The human resources industry relies heavily on a wide range of assessments to support its functions. In fact, to ensure unbiased and fair hiring practices the US department of labor maintains a set of guidelines (Uniform Guidelines) to aid HR professionals in their assessment development ventures.

人力资源行业严重依赖各种评估来支持其职能。 实际上，为了确保公正和公平的雇用做法，美国劳工部维护了一套准则( 统一准则 )，以协助HR专业人士进行评估开发。

Personality assessments are often used in selection batteries to determine cultural fit into a company. Cognitive ability (ie. IQ) tests are consistently found to be the best overall predictor of job performance across all types and levels of jobs (Schmidt & Hunter, 1998). Structured interviews are used extensively in hiring decisions as they help to remove bias by standardizing the question and scoring. Performance reviews use rigorous Likert assessments that ask managers and co-workers to rate employees of their performance (ie. behaviorally anchored rating scales). Employee engagement surveys assess the extent employees feel satisfaction, passion, effort, and commitment to their employer and job. Last but not least, employee exit surveys are often employed upon the termination of an employee in order to determine how the employee felt about a range of topics related to the organization.

个性评估通常用于选择人员，以确定公司的文化契合度。 一直以来，认知能力(即智商)测试是所有类型和级别的工作绩效的最佳总体预测指标(Schmidt＆Hunter，1998)。 结构化面试广泛用于招聘决策，因为它们有助于通过标准化问题和评分来消除偏见。 绩效评估使用严格的李克特评估，要求经理和同事对员工的绩效进行评估(即行为锚定的评估量表)。 员工敬业度调查可评估员工对自己的雇主和工作的满意度，热情，努力和承诺的程度。 最后但并非最不重要的一点是，通常在员工离职后进行员工离职调查，以确定员工对与组织相关的一系列主题的感觉。

This extensive use of employee assessment has given rise to a multi-billion dollar industry specializing in the development of a wide range of tests. Let’s focus our attention on employee attitude surveys as they form a very large segment of this industry. If one is to purchase a survey how can you be sure you are getting a quality product? Any reputable developer should supply you with not only a manual but also a validation report which outlines the steps taken to make sure the survey is actually measuring what it’s meant to measure.

员工评估的这种广泛使用已催生了数十亿美元的行业，专门从事各种测试的开发。 让我们将注意力集中在员工态度调查上，因为它们构成了该行业的很大一部分。 如果要购买一份调查表，如何确定获得优质产品？ 任何有信誉的开发人员都不仅应向您提供手册，而且还应提供验证报告，该报告概述了为确保调查实际在测量所要测量的内容而采取的步骤。

In this article, I would like to examine an employee exit survey and determine the quality of the survey based on a selected few metrics. Therefore, when you are handed a validation report from a survey vendor you will know and understand the metrics needed to make an informed purchase.

在本文中，我想检查一下员工离职调查，并根据选定的一些指标来确定调查的质量。 因此，当您从调查供应商处收到验证报告时，您将了解并了解进行明智购买所需的度量。

调查发展过程 (Survey Development Process)

Before we jump into our metrics and code, let’s take a few mins to review how a statistically rigorous survey is developed and validated.

在我们进入指标和代码之前，让我们花一些时间回顾一下如何开发和验证统计上严格的调查。

• The process begins with a question.

  该过程从一个问题开始。

  For example: “Are my employees happy with their jobs and the company?”

  例如：“我的员工对他们的工作和公司满意吗？”

• How do we define “Happiness”? Before we can start writing survey questions we need to operationally define “happiness”. We scour the literature for employee happiness research. Undoubtedly, you will arrive at topics such as employee satisfaction, commitment, and engagement. You will read dozens of studies proposing unique models of employee satisfaction. Hackman & Oldham’s Job Characteristics model is often sited when developing employee satisfaction surveys for its comprehensiveness and statistical validity. Validity in the sense that many researchers have adopted this model in their research and/or assessments and found that it holds true.
  我们如何定义“幸福”？ 在开始写调查问题之前，我们需要在操作上定义“幸福”。 我们搜寻有关员工幸福感研究的文献。 毫无疑问，您将遇到诸如员工满意度，承诺和敬业度等主题。 您将阅读数十项提出独特的员工满意度模型的研究。 Hackman＆Oldham的工作特征模型通常在开发员工满意度调查时因其全面性和统计有效性而使用。 从许多研究人员在他们的研究和/或评估中采用这种模型的意义上讲，其有效性是正确的。

• The selected model will serve as the basis for the questions or items composing the survey. Depending on the length of the survey, we will write 3–10 items for each component (ie. skill variety, task identity, autonomy, etc.) of the model. We write multiple questions to confirm the sentiment of an employee on each model component. We will be focusing on quantitative items that require the employee to select a label on a continuous scale which corresponds with their internal attitude (ie. Likert-Scale).
  选择的模型将作为构成调查的问题或项目的基础。 根据调查的时间长短，我们将为模型的每个组件(即技能种类，任务标识，自主权等)编写3-10个项目。 我们编写多个问题以确认员工在每个模型组件上的情绪。 我们将专注于要求员工选择与其内部态度相对应的连续比例的标签(即李克特量表)的定量项目。
• Each question can be validated (content validation) by allowing prominent researchers in the area of study (ie. employee satisfaction) to scrutinize each question. Content validity is very often cited in legal proceedings when an employee is under litigation for improper hiring decisions. Other forms of validation include construct and criterion validity.
  通过允许研究领域中的杰出研究人员(即员工满意度)对每个问题进行审查，可以验证每个问题(内容验证)。 当员工因不当雇用决定而受到诉讼时，通常会在法律诉讼中提及内容有效性。 验证的其他形式包括构造和准则有效性。
• Upon completion of the first draft, a pilot test is conducted with a sample of employees closely resembling the full employee population. It is important to obtain a large and representative sample in order to be confident in the results.
  初稿完成后，将对与全体员工非常相似的员工样本进行试点测试。 重要的是要获得大量有代表性的样本，以便对结果有信心。
• Once the survey has been administered and the data collected, certain psychometric properties need to be examined, namely reliability and validity. Based on the psychometric results the survey is revised until optimal psychometrics can be achieved.
  一旦完成了调查并收集了数据，就需要检查某些心理测量特性，即可靠性和有效性。 根据心理测量结果，对调查进行修订，直到可以实现最佳心理测量。

员工离职调查示例 (Employee Exit Survey Example)

First and foremost, this fictional dataset and its results should be treated as such, fictional. Secondly, termination reason (ie. voluntary, involuntary, retirement, etc.) have been omitted from the loaded dataset as this article will focus exclusively on the psychometric properties on the Likert-scale questions.

首先，这个虚构的数据集及其结果应被视为虚构的。 其次，已终止的原因(即自愿，非自愿，退休等)已从加载的数据集中省略，因为本文将只关注Likert量表问题的心理计量学特性。

import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
import pingouin as pg
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
from factor_analyzer import ConfirmatoryFactorAnalyzer, ModelSpecificationParserimport warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', None)
%matplotlib inlinewith open('likert.csv') as f:
    likert_items = pd.read_csv(f)
f.close()likert_items.head()

len(likert_items), likert_items.describe().T

Upon loading our libraries, we have a dataset of about 702 employees who answered 27 Likert-type questions where 1 is strongly disagree and 5 is strongly agree. The questions asked terminated employees on their perceptions on promotional opportunities, manager satisfaction, job satisfaction, training, and work/life balance.

加载我们的图书馆后，我们有大约702名员工的数据集，它们回答了27个Likert类型的问题，其中1个强烈不同意，5个强烈同意。 提出的问题终止了员工对晋升机会，经理满意度，工作满意度，培训和工作/生活平衡的看法。

We have to acknowledge the fact that terminated employees are not required to fill out an exit survey. It is completely at their discretion and as such the results from such survey may be skewed

我们必须承认以下事实：被解雇的员工不需要填写退出调查。 这完全由他们自己决定，因此该调查的结果可能会有所偏差。

潜在因素 (Underlying Latent Factors)

As discussed above, carefully designed surveys have their roots in validated scholarly models and this survey is no different. The 27 individual items have been developed to assess 8 underlying or latent factors.

如上所述，精心设计的问卷调查源于经过验证的学术模型，该问卷调查没有什么不同。 已经开发了27个单独的项目来评估8个潜在或潜在因素。

相关性 (Correlations)

likert_corr = likert_items.corr()plt.figure(figsize=(25,15))
mask = np.zeros_like(likert_corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
plt.figure(figsize=(70,40))
plt.xticks(fontsize=50)
plt.yticks(fontsize=50)
sns.heatmap(likert_corr, cmap='coolwarm', annot=True,
            fmt=".2f", annot_kws={'size': 40, 'color': 'black'}, linewidths=2,
            vmin=-0.5, mask=mask)

When dealing with quantitative questions the first tool at our disposal is the trusty correlation. A correlation matrix will give us the first clues as to the psychometric quality of our survey. The correlations highlighted in green are those of the items meant to measure the same underlying latent construct. These correlations should retain a relatively high correlation (0.5 to 0.7) as that would indicate the items are measuring several unique sub-components of a larger construct. However, if the correlations are too large (0.7+) that would signify the items might be measuring very similar or even the same constructs. We ultimately want our questions to be assessing different sub-components of a larger factor such as “Job Satisfaction”.

处理定量问题时，我们可以使用的第一个工具是可信赖的相关性。 相关矩阵将为我们提供有关调查的心理计量质量的第一个线索。 绿色突出显示的相关性是那些旨在测量相同潜在潜在构造的项目的相关性。 这些相关性应保持相对较高的相关性(0.5到0.7)，因为这将表明这些项目正在测量较大构造的几个唯一子组件。 但是，如果相关关系太大(0.7+)，则表示项目可能测量的结构非常相似甚至相同。 我们最终希望我们的问题是评估更大因素的不同子成分，例如“工作满意度”。

On the other hand, correlations outside of the highlighted areas should remain as low as possible. That would indicate that the items are measuring vastly different constructs. For example, the high correlation between direct_mgmt_satisfaction and feed_offered (0.75) is rather discouraging as they are measuring different latent constructs (employee value and management satisfaction). On the other hand, this correlation also gives us a glimpse into a miss-classification of the “feedback_offered” item as it is the manager that most often provides constructive feedback and, therefore, one might reason this item is actually assessing management satisfaction instead.

另一方面，突出显示区域之外的相关性应保持尽可能低。 这将表明这些项目正在衡量截然不同的结构。 例如，direct_mgmt_satisfaction和feed_offered(0.75)之间的高度相关性令人沮丧，因为它们正在测量不同的潜在构造(员工价值和管理满意度)。 另一方面，这种相关性也使我们瞥见了“ feedback_offered”项目的错误分类，因为经理经常提供建设性的反馈，因此，有人可能会认为该项目实际上是在评估管理满意度。

Overall, looking at the correlation matrix it would seem management satisfaction, job satisfaction, team satisfaction, training satisfaction, and work/life balance are relatively strong latent factors. On the other hand, employee value and organizational environment are rather weak. Finally, salary satisfaction is composed of one question.

总体而言，从相关矩阵中可以看出，管理满意度，工作满意度，团队满意度，培训满意度以及工作/生活平衡是相对较强的潜在因素。 另一方面，员工价值和组织环境相对薄弱。 最后，薪水满意度由一个问题组成。

可靠性 (Reliability)

Reliability is a measure of consistency. In other words, if someone were to take the same personality assessment multiple times their scores should not vary a large amount. The most prominent reliability metric when developing surveys is Cronbach’s Alpha and it’s a measure of internal consistency. The Alpha assumes unidimensional or that the items passed into its function are measuring one factor, therefore, we need to calculate Alpha for each latent factor separately.

可靠性是一致性的度量。 换句话说，如果某人要多次进行相同的人格评估，那么他们的分数应该不会相差很大。 进行调查时，最突出的可靠性指标是Cronbach的Alpha ，它是内部一致性的一种度量。 Alpha假定是一维的，或者传递到其函数中的项正在测量一个因子，因此，我们需要针对每个潜在因子分别计算Alpha。

valued = likert_items[['promotional_opportunities', 'performance_recognized', 'feedback_offered','coaching_offered']]mgmt_sati = likert_items[['mgmt_clear_mission','mgmt_support_me',
'mgmt_support_team', 'mgmt_clear_comm', 'direct_mgmt_satisfaction']]job_satisfaction = likert_items[['job_stimulating', 'initiative_encouraged', 'skill_variety','knowledge_variety', 'task_variety']]team_satisfaction = likert_items[['teamwork','team_support', 'team_comm', 'team_culture']]training_satisfaction = likert_items[['job_train_satisfaction',
'personal_train_satisfaction']]org_environment = likert_items[['org_culture', 'grievances_resolution', 'co-worker_interaction', 'workplace_conditions']]work_life_balance = likert_items[['job_stress','work/life_balance']]salary_satisfaction = likert_items[['fair_salary']]dict = {'valued': valued, 'mgmt_sati': mgmt_sati,
        'job_satisfaction': job_satisfaction,
        'team_satisfaction': team_satisfaction,
        'training_satisfaction': training_satisfaction,
        'org_condition': org_condition,
        'work_life_balance': work_life_balance}for i in dict:
    print('{} Alpha: {}'.format(i, pg.cronbach_alpha(data=dict[i], nan_policy='listwise'))

Mgmt satisfaction, job satisfaction, team satisfaction, and training satisfaction all exhibit high to very high alpha coefficients (0.8+). High alpha results signify the items are measuring the same underlying construct. Employee valued, org environment, and work-life balance resulted in lower alpha coefficients which means the items can be improved to better measure their constructs. We need at least 2 items to measure alpha therefore, salary_satisfaction was not included in this analysis.

管理满意度，工作满意度，团队满意度和培训满意度都表现出高到非常高的alpha系数(0.8+)。 高alpha结果表示这些项目正在衡量相同的基础结构。 员工重视，组织环境和工作与生活的平衡导致较低的alpha系数，这意味着可以改进这些项目以更好地衡量其构造。 我们至少需要2个项目来衡量Alpha，因此本分析中不包括salary_satisfaction。

Another commonly used reliability measure is “Test-Retest Reliability”. The survey is administered to the same sample multiple times and the scores are compared to determine consistency.

另一个常用的可靠性度量是“重测可靠性”。 多次对同一样本进行调查，并比较分数以确定一致性。

因子分析 (Factor Analysis)

探索性因素分析(EFA) (Exploratory Factor Analysis (EFA))

Factor analysis or more specifically Exploratory Factor Analysis is often used in the survey development process. It is used to conduct a preliminary exploration of the assessment during the development stage. Factor analysis is a data reduction technique that attempts to extract the commonly held variance between features into a factor. In other words, survey items that are meant to assess a common latent factor will covary together. Covary in the sense that if someone answers “strongly agree” on the first question they will most likely answer in a similar fashion on a different question which is meant to assess the underlying latent factor.

因子分析或更具体地说是探索性因子分析经常在调查开发过程中使用。 它用于在开发阶段对评估进行初步探索。 因子分析是一种数据缩减技术，试图将要素之间的共同保留的方差提取到因子中。 换句话说，旨在评估共同潜在因素的调查项目将同时出现。 某种意义上说，如果有人回答第一个问题“强烈同意”，那么他们很可能会以类似的方式回答另一个问题，以评估潜在的潜在因素。

EFA vs PCA

全民主场迎战PCA

EFA is similar to a PCA (principal component analysis) in terms of dimensionality reduction but it has certain differences:

EFA在降维方面类似于PCA(主要成分分析)，但存在某些差异：

• An EFA identifies latent factors that can be interpreted because the latent factors were developed on purpose.
  EFA识别可以解释的潜在因素，因为潜在因素是有意开发的。
• PCA attempts to explain the maximum variance (PC1 will hold most variance) which EFA attempts to explain the covariance among the features.
  PCA试图解释最大方差(PC1将保持最大方差)，而EFA试图解释特征之间的协方差。
• PCA performs its analysis without regard for underlying latent constructs, it simply wants to account for all the variance in the features.
  PCA进行分析时不考虑潜在的潜在结构，它只想考虑特征中的所有差异。
• PCA components are orthogonal (uncorrelated) by design but EFA factors don’t need to be (depending on the rotation).
  PCA组件在设计上是正交的(不相关)，但不需要EFA因子(取决于旋转)。

Bartlett’s Test of Sphericity

巴特利特的球性测试

Before attempting to use a factor analysis one should conduct the “Bartlett’s Test of Sphericity” which examines whether or not there are multiple features that can be reduced to latent factors. The result of Bartlett’s test is significant (p=0.00) which means the data can be reduced and we can continue with the factor analysis.

在尝试使用因子分析之前，应进行“巴特利特球性测试”，以检查是否存在可以简化为潜在因子的多个特征。 Bartlett检验的结果很显着(p = 0.00)，这意味着数据可以减少，我们可以继续进行因子分析。

chi_square_value,p_value=calculate_bartlett_sphericity(likert_items)
chi_square_value, p_value

factor = FactorAnalyzer()
factor.fit(likert_items)ev, v = factor.get_eigenvalues()
plt.figure(figsize=(15,10))
plt.plot(range(1, likert_items.shape[1]+1), ev, marker='X', markersize=12)
plt.xlabel('# of Factors')
plt.ylabel('Eigenvalues')
plt.grid()
plt.show()

We can use a scree plot to determine the optimal number of factors. The Kaiser criteria mentions a factor should account for a certain amount of variance for it to be considered a valid factor. We typically set the cut-off at 1 eigenvalue.

我们可以使用碎石图来确定最佳因素数。 凯泽(Kaiser)标准提到因素应考虑一定数量的方差才能被视为有效因素。 我们通常将截止值设置为1个特征值。

factor = FactorAnalyzer(8, rotation='promax', method='ml', impute='mean')factor.fit(likert_items)
factors = factor.loadings_
factors_df = pd.DataFrame(factors, columns=['factor1', 'factor2', 'factor3','factors4','factor5', 'factor6', 'factor7', 'factor8'] index=likert_items.columns)factors_df.style.bar(subset=['factor1', 'factor2', 'factor3',
'factors4','factor5', 'factor6','factor7','factor8'], color='green')

Despite the scree plot of 5 optimal factors, we know the survey was developed to contain 8 latent factors. Finally, the cut-off value for loading scores is rather subjective but we are going to use 0.5.

尽管有5个最佳因素的散布图，但我们知道该调查包含8个潜在因素。 最后，载入分数的临界值相当主观，但我们将使用0.5。

Factor 1: All but one “management satisfaction” item seem to load very highly on this factor. We also have items from “employee_valued”, and “org_environment” loading highly onto Factor 1. Furthermore, it would seem the items assessing the “management satisfaction” construct situate the survey taker in a holistic organizational perspective or mindset when answering the questions. In other words, the individual is asked to think about management holistically. However, when the survey taker comes to the “direct_mgmt_satisfaction” question their perspective is narrowed and despite enjoying the broader organizational leadership their opinion of their direct manager might differ. This might explain why the “direct_mgmt_satisfaction” item loads so low on factor 1.

因素1：除一项“管理满意度”项目外，其他因素似乎都非常重视这一因素。 我们还从“员工价值”和“组织环境”两个项目中高度关注因素1。此外，似乎在评估“管理满意度”构造的项目在回答问题时从整体组织角度或心态定位了调查接受者。 换句话说，要求个人从整体上考虑管理。 但是，当调查接受者提出“ direct_mgmt_satisfaction”问题时，他们的视野就会缩小，尽管享有更广泛的组织领导权，但他们对直接经理的看法可能会有所不同。 这可能可以解释为什么“ direct_mgmt_satisfaction”项加载的系数如此之低。

I would suggest the survey to separate out the management latent construct into broad leadership and direct manager satisfaction. Furthermore, I would strongly suggest the “employee_value” construct needs to be rethought as the items do not load together on any factor.

我建议该调查将管理潜能结构分为广泛的领导和直接的经理满意度。 此外，我强烈建议您需要重新考虑“ employee_value”构造，因为这些项目不会在任何因素上一起加载。

Factor 2: This factor almost perfectly loads onto the “job satisfaction” construct. This would indicate the items have been well developed and are measuring what they indented to measure.

因素2：该因素几乎完美地加载到“工作满意度”结构中。 这将表明这些项目已经开发完善，并且正在衡量它们要衡量的内容。

Factor 3: “Training satisfaction” perfectly loads onto factor 3. However, it is important to mention the item “assessing promotional_opportunities” has a somewhat positive loading. This would make sense as an organization which offers training opportunities for their employees is surely to have a good succession plan in place.

因素3： “培训满意度”完全符合因素3。但是，重要的是要提及“评估proportation_opportunities”项具有一定的积极影响。 对于为员工提供培训机会的组织，一定要制定一个好的继任计划。

Factor 4: Factor 4 seems to correspond with the “team_satisfaction” construct. Although, the “team_culture” item needs to be improved due to its low loading. Items that attempt to assess ambiguous topics such as “culture” often present with low loadings onto their designated constructs. Perhaps focusing the item on one or two specific components of an organizational culture model would definitely help to narrow down the question.

因素4：因素4似乎与“ team_satisfaction”构造相对应。 虽然，“ team_culture”项目由于其低负载而需要改进。 试图评估模棱两可主题(例如“文化”)的项目通常在其指定结构上的负荷较低。 也许将项目重点放在组织文化模型的一个或两个特定组成部分上肯定会有助于缩小问题的范围。

Factor 5: This factor loads great with the “work/life balance” latent construct.

因素5：这个因素在“工作/生活平衡”潜在结构中起着很大的作用。

Factor 6: It is interesting to see that both “team_comm” and “team_culture” load very highly onto factor 6 despite the fact “team_comm” also loads onto factor 4. Because “team_comm” loads highly on two factors, this would indicate the question might be interpreted differently by the survey takers. Perhaps adding some context into the question might help to improve its loading onto factor 4. Finally, simply changing the order of the questions which assess “team satisfaction” might help these two items to load better on factor 4.

因子6：有趣的是，尽管“ team_comm”也加载到因子4上，“ team_comm”和“ team_culture”都非常高地加载到因子6上。受访者可能对它的解释有所不同。 也许在问题中添加一些上下文可能有助于改善因素4的负荷。最后，简单地更改评估“团队满意度”的问题的顺序可能有助于这两项在因素4上的负荷更好。

Factor 7: We are not seeing any identifiable constructs loading onto factor 7.

因素7：我们看不到任何可识别的结构加载到因素7上。

Factor 8: This is the only factor where the “fair_salary” item seems to load. This construct certainly needs additional questions before any recommendations can be made.

因素8：这是“ fair_salary”项目似乎加载的唯一因素。 在提出任何建议之前，此结构肯定需要其他问题。

验证性因素分析(CFA) (Confirmatory Factor Analysis (CFA))

A confirmatory factor analysis is used most often to confirm already validated assessments. For example, you wish to conduct an employee engagement assessment using an off-the-shelf survey purchased from a vendor. It might be a good idea to run a pilot test using the survey on your specific organizational sample of employees. A CFA would be a great tool to confirm the underlying constructs the survey purports to measure but using your sample of employees.

验证性因素分析最常用于确认已经验证的评估。 例如，您希望使用从供应商处购买的现成调查来进行员工敬业度评估。 使用针对您的特定组织样本的调查进行试点测试可能是一个好主意。 CFA将是确认调查所要衡量但使用您的员工样本的基础结构的好工具。

In reality, researchers will often use both factor analyses to validate their assessments. There is nothing wrong with using an EFA to observe purposefully developed constructs. I have found that the results of an EFA can be tremendously helpful in distilling issues with the individual items. Once you have improved the items based on the EFA results the CFA results will typically increase as well.

实际上，研究人员通常会同时使用两种因子分析来验证其评估。 使用EFA观察有目的开发的结构没有错。 我发现，EFA的结果对于提炼单个项目的问题非常有帮助。 一旦您基于EFA结果改进了项目，CFA结果通常也会增加。

model_dict = {'valued_employee': ['promotional_opportunities', 'performance_recognized','feedback_offered', 'coaching_offered'],'mgmt_sati':['mgmt_clear_mission', 'mgmt_support_me', 'mgmt_support_team','mgmt_clear_comm', 'direct_mgmt_satisfaction'],'job_satisfaction': ['job_stimulating', 'initiative_encouraged',
'skill_variety','knowledge_variety','task_variety'],'salary_satisfaction': ['salary_satisfaction'],'team_satisfaction': ['teamwork','team_support', 'team_comm', 'team_culture'],'training_satisfaction': ['job_train_satisfaction', 'personal_train_satisfaction'],'org_condition': ['org_culture', 'grievances_resolution', 'co-worker_interaction','workplace_conditions'],'work_life_balance': ['job_stress','work/life_balance']}model_spec = ModelSpecificationParser.parse_model_specification_from_dict(
    likert_items, model_dict)cfa = ConfirmatoryFactorAnalyzer(model_spec, disp=False)
cfa.fit(likert_items)cfa_factors_df = pd.DataFrame(cfa.loadings_, columns=['factor1', 'factor2','factor3','factors4','factor5','factor6','factor7', 'factor8'],index=likert_items.columns)cfa_factors_df.style.bar(subset=['factor1','factor2','factor3',
'factors4','factor5','factor6','factor7','factor8'], color='orange')

摘要 (Summary)

员工价值 (Employee Value)

The CFA results seem to mostly confirm the results obtained from the correlation matrix, alpha reliability, and EFA. The items assessing the “employee_value” construct certainly need to be polished as there seems to be some confusion about what is being measured. “Performance_recognized” and “feedback_offered” seem to be measuring one underlying construct as their correlation is 0.63 and their loadings on both the EFA and CFA are high. The alpha reliability for this construct is borderline acceptable (0.79) and as we look at additional metrics there is certainly work needed to be done on this construct.

CFA结果似乎主要证实了从相关矩阵，α可靠性和EFA获得的结果。 评估“ employee_value”构造的项目当然需要完善，因为似乎对所测量的内容有些困惑。 “ Performance_recognized”和“ feedback_offered”似乎正在衡量一种基础结构，因为它们的相关性为0.63，并且它们在EFA和CFA上的负载都很高。 此结构的alpha可靠性是可接受的边界(0.79)，并且当我们查看其他指标时，肯定需要在此结构上进行工作。

管理满意度 (Management Satisfaction)

With the exception of “direct _mgmt_satisfaction,” the items measuring management satisfaction are well developed and stands up to the rigorous testing of correlations, alpha reliability, and factor analysis. As mentioned previously, there might be a scale issue with the “direct _mgmt_satisfaction” question compared to the rest of the questions. Survey takers are mostly answering questions regarding the broader leadership construct and this question asks about their direct manager.

除了“直接的_mgmt_satisfaction”外，衡量管理满意度的项目也很完善，可以经受严格的相关性，alpha可靠性和因子分析测试。 如前所述，与其他问题相比，“直接_mgmt_satisfaction”问题可能存在规模问题。 受访者大多是在回答有关更广泛的领导力结构的问题，而这个问题是关于他们的直接经理的。

工作满意度 (Job Satisfaction)

All 4 analyses support the quality of the items assessing this construct. I wouldn’t change a thing :)

所有4种分析均支持评估此结构的项目的质量。 我不会改变任何事情:)

团队满意度 (Team Satisfaction)

This construct had some contradicting loading results when comparing the EFA and CFA. Nevertheless, these contradictions, borderline alpha reliability, and less than stunning correlations among the items point to much needed revision of the questions. I believe the construct of a team is well operationalized but the notion of “team culture” might be interpreted differently among those taking the survey.

比较EFA和CFA时，此结构的加载结果有些矛盾。 但是，这些矛盾，临界的alpha可靠性以及各项之间不够惊人的相关性都表明，急需对该问题进行修订。 我相信团队的构建可以很好地运作，但是“团队文化”的概念在接受调查的人中可能会有所不同。

培训满意度 (Training Satisfaction)

It is important to offer at least 3 questions per construct in order to establish credible reliability and construct validity. The two items measuring this construct are very well written and seem to be assessing one factor. I would like to see additional questions. Kirkpatrick’s training evaluation model can serve as the basis for additional questions.

重要的是，每个结构至少要提供3个问题，以建立可信的可靠性和结构有效性。 衡量此构造的两个项目写得很好，似乎正在评估一个因素。 我想看看其他问题。 柯克帕特里克(Kirkpatrick)的培训评估模型可以作为其他问题的基础。

组织环境 (Organizational Environment)

Unfortunately, much like “employee value” this construct needs major edits. All the metrics resulted in subpar results and from a face-validity perspective, it is simply difficult to mentally encompass these items into one cohesive construct.

不幸的是，这种构造很像“员工价值”，需要进行大量修改。 所有度量标准均得出不及格的结果，并且从脸部有效性的角度来看，仅是很难在精神上将这些项目包含在一个有凝聚力的结构中。

工作与生活的平衡 (Work/life Balance)

Much like “training satisfaction” this construct has performed well but we would like to see at least 3 items per construct.

就像“培训满意度”一样，此构造的效果很好，但我们希望每个构造至少看到3个项目。

工资满意度 (Salary Satisfaction)

This construct was composed of one item and from a practical perspective, it is simply lacking. Even Cronbach’s alpha requires at least 2 questions.

这种构造只包含一个项目，从实际的角度来看，它只是缺乏。 甚至Cronbach的alpha也至少需要2个问题。

结论 (Conclusion)

There is certainly a large amount of work which goes into developing employee surveys. What’s more, when the survey becomes an assessment (ie. cognitive ability or skill test) used in a hiring process the rigor and documentation increases exponentially. I hope you enjoyed a glimpse into some of the work I/O psychologists perform. As always I welcome your feedback.

当然，开展员工调查需要大量工作。 更重要的是，当调查成为招聘过程中使用的评估(即认知能力或技能测试)时，严谨性和文件记录成倍增加。 我希望您对I / O心理学家执行的某些工作有所了解。 一如既往，我欢迎您的反馈。