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JURNAL SPIRITS

Vol. 14, No. 1, November 2023, pp. 14-33 e-ISSN 2622-3236 p-ISSN 2087-7641 https://jurnal.ustjogja.ac.id/index.php/spirit/index

14

DEVELOPMENT AND VALIDATION OF LEARNING AGILITY INSTRUMENT

Arbania Fitriani ^1*, Resa Pahlawan², Jamiu Temitope Sulaimon³, Wihana Kirana Karya⁴, Sumaryono⁵, Reni Rosari⁶, Dominikus David Biondi Situmorang⁷

1,2 University of Esa Unggul, Indonesia

3 University of Ilorin, Kwara State, Nigeria

4,5,6 University of Gadjah Mada, Indonesia

7 Atma Jaya Catholic University of Indonesia

[email protected]^1*; [email protected]²; [email protected]³; [email protected]⁴; [email protected]⁵; [email protected]⁶; [email protected]⁷

Received: August 25, 2023 Revised: October 19, 2023 Accepted: November 20, 2023

*Correspondent Author

Introduction

Executing complicated initiatives requires high-potential workers who are open, willing to learn, and flexible (Hallenbeck & De Meuse, 2008). According to Charan, Hewitt, and SHRM (2006), a crucial part of talent management is creating a structured procedure for evaluating and identifying high-potential personnel.

Learning agility is one characteristic that has drawn much attention as a predictor of high potential.

KE Y WOR D S A B STR A K

Cognitive Perspective Learning Agility Psychometric Police Woman

This study aimed to develop a learning agility measuring instrument based on De Meuse's theory (2015) with a sample of Government Employees from the National Police of the Indonesian Republic and members of Polda Metro Jaya (N=60). The development of an Indonesian version of the learning agility measuring tool is important to adapt the construct according to the character of Indonesian culture so that it can be used widely and practically by organizational management. The data was collected using an online questionnaire survey distributed via Jotform. The stages of developing measuring instruments start from construct operationalization, item development, readability testing by expert judgment, pilot tests to eliminate invalid items, and field studies. The results of the statistical test using a differential power test where the researcher aborted 35 items in the pilot study, which were initially 95 items, so that the items that could be continued to the next test stage in a large sample totaled 60 items, then from the average item reliability value has a value per -item ≥ 0.9, this figure shows consistent numbers using the same measuring instrument (test-retest reliability) when testing on small samples and also large samples. Construct validity was assessed using item homogeneity techniques, employing the Product Moment correlation, revealing p-values ≤ 0.05 for all items. Additionally, the Pearson correlation values for all things were ≥ 0.279 (r-table) with N=50. Therefore, all items in the second phase of the trial can be deemed valid and suitable for use.

Moreover, positive correlations were observed among dimensions. The results of the factor loading test indicated that the Standardization Estimate for each dimension exceeded 0.71, confirming that each dimension contributes significantly to the latent construct.

This is an open-access article under the CC–BY-SA license.

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Vol. 14, No. 1, November 2023, pp. 14-33 p-ISSN 2087-7641

Fitriani et al (Development And Validation Of Learning …..) 15

The idea of learning agility answers the question of what personal qualities are required for someone to get the most from such a developmental experience. All employees have talent, but each person's skill is unique in type and quantity. Learning agility is a multifaceted idea, similar to the complexity of developmental activities.

This skill area connects to the notion of learning agility. Learning agility is the capacity and willingness to quickly absorb and apply new information to succeed in novel and challenging leadership settings. Everyone needs to develop their agility. Furthermore, if we so want, we can further enhance it. As a result, learning from complex and demanding work experiences involves more than just having a learning objective (Hallenbeck & De Meuse, 2008).

According to Lombardo and Eichinger (2000), learning agility is the readiness and capacity to absorb knowledge from experience and then use it to succeed under novel circumstances or for the first time. Over the past few years, learning agility has gained wide acceptance as a strategy for helping human resource specialists and organizational executives make talent decisions. There are a few opinions about learning agility, how to evaluate it, when to utilize it, and how closely it relates to other concepts (De Meuse, 2015).

Authors intend to develop learning agility assessment tools from theory by drawing from diverse studies that address the concept of learning agility (De Meuse, 2015). The construction of this assessment tool is to assess learning agility so that the measurement outcomes can be effectively applied to identifying, selecting, and developing high-potential talent and selecting people for leadership positions. Learning agility can also be used to find, select, and develop high-potential individuals as well as executives, managers, and supervisors.

The need for organizations to uncover and develop people who can continually let go of knowledge, views, and ideas that are out-of-date and learn new ones has led researchers to build learning agility measurement tools (Ferry, 2011). It is critical to stay current with learning and development trends in the modern world, where change is the only constant. In order to remain competent in their current roles and ultimately succeed in an organization, people must continue to develop themselves (Burke, 2018). Individuals must be capable of growing in their abilities amid a development process that moves at such a rapid pace. Learning agility is a psychological measurement instrument that must gauge an individual's development to determine whether a person is growing. Learning agility may be one of the abilities that human resource professionals perceive most important for identifying qualified candidates for the organization. Therefore, industrial parties or human resource professionals can use the established measurement instruments.

When choosing employees, maximizing their potential, and keeping them on board, businesses or organizations should pay particular attention to their learning agility. Agility in learning can also have a favorable effect on an organization's performance. According to DeRue et al. (2012), Eichinger and Lombardo (2004), and Smith (2015), learning agility is linked to improved performance and promotions in addition to predicting the potential and future outcomes of leaders and members. The correct future leaders, essential to organizational success, can be found and developed using learning agility (DeRue et al., 2012; Silzer & Church, 2009). The outcomes of developing the learning agility concept can be a valuable resource when hiring talent,

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16 Fitriani et al (Development And Validation Of Learning …..)

such as whether to consider internal candidates for management promotions, whom to include in high- potential leadership programs, or whether to bring in external leaders. The findings from this analysis, however, offer diagnostic advice for the areas of leadership that still require work for development purposes.

Research on learning agility is continually expanding. Since readability measures are essentially not acceptable everywhere in the world, numerous studies have generated new instruments to modify the characteristics of learning agility (Gravett & Caldwell, 2016). Access to the learning agility construct from De Meuse's (2015) theory may be found at https://thetalentx7.com/. However, from a readability metric that is not acknowledged globally, as well as the fact that little research has been carried out in Indonesia. In order to adapt the learning agility construct from De Meuse's (2015) theory to Indonesian culture and language, the researcher plans to do so. The reason researchers developed the concept of learning agility because focus organizations must now shift to discover and develop individuals who can continually unleash skills, perspectives, and ideas that are no longer relevant and learn new ones (Ferry, 2011). Progress high-speed technology also emphasizes personal improvement and learning agility. In today's world, where change is the only constant thing, keeping up with learning trends and development is important. People must continue to develop themselves; if they do, they may become competent in their current and eventual position, struggling to survive in an organization (Burke, 2018). Research has been carried out on the development of learning agility measuring tools previously by Nurnaifah Selvia Wardhani, Marina Sulastiana, and Rezki Ashriyana in July 2022 by developing Lombardo's and Eichinger (2000) theory, which contains 4 (four) dimensions of learning agility, namely: people agility, results agility, mental agility, and change agility on the subject of employees to be able to increase organizational agility. Sample in this research are individuals who are fresh graduates, job seekers, or employees in a government agency, State-Owned Enterprise (BUMN), or private institutions aged 18 to 45 years. Number of employees filling in in total there are 235 employees. This research wants to continue previous research by using the latest theory from De Meuse (2015) with seven dimensions, where the construct that previous researchers have developed is still four dimensions using the theory of Lombardo and Eichinger (2000).

De Meuse's (2015) theory was used to produce the learning agility construct, which has seven (seven) components: the cognitive perspective, interpersonal acumen, change alacrity, drive to excel, self-insight, environmental mindfulness, and feedback responsiveness. A recent research finding that supports the significance of including measurements of "environmental concern" and "feedback response" in a thorough assessment of learning agility was the learning agility construct from De Meuse's (2015) theory (Hulsheger et al., 2013; Sheldon et al., 2014). Table 1 below shows the information.

Table 1

Definition of the Learning Agility Dimension

Dimension Indicator

Cognitive Perspective People can think critically and strategically, approach organizational challenges from a high-level and comprehensive viewpoint, and

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concentrate on various inputs rather than just one or two functional or technical perspectives.

Interpersonal Acumen Successfully communicate with others. Recognise others' intentions, values, and objectives, as well as their strengths and limitations.

Encourage people to do well by giving them confidence.

Change Alacrity Successfully communicate with others. Recognize others' intentions, values, and objectives, as well as their strengths and limitations. Inspire others with confidence and motivate them to do their best, be willing to try new things, and continually look for hazardous (and occasionally innovative) methods to handle a situation.

Drive to Excel Set goals for one's success and the accomplishments of the organization. Intelligent problem-solver who consistently produces excellent results in novel and unproven positions.

Self Insight Individuals have personal goals relating to the workplace. They are aware of their strengths and shortcomings, as well as of their views, values, and sentiments.

Environmental Mindfulness Closely monitors the external environment, fully supports changes in job responsibilities and organizational tasks, and creates issues due to the changing environment by being slow to recognize and appropriately manage emotions.

Feedback Responsiveness Ask for, pay attention to, and receive comments from others individually while carefully weighing the advantages. Take corrective action in response to feedback to enhance performance.

Method

This study aims to create a tool for measuring the learning agility construct from De Meuse's (2015) theory. This study falls under quantitative research in development (R&D). According to Azwar (2014), quantitative research emphasizes its analysis in the form of numerical data (numbers) processed using statistical methods. In research, this form of development focuses on creating or validating specific items and evaluating their efficacy (Sugiyono, 2018). This development research aims to learn more about valid and reliable measuring tools for learning agility. In this study, there were 50 participants in the large sample trial and 20 people in the pilot study. The National Police (Government Employees) and the Polda Metro Jaya Police Department in Indonesia are the samples.

The sample was chosen based on the requirements of having a two-year service history and having worked permanently. The simplicity of getting data is the basis for the decision to take samples from police facilities where the research results will be used at the institution studied. Non-probability sampling utilizing convenience sampling methods is the sampling approach employed. Sugiyono (2018) claims that convenience sampling is a sampling approach based on chance, meaning that workers who randomly cross paths with researchers may be employed as samples provided it is determined that the individual is suitable as a data source. The data was gathered using Jotform to distribute an online questionnaire survey.

Table 2 Demography

Data Characteristic n %

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Gender Male 32 62.7%

Female 18 35.3%

Age 21-25 Years 20 40%

26-30 Years 15 30%

31-35 Years 11 22%

36-40 Years 3 6%

41-45 Years 1 2%

Education Post Graduate (S2) 3 5.9%

Graduate (S1) 23 45.1%

High School 24 49%

Occupation National Police 12 24.5%

Government Employee 38 75.5%

Working Period 1-5 Years 19 38%

6-10 Years 17 34%

11-15 Years 12 24%

16-20 Years 2 4%

The research is divided into the following phases at figure 1:

The research is divided into the following phases:

In the first stage, the researcher decides which constructs will be created and validated by looking through relevant literature and considering the demand for testing tools in psychological assessment.

The researcher determines the learning agility construct after gathering data on staff development requirements in steering the future.

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Stage 2, Researchers did an integrated examination of the literature on learning agility. The literature review was part of an integrated methodology that enables the generation of perspectives on novel conceptual models. Integrated literature research is a fundamental technique for reasoning and explanation through data analysis. The theory and relevant information concerning current phenomena are then clarified. No matter how extensive the research, an integrated literature review might offer a fresh perspective or way of thinking.

Stage 3, since the sample to be measured is chosen: It is necessary to identify the subject to be measured before creating a questionnaire. The participants in this study are already employed employees.

Stage 4, involves identifying the components of the Learning Agility construct from De Meuse's (2015) theory, which includes seven indicators: cognitive perspective, Interpersonal Acumen, change alacrity, self-insight, drive to excel, environmental mindfulness, and feedback responsiveness.

Stage 5, Gathering Items. The questionnaire items are prepared and divided into favorable and unfavorable items. Where are the declarations about the favorable and unfavorable items? Favorable items operationally define behavior that supports the subject's behavioral features. In contrast, unfavorable items clash with or do not support the behavioral qualities that the subject's indicators on the subject want.

Stage 6, Creating a score or rating for each question item: A Likert scale will be used to process data from the questionnaire, specifically to give the weight of the assessment of each item. The variables that are measured are converted into indicators or variables.

Stage 7, Following creating items in the learning agility construct, the researchers conducted a readability test with 20 students from Esa Unggul University's Faculty of Psychology. An expert panel of three psychologists familiar with the development of psychological assessment methods and the concept of learning agility was used for content validity. Twenty of the 95 items created were revised. The 60 elements are then entered into the questionnaire on learning agility, which is available as an online survey through the Jotfrom platform.

Stage 8, Conducting a Survey/Pilot Study. Following Arikunto (2013), this study is required to determine whether the study's questionnaire is practicable. In order to gather data for this study, a sample of 20 respondents was used.

Stage 9, Item Validity and Reliability Test. The consistency of several measures or measuring tools is the subject of a reliability test. Reliability tests can take measurements made with the same measuring tool yielding the same results (test with retest) or, for more subjective measurements, whether two raters produce results reasonably close to each other (inter-rater reliability). Additionally, a validity test is performed to demonstrate how well the measurement tool employed in a measurement measures what is being measured. A test is said to have high validity if it fulfills its intended measurement purpose or produces precise and accurate measurement results.

Stage 10, Establishing Measurement Instrument Standards: To interpret the quantitative

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Vol. 14, No. 1, November 2023, pp. 14-33 p-ISSN 2087-7641

data obtained as qualitative, the researcher creates measuring instrument norms from the learning agility construct after getting valid and reliable item items.

Result

The statistical information from the questionnaire results from 50 participants using the SPSS 25 program. Testing of data included:

Normality Test

The normality test in this study used the Kolmogorov-Smirnov method (K-S test). To determine the normality of the data tested

Table 3

Normality Data

Test of Normality

Learning Agility

N 50

Normal Parameters^a.b Mean 277.52

Std.Deviation 29.519

Most Extreme Differences Absolute 0.120

Positive 0.120

Negative -0.66

Test Statistic 0.120

Asymp. Sig. (2-tailed) 0.069^c

From the results of the Normality test, the value of Asymp. Signature. (2-tailed) namely: 0.069

≥ 0.05, so it can be concluded that the data is normally distributed.

Homogeneity Test

The homogeneity test uses the One-Way ANOVA test with a level of 5% or looks at the significance value.

Table 4

Homogeneity Data

Test of Homogeneity of Variances Levene

Statistic df1 df2 Sig.

Learning

Agility Based on Mean 6.970 1 48 0.011

Based on Median 7.078 1 48 0.011

Based on Median and

with adjusted df 7.078 1 39.74

1 0.011

Based on trimmed mean 6.987 1 48 0.011

From the results of the homogeneity test, the Asymp. Sig. (2-tailed) namely: 0.011 ≥ 0.05 so that the data is homogeneous.

Table 5 Data ANOVA

ANOVA Learning Agility

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Sum of

Squares df Mean

Square F Sig.

Between

Groups 14522.605 1 1452.605 1.690 0.200 Within

Groups 412455.875 48 859.289 Total 426998.480 49

From the results of the ANOVA test, the value of Asymp. Sig. (2-tailed) namely: 0.200 ≥ 0.05 so that the data is, on average, the same.

Item Discriminating

Following the completion of the precondition tests, the normality and homogeneity tests—another item test—were conducted. The item differential power test aims to choose items whose measuring function matches the test's (Azwar, 2012). Test items will be eliminate if they are deemed low or poor quality. The researcher used a sample of 20 participants in a pilot study to examine the items' differential power. The primary goal of the pilot study was to evaluate the questionnaire's value as a survey tool for researchers and respondents (Hartono, 2010).

Testing the discriminating power of item or item discriminating power in the learning agility construct is carried out by correlating the score of each item with the total score using the Pearson Product Moment correlation technique. According to Azwar (2012), the criteria for selecting items based on item correlations use the rix ≥ 0.30.

From the results of the Pearson Product-Moment correlation test, there are several items with rix ≤ 0.30, namely items: 9, 18, 22, 23, 24, 27, 28, 29, 33, 39, 43, 44, 46, 47, 48, 52, 60, 64, 66, 67, 69, 72, 73, 75, 76, 77, 78, 81, 83, 85, 88, 90, 91, 93, 94. So that the item is no longer used in the next test.

Kaiser Meyer Olkin and Bartlett’s test (KMO) Test

The KMO test and Bartlett's test are factor analysis tests used to identify the structure of the correlation relationship between questions (indicators) so that the relationship between latent dimensions is identified without knowing the theoretical basis behind them. The cut-off value of the Kaiser Meyer Olkin Measure of Sampling Adequacy ≥ 0.5.

Table 6

Kaiser Meyer Olkin Measure of Sampling Adequacy Data

Kaiser Meyer Olkin Measure of Sampling Adequacy

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.815 Bartlett’s Test of Sphericity

Approx. Chi-Square 235.569

df 21

Sig. 0.000

From the results of the KMO MSA test, it obtained a value of 0.815 ≥ 0.5. Also, it obtained Bartlett's Test of Sphericity value of (Sig.) 0.000 ≤ 0.05, so factor analysis can be done to see the validity of the measurements in this study.

After the KMO and Bartlett's tests were carried out, the researcher continued with the item validity test, which aimed to measure whether the data obtained after the research was

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valid data or not. Validity indicates the degree of accuracy between the data that occurs on the object and the data collected by the researcher. In testing the validity, the researcher carried out 2 (two) times validity testing processes on small samples and large samples; the reason for doing 2 (two) times the testing process was that researchers wanted to see the measurement results with the degree of accuracy between the data that occurred on the subject and the data collected by researchers.

The scale's items can be deemed invalid if they have a total item probability score of less than 0.5. The scale's items are valid if the Pearson product-moment value has a total score probability of less than 0.5.

Table 9

Pearson Product Moment between Item and dimension (Pilot Test)

Cognitive Perspective r p Interpretation

Item 1 0.607 0.005 Valid

Item 2 0.681 0.001 Valid

Item 3 0.584 0.007 Valid

Item 4 0.688 0.001 Valid

Item 5 0.597 0.005 Valid

Item 6 0.627 0.003 Valid

Item 7 0.799 0.000 Valid

Item 8* 0.429 0.059 Not valid

Item 10 0.690 0.001 Valid

Item 11 0.504 0.023 Valid

Item 12 0.808 0.000 Valid

Item 13 0.664 0.001 Valid

Item 14 0.715 0.000 Valid

Item 15 0.675 0.001 Valid

Interpersonal Acumen r p Interpretation

Item 16 0.733 0.000 Valid

Item 17 0.824 0.000 Valid

Item 19 0.733 0.000 Valid

Item 20* 0.359 0.120 Not valid

Item 21 0.499 0.025 Valid

Item 25 0.688 0.001 Valid

Item 26 0.735 0.000 Valid

Item 30* 0.362 0.117 Not valid

Change Alacrity r p Interpretation

Item 31 0.696 0.001 Valid

Item 32 0.648 0.002 Valid

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Item 34 0.853 0.000 Valid

Item 35 0.703 0.001 Valid

Item 36 0.879 0.000 Valid

Item 37 0.808 0.000 Valid

Item 38 0.818 0.000 Valid

Item 40 0.843 0.000 Valid

Item 41 0.885 0.000 Valid

Item 42 0.727 0.000 Valid

Item 45 0.580 0.007 Valid

Drive to Excel r p Interpretation

Item 49 0.763 0.000 Valid

Item 50* 0.333 0.152 Not valid

Item 51 0.595 0.006 Valid

Item 53 0.557 0.011 Valid

Item 54 0.688 0.001 Valid

Item 55* 0.432 0.057 Not valid

Item 56 0.549 0.012 Valid

Item 57 0.622 0.003 Valid

Item 58 0.555 0.011 Valid

Item 59* 0.403 0.078 Not valid

Self-Insight r p Interpretation

Item 61 0.463 0.040 Valid

Item 62 0.509 0.022 Valid

Item 63 0.478 0.033 Valid

Item 65 0.540 0.014 Valid

Item 68* 0.422 0.064 Not valid

Item 70 0.954 0.000 Valid

Environmental Mindfulness r p Interpretation

Item 71 0.568 0.009 Valid

Item 74 0.737 0.000 Valid

Item 78 0.597 0.005 Valid

Item 79* 0.347 0.133 Not valid

Item 80 0.572 0.008 Valid

Item 82 0.636 0.003 Valid

Item 84 0.531 0.016 Valid

Feedback Responsiveness r p Interpretation

Item 86 0.832 0.000 Valid

Item 87 0.649 0.002 Valid

Item 89 0.788 0.000 Valid

Item 92 0.494 0.027 Valid

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Item 95 0.696 0.001 Valid

Table 10

Pearson Product Moment between Item and dimension (field test)

Cognitive Perspective r p Interpretation

Item 1 0.740 0.000 Item valid

Interpersonal Acumen r p Interpretation

Change Alacrity r p Interpretation

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Drive to Excel r p Interpretation

Self-Insight r p Interpretation

Environmental Mindfulness r p Interpretation

Feedback Responsiveness r p Interpretation

Table 11

Pearson Product Moment between dimension and total score (Pilot Test)

Dimension r p Interpretation

Cognitive Perspective 0.938 0.000 Positive correlation Interpersonal Acumen 0.750 0.000 Positive correlation Change Alacrity 0.740 0.000 Positive correlation Drive to Excel 0.905 0.000 Positive correlation

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Self-Insight* -0.088 0.712 Negative correlation Environmental Mindfulness 0.787 0.000 Positive correlation

Feedback Responsiveness 0.710 0.000 Positive correlation

Table 12

Pearson Product Moment between dimension and total score (Field Test)

Dimension r p Interpretation

Cognitive Perspective 0.874 0.000 Positive correlation Interpersonal Acumen 0.779 0.000 Positive correlation Change Alacrity 0.842 0.000 Positive correlation

Drive to Excel 0.889 0.000 Positive correlation

Self-Insight 0.632 0.000 Positive correlation

Environmental Mindfulness 0.796 0.000 Positive correlation Feedback Responsiveness 0.720 0.000 Positive correlation

Using the findings of Product-Moment Validity testing, calculate each dimension's value and the dimension's value using the total value of the sum of the dimensions between the items.

The validity results between the small sample and the large sample are different. The researcher obtains multiple items with a probability value 0.05 from testing with a small sample, including items: 8, 20, 30, 50, 55, 59, 68, and 79. The Items were thus classified as invalid. Additionally, a dimension called Self Insight has a bad association with the concept of learning agility.

However, from the results of item revisions and validity testing again with a large sample, all items' Product-Moment Validity values have a Sig value. (2-tailed) ≤ 0.05. Moreover, it can also be seen from the Pearson correlation value of all items ≥ 0.279 (r-table) of N=50. So that all items can be said to be valid and feasible to use, it can also be seen that the dimensions are positively correlated and can be included in the learning agility construct.

Factor Analysis Test

After conducting item validity through the Pearson moment product, 60 items are feasible and valid. Then, the researcher conducted a First Order Confirmatory Factor Analysis (CFA) test to identify the correct numerical model and explain the relationship between a set of items and the construct measured by the items. These items become indicators of the construct, measured by looking at the low measurement error values and high component factor loadings.

According to Hu and Bentler (1999), a good structural model must be assessed: X2 and its relationship with df, one absolute fit index (e.g., GFI, RMSEA, or SRMR), one incremental index fit (e.g., CFI or TLI), one goodness-of-fit index (GFI et al.), and one badness-of-fit index (RMSEA and SRMR).

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Figure 2: Factor Analysis Diagram

Figure 1 shows the correlation of the estimated numbers from the loading factor of the learning agility construct with its dimensions.

Table 13 Model fit result

Variable X² df p NNFI CFI RMSEA

Learning agility 49.625 14 ≤ 0.001 0.808 0.872 0.226

From the Goodness of fit Index measurement result at table 13, most of the Goodness of fit criteria do not meet the cut-off value. This value follows the opinion of Ghozali (2006), which states that some Goodness of Fit parameters do not meet the requirements. It can be seen from the other parameters. If most of the parameters meet the requirements, then it can be stated that the model meets the Goodness of Fit assumption.

Table 14

Factor loading result

Factor Indicator Estimate p Std.Est (all)

Learning agility

Cognitive Perspective 6.315 ≤ 0.001 0.812 Interpersonal Acumen 3.127 ≤ 0.001 0.729 Change Alacrity 4.620 ≤ 0.001 0.814 Drive to Excel 5.810 ≤ 0.001 0.929

Self-Insight 1.872 ≤ 0.001 0.637

Environmental Mindfulness 3.159 ≤ 0.001 0.886 Feedback Responsiveness 2.163 ≤ 0.001 0.776

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The results of the factor loading test show that the Standardization Estimate All numbers for each dimension are ≥ 0.71. According to Tabachnick and Fidell (2007), the factor loading number ≥ 0.71 shows excellent dimensional results.

Measurement Norm

At the measuring level, the created learning agility construct generates numerical scores.

However, it can only generate categories or groups of scores at the ordinal level regarding interpretation. The interpretation of psychological scale scores is normative, which means that the score is used as a parameter to compare the score to a mean theoretical population norm, allowing for the qualitative interpretation of quantitative measurement results. The norms employed in this study are within group norms, broken down into three categories: high, medium, and low. The different parts are the range, the hypothetical mean, the hypothetical standard deviation, and the maximum hypothetical value. This norm was created using the Azwar formula (2012).

The normal curve is used to categorize the research's norm. The learning agility construct consists of 60 items, each with a response option of 5 on a Likert scale.

Figure 2

Measurement Norm of Learning Agility

Table 15

Table of Norms Categorization Learning agility measuring tool

Low : 0 – 140

Medium : 140 – 220

High : 220 – 300

Discussion

The bell curve categorizes the research's norm. The learning agility construct has 60 elements with a response range of 5 options. The data in psychological measurement tools are valuable as the foundation for predictions or as part of the fundamental considerations for decisions about the subject through interpretation and diagnosis (Azwar, 2022). Psychological measuring tools, which contain the dimensions and signs of developing individuals, can be

Low

Medium

High

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tested to determine whether a person is developing. According to Arikunto (2013), using samples with non-probability techniques will influence the ability of a measuring instrument to be used on different samples. For this reason, the numerical standards resulting from this research can only be used for the institution where the data was collected.

The data were determined to be homogenous and normally distributed based on the results of the normality and homogeneity tests, allowing parametric statistical analyses using regression techniques.

The discrimination or item differential test revealed that 35 items were eliminated because the rix value was less than 0.30, so they cannot be added to the subsequent test.

From the Kaiser Meyer Olkin Measure of Sampling Adequacy (KMO) test, a value of 0.815 ≥ 0.5 was obtained, and a Bartlett's Test of Sphericity value of (Sig.) 0.000 ≤ 0.05 was obtained, so factor analysis in this study could be carried out.

Furthermore, a reliability test is carried out to see how far the measurement results remain consistent if done more than once and still show consistent results using the same measuring instrument during the test. In testing the reliability, the researchers conducted 2 (two) reliability testing processes on small and large samples. The reliability test results, show a value from the two tests with an average Cronbach's alpha value of 0.9, where the results are said to be reliable; the reliability coefficient value is close to 1.00.

Results of the Product Moment correlation are calculated between items using the sum of each dimension as well as the value of each dimension using the total value of the sum of the dimensions. The validity results between the small sample and the large sample are different.

The researcher obtains multiple items with a probability value > 0.05 from testing with a small sample, including items: 8, 20, 30, 50, 55, 59, 68, and 79. Thus, the items are classified as invalid.

Additionally, a dimension called Self Insight has a bad association with the concept of learning agility.

From the results of item revisions and validity testing again with a large sample, the Product-Moment Validity values on all items have a Sig value. (2-tailed) ≤ 0.05. Furthermore, it can also be seen from the Pearson correlation value of all items ≥ 0.279 (r-table) of N=50. So that all items can be said to be valid and feasible to use, it can also be seen that the dimensions are positively correlated and can be included in the learning agility construct.

To determine the correct numerical model and clarify the relationship between a collection of items and the construct being measured by the items, the researcher then ran a First Order Confirmatory Factor Analysis (CFA) test. According to the factor loading test results, each dimension's Standardisation Estimate All numbers are between 0.71 and 0.73. The factor loading value 0.71, according to Tabachnick and Fidell (2007), displays outstanding

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dimensional findings.

Based on the overall results of psychometric property testing, where it is proven that the learning agility measurement dimensions constructed from the items in this research are proven to be valid and reliable where all dimensions are positively correlated with loading factor values >0.71, it can be concluded that the learning agility measuring tool can be used for other purposes. Practical matters such as management decision-making or research needs with similar themes.

Conclusion

Small and large samples were used to construct measuring instruments from the learning agility construct. Based on the test results of a set of items from the power difference test, the researcher eliminated 35 of the original 95 items, bringing the number of items that could be tested further to 60. The items' reliability values showed that each item had a value of less than 0.9, with a mean of 0.946 on small samples. This value shows that the items are in the very high correlation category. The researcher revises the items on a large sample, hoping that the item revisions and reliability testing again show consistent numbers using the same measuring instrument when testing small and large samples. After revising the items on a large sample, the researcher conducted another reliability test with the results of Cronbach's Alpha increasing by 0.968. The conclusion is that the items tested are reliable or trusted and can be used in data collection.

The researcher next performed a validity test using the findings of the reliability test. The researcher used a small sample size and a large sample size to test the dependability of the carried conducted validity. Based on the findings of validity testing, which involved adding up the values of each dimension's component elements and the value of each dimension when added together. The validity results between the small sample and the large sample are different. The researcher obtains multiple items with a probability value > 0.05 from testing with a small sample, including items: 8, 20, 30, 50, 55, 59, 68, and 79. So, the item is invalid.

Furthermore, there is also a Self Insight dimension that negatively correlates with the learning agility construct. However, from the results of item revisions and validity testing again with a large sample, all items' Product-Moment Validity values have a Sig value. (2-tailed) ≤ 0.05.

Moreover, it can also be seen from the Pearson correlation value of all items ≥ 0.279 (r-table) from N 50. So that all items can be said to be valid and feasible to use, it can also be seen that the dimensions are positively correlated and can be included in the learning agility construct.

The researcher ran the First Order Confirmatory Factor Analysis (CFA) test through the goodness of fit test using the results of the item validity test that had been acquired. The

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outcomes revealed that while they nearly complied with the established restrictions, they still needed improvement. However, the results of the factor loading values included some excellent value indexes, as the researchers could see from other indications, particularly factor loading. According to the CFA analysis test findings, the conclusion is that the measuring tool developed for De Meuse's (2015) theory complies with basic psychometric principles.

SUGGESTION

Based on the validity and reliability testing results, we found that the Items were valid and reliable. However, this research has limitations where the number of samples is still relatively small and only came from police officers with a minimum of 2 years of work experience. For this reason, we suggest that future research increase the number of samples and then conduct convergent and divergent validity tests, considering that the validity of this study only looks at homogeneity items.

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