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Preparing your data for an Affirmative Action Plan is an essential component of your overall compliance strategy. In this presentation, we'll focus on how to prepare your applicant flow data. Specifically, we'll discuss the kinds of data required, and talk about how and why bad or missing applicant flow data can render a data set useless for analysis purposes. Simple techniques for scrubbing data will be presented, and the webinar will conclude with a summary of common data validation tools.

Preparing Your Data for an Affirmative Action Plan: Applicant Flow

Thomas Econometrics

Preparing your data for an Affirmative Action Plan is an essential component of your overall compliance strategy. In this presentation, we'll focus on how to prepare your workforce snapshot. Specifically, we'll discuss the kinds of data required, and talk about how and why bad or missing workforce snapshot data can render a data set useless for analysis purposes. Simple techniques for scrubbing data will be presented, and the webinar will conclude with a summary of common data validation tools.

Preparing Your Data for an Affirmative Action Plan: Workforce Snapshot

Thomas Econometrics

Preparing your data for an Affirmative Action Plan is an essential component of your overall compliance strategy. In this presentation, we'll focus on how to prepare your terminations data. Specifically, we'll discuss the kinds of data required, and talk about how and why bad or missing terminations data can render a data set useless for analysis purposes. Simple techniques for scrubbing data will be presented, and the webinar will conclude with a summary of common data validation tools.

Preparing Your Data for an Affirmative Action Plan: Termination

Thomas Econometrics

Preparing your data for an Affirmative Action Plan is an essential component of your overall compliance strategy. In this presentation, we'll focus on how to prepare your promotions data. Specifically, we'll discuss the kinds of data required, and talk about how and why bad or missing promotions data can render a data set useless for analysis purposes. Simple techniques for scrubbing data will be presented, and the webinar will conclude with a summary of common data validation tools.

Preparing Your Data for an Affirmative Action Plan: Promotions

Thomas Econometrics

Preparing your data for an Affirmative Action Plan is an essential component of your overall compliance strategy. In this presentation, we'll focus on how to prepare your hiring data. Specifically, we'll discuss the kinds of data required, and talk about how and why bad or missing hiring data can render a data set useless for analysis purposes. Simple techniques for scrubbing data will be presented, and the webinar will conclude with a summary of common data validation tools.

Preparing Your Data for an Affirmative Action Plan: Hiring

Thomas Econometrics

Given the current economic conditions, many businesses are struggling and may need to take action to not only remain profitable but to remain sustainable. Some organizations may be considering a reduction in force. When exploring the option of a reduction in force, it is important that corporate counsel is involved. Corporate counsel will be able to advise on the legal implications of the reduction, to protect the interests of both the employer and the employees. The following ten points are designed to facilitate the discussion with your legal department when having a reduction in force conversation.

eBook: Reductions in Force - A Ten Point Inspection

Thomas Econometrics

In this presentation, we discuss the role of statistical analysis in examining class certification issues. Specifically, we discuss: (1) Requirements for Class Certification (2) Basics of Statistical Inference (3) Statistical Analysis: Commonality and Typicality (4) Class Certification Analysis Versus Merits Analysis (5) Importance of Expert Statistical Testimony: Randall v. Rolls Royce and Dukes v. WalMart

Examining Class Certification Issues with Statistical Analysis

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In this presentation, we examine the role of statistical analysis as evidence in employment discrimination analysis. Specifically, we address: (1) Alternative Legal Theories of Discrimination - Disparate Treatment and Disparate Impact (2) Direct versus Indirect Evidence (3) Basics of Statistical Inference (4) Statistical Significance and Sample Size (5) Application of Statistical Inference to Disparate Impact and Disparate Treatment Claims (6) The Importance of Statistics: Randall v. Rolls Royce

Statistical Analysis as Evidence in Employment Discrimination Litigation

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The Gender Pay Gap: A Closer Look at the Underlying Causes

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Recent Developments in Compensation Analysis

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Affirmative action plans are only as good as the availability estimates used to construct them. In this webinar, we’ll discuss the basic components of an affirmative action plan with an emphasis on how to construct the availability estimate. We’ll talk about different data sources, and the importance of industry, occupation, and geography. This webinar is part of our monthly series, vidEEO. vidEEO is designed to help individuals and organizations understand their compliance and legal responsibilities, measure quantitatively where they’re at, give them practical ways to improve, and show that meeting those responsibilities not only avoids litigation but improves their bottom line. Each month, we’ll bring you a new vidEEO webinar on a different aspect of EEO compliance. The program is based on the format popularized by TED – presentations are 18 minutes in length and deliver the best content available. All of the content will be released under a Creative Commons license, so the vidEEOs can be freely shared and reposted.

Affirmative action plans

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Measuring EEO compliance is hard. It's important to have a "bottom line" solution that's easy to calculate, easy to understand, and easy to communicate. In this presentation we’ll discuss why measuring EEO performance is so hard, provide a practical solution, and give you some ideas on how to effectively present the metrics to supervisors, managers, and the C Suite. This webinar is the first in our new monthly series, vidEEO. vidEEO is designed to help individuals and organizations understand their compliance and legal responsibilities, measure quantitatively where they’re at, give them practical ways to improve, and show that meeting those responsibilities not only avoids litigation but improves their bottom line. Each month, we’ll bring you a new vidEEO webinar on a different aspect of EEO compliance. The program is based on the format popularized by TED – presentations are 18 minutes in length and deliver the best content available. All of the content will be released under a Creative Commons license, so the vidEEOs can be freely shared and reposted.

Measuring EEO Compliance

Thomas Econometrics

In the self-evaluation guidelines, the OFCCP outlines an analysis method by which contractors may comply, centering around five “standards”: 1. The self-evaluation must be based on similarly situated employee groupings” (SSEGs); 2. The employer must make a reasonable attempt to produce SSEGs that are large enough for meaningful statistical analysis; 3. On an annual basis, the employer must perform some type of statistical analysis of the compensation system; 4. The employer must investigate any statistically significant disparities in compensation (defined as two or more standard deviations) and provide appropriate remedies; 5. The employer must contemporaneously create and retain the required data and make it available to the OFCCP during a compliance review.

eBbook: Five Standards For An OFCCP Compliant Compensation Self Evaluation

Thomas Econometrics

eBbook: The Fair Pay Act and Implications For Compensation Modeling

Thomas Econometrics

Employers conduct compensation self-audits for a variety of reasons, ranging from obligations as a federal contractor to a desire for a deeper understanding of their compensation practices. Compensation self-audits are also frequently found as part of an employer’s risk management plan; self-audits allow an employer to identify potential problem areas and assess the extent of exposure in the event of compensation litigation.

eBook: A Practical Guide to Compensation Self-Auditing

Thomas Econometrics

Slidecast: Understanding the Compensation Self Audit

Thomas Econometrics

When evaluating the economic damages in wrongful termination litigation, the central question being addressed consists of two components: 1. What is the likely compensation (including benefits) that the plaintiff would have earned but for the alleged wrongful termination; 2. What is the likely compensation (including benefits) that the plaintiff can be expected to earn from alternate employment given the alleged wrongful termination. The amount of damages is equal to the difference between the two compensation streams. The task at hand, then, is to appropriately calculate the two compensation streams.

Calculation of Damages in Wrongful Termination Litigation

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About Thomas Econometrics

Preparing Your Data for an Affirmative Action Plan: Applicant Flow

Preparing Your Data for an Affirmative Action Plan: Workforce Snapshot

Preparing Your Data for an Affirmative Action Plan: Termination

Preparing Your Data for an Affirmative Action Plan: Promotions

Preparing Your Data for an Affirmative Action Plan: Hiring

eBook: Reductions in Force - A Ten Point Inspection

Examining Class Certification Issues with Statistical Analysis

Statistical Analysis as Evidence in Employment Discrimination Litigation

The Gender Pay Gap: A Closer Look at the Underlying Causes

Recent Developments in Compensation Analysis

Affirmative action plans

Measuring EEO Compliance

eBbook: Five Standards For An OFCCP Compliant Compensation Self Evaluation

eBbook: The Fair Pay Act and Implications For Compensation Modeling

eBook: A Practical Guide to Compensation Self-Auditing

Slidecast: Understanding the Compensation Self Audit

Calculation of Damages in Wrongful Termination Litigation

Compensation Document Creation and Retention

Managing Employment Litigation Risk with Proactive Analyses

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