of factors, such as education level, experience, responsibility, and complexity. The scores are then converted into a job grade or level.
Example: Imagine a manufacturing company that uses the Benge Method for job evaluation. They might assign weighted scores for factors like education, experience, and physical demands. A machine operator with a high school diploma and two years of experience may be assigned a lower grade, while a technician with a college degree and five years of experience might be assigned a higher grade.
3. Multiple Regression:
Multiple Regression is an objective method of job evaluation that uses statistical analysis to determine the relationship between job characteristics and job worth. It involves analyzing data on multiple jobs, considering factors such as required education, experience, complexity, and responsibility. These factors are used to build a regression equation, which estimates job worth based on the characteristics' influence.
Example: Let's say a large technology company uses Multiple Regression for job evaluation. They analyze data from a wide range of jobs, including programmer, project manager, and system analyst. Using multiple regression analysis, they might find that years of experience, level of education, and team management responsibility are key factors in determining job worth.
Key Differences:
1. Subjectivity vs. Objectivity:
The Lott and Benge methods rely on subjective judgment to assign point values or scores to job characteristics. Multiple Regression, on the other hand, is an objective method that utilizes statistical analysis based on actual data.
2. Data Analysis:
The Lott and Benge methods mainly consider job characteristics determined by the evaluator's judgment, with little data analysis. In contrast, Multiple Regression involves analyzing large amounts of data to determine the relationship between job characteristics and job worth.
3. Flexibility:
The Lott and Benge methods can be more flexible and easily adapted to small organizations or situations where a limited number of jobs need evaluation. Multiple Regression, due to its reliance on data analysis and statistical modeling, may require more resources and may be better suited for large organizations and broader job evaluations.
Remember, the examples provided here are fictional and not from the textbook to demonstrate the understanding of the concepts. It's important to consult relevant literature or resources for more accurate and in-depth information about these methods of job evaluation.