Advanced Theories Of Personality A5

Discussion Questions I

All assignments MUST be typed and double-spaced, in APA style and must be written at graduate level English. The content, conciseness and clarity of your answers will be considered in the evaluation of your work. You must use and integrate the material presented in the course text and cite your work according to APA format. Use of outside resources can be used to enhance the text information, but cannot replace the text.

Respond to each question in 1-1 ½ pages per question.

Total assignment should be 4-6 pages total plus a Title and Reference Page

Do not copy the questions in your responses. See APA style on how to create Topic Headings.  Suggested Topic Headings follow each question.  You may use them or create your own.

 

Question One: The text discusses three main approaches to personality research:  case studies, experimental studies conducted in laboratory settings, and correlational studies.  If you were to conduct a research study today, which approach would you use and why?

Suggested Topic Heading:   Personality Research

 

Question Two: It has been said that psychoanalytic theory suffers from a number of cultural biases due to the limitations in kinds of patients seen and the Victorian era from which the concepts were originally derived. Which concepts or parts of the theory do you think could become a particular target for arguments of cultural bias?

Suggested Topic Heading:  Cultural Bias in Psychoanalytic Theory

 

Question Three: As you will see in subsequent chapters, many personality theorists developed ideas that differed markedly from those of Freud and the various neo-Freudian theorists.  What aspects of Freudian theory would you “least want to lose” in such developments – i.e., which features seem so important that they should be taken into account by any other personality theory?

Suggested Topic Heading: Positive Aspects of Freudian Theory

 

Question Four: Rogers proposed that the fundamental human motive is self-actualization: a positive, growth-oriented human motive.  That sounds like a very nice idea. And it is easy to think of cases in which people seem to be striving toward self-actualization.  But it naturally raises the question of how, in Rogerian theory, one could explain the personality of people who seem oriented toward evil rather than positively-oriented growth.  In other words, what about Hitler?  Mass murderers? Etc. How could one posit a self-actualization theory in the face of such cases?

Suggested Topic Heading:  Self-Actualization and Evil

Assignment Outcomes

Distinguish the major theories of personality

Contrast historical and current views of personality

Integrate evidence based treatment interventions

Combine current research to assessment and technique

Identify legal, ethical issues in theories of personality and psychotherapy

Examine issues of culture and diversity in theories and application

Must have turn it in

Cervone, D. & Pervin, L.A.   (2016).   Personality theory and research.   (13th ed.).   New York , NY   Wiley, John & Sons, Inc.    ISBN 9781118976296

Evaluate The Psychometric Properties Of A Psychological Assessment On DEPRESSION.

Evaluation of the Psychometric Properties of the Test

Your evaluation of the test should include the following areas of consideration:

1. Purpose of Test: What is the purpose of the test (personality, screening, diagnosis, marriage counseling, placement for children, etc.)?  Who developed it and why?  How is it used?

2. Type of Test/Scoring: What kind of items does the test utilize (T/F, likert, etc)?  How is the test scored? What kind of score(s) do respondents receive (percentile rank, z score, T score, total and /or subscale scores?)

3. Normative Sample: Describe the normative sample (including the number of participants and their know demographic characteristics). Indicate whether or not the normative sample is adequately representative of the intended test-takers.

4. Administration: How is the test administered?  Paper and pencil? Computer based?  Who can purchase/administer the test (i.e., minimum qualifications)?

5. Reliability: Correctly use terms from the textbook/course materials to define the types of evidence for reliability reported in the review articles, and provide the specific numerical values of the reliability statistics. If no reliability data are provided, then explain what type of evidence for reliability you would need in order to fully evaluate the test.

6. Validity: Correctly use terms from the textbook/course materials to define the types of evidence for validity reported in the review articles, and provide the specific numerical values of the validity coefficients. If no validity data are provided, then explain what type of evidence for validity you would need in order to fully evaluate the test.

 Justification for Selecting the Test

Your justification for selecting the test should include the following areas of consideration:

1. Explain why you selected this test for review. Specifically, explain how the test is relevant to what you are doing now and/or your future career plans.

2. Explain how the test that you chose fits in with the goals and responsibilities of Christian professionals who might utilize the test. Choose at least one scriptural citation from the bible(an actual verse) THIS IS A REQUIREMENT to support your argument.

Assignment Parameters

1. Use of current APA formatting guidelines is expected thoughout your paper.

2. Your assignment should include an APA-formatted Title Page.

3. DEPRESSION psychological test to evaluate, and you will find two (3) articles(attached) from the Mental Measurements Yearbook (MMY) database that assess the psychometric properties of the DEPRESSION.

4. In your evaluation of the test, you will use information gathered from both review articles to write a comprehensive evaluation of the test.

· The written evaluation of the test itself (the body of your paper) should be 2-3 pages in length.

· Your paper should be written in a scholarly writing style with a formal, college-level tone that utilizes appropriate grammar, diction, spelling, and punctuation.

· Your paper should appropriately utilize in-text citations of all sources (2 review articles and 1 scriptural citation), and citations should be presented in accurate APA format.

5. Your paper will include an APA-formatted References Page.

· Your references page should include the reference information for the 2 review articles that you obtained from the MMY.

You MUST REFERENCE the testbook as a reference if you cite information from the textbook when writing your paper. Cohen, R. J. & Swerdlik, M. E. (2017). Psychological testing and assessment: An introduction to tests and measurement (9th ed.). Boston, MA: McGraw-Hill. ISBN: 9781259870507.

· Make sure that you reference every source that you cite and that you cite every source that you reference. (Referencing the Bible is not required in APA-formatted manuscripts, but you can choose to reference it if you would like. Citing the Bible is required.)

EBSCO Publishing Citation Format: APA (American Psychological Assoc.): NOTE: Review the instructions at http://support.ebsco.com.ezproxy.liberty.edu/help/? int=ehost&lang=&feature_id=APA and make any necessary corrections before using. Pay special attention to personal names, capitalization, and dates. Always consult your library resources for the exact formatting and punctuation guidelines.

References Reynolds, W. M., & Kobak, K. A. (1998). Reynolds Depression Screening Inventory. Retrieved from

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Reynolds Depression Screening Inventory Review of the Reynolds Depression Screening Inventory by MICHAEL H. CAMPBELL, Director of Residential Life, New College of University of South Florida at Sarasota, Sarasota, FL: TEST COVERAGE AND USE. The Reynolds Depression Screening Inventory (RDSI) is a paper- and-pencil self-report measure based on the well-known Hamilton Depression Inventory (HDI), which in turn was adapted from the classic Hamilton Depression Rating Scale. The test was designed to provide a brief, convenient, and cost-effective screening for the severity of depressive symptoms. Items for the RDSI were drawn from the 32-item HDI and selected to provide broad coverage of the DSM-IV criteria for Major Depressive Disorder, as well as to maximize scale homogeneity. The authors make clear that the RDSI is not intended to function as a diagnostic or predictive instrument; rather, the test provides quantitative and qualitative information on current levels of depressive symptomatology. The test is appropriate for use with adult outpatients, whether or not they meet DSM-IV criteria for diagnosis of a depressive disorder. NORMS AND TEST BIAS. The standardization sample for the RDSI consisted of 450 nonclient adults (ages 18-89) selected from a larger sample (n = 531) to provide balanced representation of gender and age groups. The authors also report norms from a psychiatric outpatient sample (n = 324), in which patients with Major Depressive Disorder (n = 150) were represented. Many of the analyses reported in the manual are based on the total development sample (n = 855). The manual provides comprehensive descriptions and analyses of sample demographics. There was a significant effect of gender on RDSI scores, consistent with previous research demonstrating a slight trend for women to report greater depressive symptomatology. There were no significant main effects for age or ethnicity, and no significant age X gender or ethnicity X gender interaction effects. However, as the authors prudently note, ethnic minorities, especially Asians and Hispanics, had relatively small sample sizes; therefore, statistical power may be insufficient to detect ethnicity-

 

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related differences in scores. ADMINISTRATION AND SCORING. The RDSI is clearly and elegantly designed. The test is easily administered in both individual and group formats and is sufficiently straightforward to be used by a wide variety of mental health professionals with appropriate training. The manual provides clear instructions for administration and scoring, which is readily accomplished by hand. Additionally, the manual includes procedures for prorating incomplete protocols and describes some simple validity checks based on usual or inconsistent response patterns. The RDSI also contains six critical items that merit follow-up when scored in the keyed direction. The RDSI produces raw scores ranging from 0 to 63, although raw scores above 35 are rare. The manual provides tables for conversion of raw scores into T-scores and percentile ranks. Raw scores of 10 or below are not suggestive of clinical severity; scores from 11 to 15 suggest mild severity. A cutoff score of 16 identifies “a clinically relevant level of depressive symptoms” that warrants referral “for further evaluation and consideration of treatment” (professional manual, p. 15). The manual provides a detailed description of the cutoff score selection criteria: to maximize both hit rate and clinical sensitivity. In a study of the RDSI’s ability to differentiate between participants with an existing diagnosis of Major Depressive Disorder and nonpatient controls, a score of 16 correctly classified 94.9% of persons overall and 95.3% of those with an existing diagnosis of Major Depression. RELIABILITY. The reliability estimates of the RDSI appear excellent across a series of measures. Cronbach’s alpha estimates of internal consistency were .93 for the total sample and .89 for the psychiatric outpatient sample, with minimal differences between genders. Test-retest reliability computed at approximately one-week intervals (using a sample of 190 adults retested after the initial data collection) yielded an overall correlation of .94. The authors also report correlations between individual items and total scale score for the total development sample. Correlations ranged from .44 to .83 (all but two were above .50), suggesting substantial homogeneity of item content, even though the RDSI taps a diverse group of depressive symptoms. Finally, the standard error of measurement is less than 3 points for both men and women, indicating a stability of measure that supports clinical use. VALIDITY. The manual provides clear and comprehensive summaries of validational data. The descriptions of statistical and conceptual strategies for validation are very well written and well organized; the material should be broadly accessible, even to readers with relatively little training in quantitative methods. More importantly, the substance of these analyses is clear and convincing evidence of content, criterion-related, construct, and clinical validity. The item selection procedures for the RDSI provide important evidence of content validity. The selection process ensured that items reflected mood, cognitive, somatic, neurovegetative, psychomotor, and interpersonal areas of symptomatology. The RDSI item content is tied to the diagnostic criteria of the DSM-IV; the instrument is therefore atheoretical in that content parallels the DSM’s focus on symptom presentation rather than etiological explanation. Additionally, item validity can be implied from item homogeneity demonstrated by item-with-total-scale correlations, as noted above. The authors’ evaluation of criterion validity focuses on concurrent rather than predictive criteria, a choice defended on the grounds that the RDSI is designed to assess current levels of severity but not to predict the future course of depression. The manual presents strong evidence of concurrent validity based on correlations with a variety of criterion measures, including the Hamilton

 

 

Depression Rating Scale (.93), the Beck Depression Inventory (.94), the Beck Hopelessness Scale (.80), the Adult Suicidal Ideation Questionnaire (.67), the Beck Anxiety Inventory (.71), the Rosenberg Self-Esteem Scale (-.71), and the Marlowe-Crowne Social Desirability Scale–Short Form (-.37). This is an impressive array of correlations with well-validated criterion instruments. Moreover, the choice of criterion instruments provides strong evidence of convergent and discriminant construct validity. Further evidence of construct validity comes from factor analytic evaluation of the RDSI items. An initial principal components analysis using both orthogonal and oblique rotations yielded a consistent three-factor structure for the RDSI; the dimensions were depressed mood- demoralization, somatic complaints, and vegetative symptoms-fatigue. A second principal components analysis, restricted to data from psychiatric outpatients, yielded essentially the same factor structure. The manual includes an interesting discussion of clinical efficacy or clinical validity. In addition to a detailed discussion of the issues of hit rate and sensitivity noted earlier, the authors demonstrate statistically significant differences in RDSI score among nonreferred adults, persons with Major Depressive Disorder, and persons with other psychiatric diagnoses; the authors term this type of analysis “contrasted groups validity.” SUMMARY. The RDSI provides a reliable, valid, and convenient short screening for severity of depressive symptoms in psychiatric outpatients. The supporting materials are outstanding for their thorough documentation and clarity of expression, and the evidence of reliability and validity is compelling. Although the test probably does not provide much additional or qualitatively different clinical information relative to other instruments (e.g., Revised Hamilton Rating Scale for Depression [RHRSD], Beck Depression Inventory-II [BDI-II], or Minnesota Multiphasic Personality Inventory-2 [MMPI-2], the RDSI is an excellent choice for clinicians who desire an efficient screening focused on depressive symptoms.

Review of the Reynolds Depression Screening Inventory by ROSEMARY FLANAGAN, Adjunct Associate Professor of Psychology, St. John’s University, Jamaica, NY: The Reynolds Depression Screening Inventory (RDSI) is an instrument in a series (e.g., Reynolds, 1986) of depression inventories. The manual is well written and appears useful for both researchers and practitioners. Standardization procedures and psychometric properties are carefully explained; illustrative case examples are provided. To the credit of the authors, sufficient data are reported in the manual, permitting test users to arrive at their own judgments about the RDSI. A literature search did not yield further information; therefore, this review is based on material in the manual, and a recent conference presentation (Reynolds, Flament, Masango, & Steele, 1999). The authors appear to have realized their stated goal of developing a measure of depression consistent with the diagnostic criteria for Major Depressive Disorder, according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994). Depression is a significant mental health problem in that surveys indicate (e.g., Kessler et al., 1994) prevalence rates as high as 10.3% for the general population. The RDSI is not intended for the diagnosis of depression, but rather, is to be used to provide an indication of the severity of the problem over the past 2 weeks. Items reflect the same domains that are covered on the Hamilton Depression Rating Scale (HDRS; Hamilton, 1960), with weighted response options for the items. The RDSI is similar in format to the Beck Depression Inventory (BDI; Beck, Steers, & Brown, 1987) in that each item is rated along a continuum, with higher scores indicative of greater depressive

 

 

symptomatology. There are three to five response options for each item, stated in specific behavioral terms, similar to a structured interview. Administration and hand scoring can be accomplished in 10-15 minutes; there is no computer-scoring format. Scoring involves summing the numerical values assigned to the response options, with data reported as linear T=scores (Mean = 50; SD = 10) and percentiles. Responses to six critical items are also reviewed. These items address the following and its extent: whether the respondent is feeling depressed, the respondent’s outlook, suicidal ideation, changes in interest and work performance, and general feelings about oneself. The RDSI is written at approximately a fifth-grade reading level, somewhat below the reading level of the BDI (eighth-grade). Similar to the BDI, this format is advantageous to practicing clinicians, as it can be administered and scored during an office visit, if necessary. Norms were derived from a sample of 450 individuals who were matched for gender and age. A concern is that the sample is geographically limited, having been drawn from the Midwestern and Western United States. The racial-ethnic composition of the sample is 89.1% Caucasian, 4.5% African-American, 2.0% Asian, 3.3% Hispanic, and 1.1% Other. Approximately 72% of the individuals were between 25 and 64 years of age, with 14% in both the 18-24- and 65-89-year cohorts; the mean age of the participants was 43. Socioeconomic status varied from professionals to the unemployed; dwelling areas were urban, suburban, and rural. Data were collected on several additional samples that were used in subsequent analyses. The psychiatric sample was composed of 324 individuals, 150 of whom were diagnosed as having major depression, 123 had anxiety disorders, and 51 were diagnosed with other psychopathology. The demographic characteristics of this group were generally similar to the standardization sample. The mean scores for each group were such that the groups were collapsed into two groups: those with major depression and those with other psychopathology. An additional sample referred to as the total development sample was used for some analyses; its composition is demographically similar to the other samples used. It was composed of 855 individuals, approximately 62% had no DSM-IV (American Psychiatric Association, 1994) diagnosis. The remaining 38% comprised a group with major depression and a group with other psychiatric problems. Coefficient alpha for the total sample and the psychiatric sample was .933 and .898, respectively. Test-retest reliability at a 1-week interval was .944. These values are adequate for clinical decision making and research (Kaplan & Saccuzzo, 1997; Nunnally & Bernstein, 1994). The instrument appears to assess a sole construct, with scores demonstrating adequate stability. Validity was examined in several ways: content, criterion-related, construct, and clinical (contrasted groups), and the efficiency, sensitivity, and diagnostic specificity of the RDSI cutoff score. Item-total correlations, reflecting content validity, are described as moderate to high, with approximately 25%-69% of the variance being explained for 16 of 19 items. Criterion-related validity was assessed by examining the sample correlation (r = .93) between an adapted version of the Hamilton Depression Rating Scale (HDRS; Reynolds & Kobak, 1995) and RDSI scores. The adapted form of the HDRS requires considerably less time to administer and is much less labor-intensive than the original HDRS (Hamilton, 1960), and is similar in format to the RDSI. Construct validity was evaluated by examining the relationship between the RDSI and several measures. Correlation with the Beck Depression Inventory (BDI; Beck, Steer, & Brown, 1987) was .94. Correlations with related constructs, such as suicide ideation, assessed by the Adult Suicidal Ideation Questionnaire (Reynolds, 1991) was .67. Correlation with the Rosenberg Self-Esteem Scale (Rosenberg, 1965) indicated an inverse relationship, as might be expected (-.71). Additional evidence of construct

 

 

validity was provided as part of the validation of the Physical Self-Concept Scale (Reynolds, Flament, Masango, & Steele, 1999). The measure evaluates physical aspects of appearance, ability/skills, intelligence, health, and self-efficacy related to these same domains. A moderate relationship with the RDSI was demonstrated, accounting for 21% of the variance for a sample of community-based college students and adults. Multiple regression analysis indicates that the RDSI measures depression as opposed to generalized psychological distress. This was substantiated in two analyses in which the beta weights for depression as assessed by the BDI and HDRS were .66 and .72, respectively. In contrast, beta weights ranged from -.22 to .18 for measures of hopelessness, suicide ideation, self- esteem, and anxiety. Factor analytic studies indicate that 58% of the variance in the total development sample is explained by a three-factor solution, corresponding to depressed mood, somatic complaints, and vegetative symptoms. The factors were extracted to provide evidence of validity, rather than to provide information about aspects of depression. It is made clear that the RDSI should not be the sole criterion used to diagnose depression, and that the factors should not be interpreted individually. This bears some similarity to the BDI. The most critical validity evidence is the efficacy of the RDSI cutoff scores. Analyses were conducted to determine the level at which the combination of sensitivity (correct identification of those with major depression), specificity (correct identifications of those who do not have major depression), positive and negative predictive value (correct identifications), and hit rate (proportions of correct identifications) were optimized. Data are also presented on the strength of association (chi square, kappa coefficient) and the quantified clinical validity of the cutoff scores (phi coefficient). The cutoff score that is expected to result in optimal decision making is 16, substantiated by tabled data indicating four (sensitivity, hit rate, chi square, phi coefficient) indices at their peak; the remaining indices are acceptably high. This corresponds to the 96th percentile, or T = .72. Should the score not be in the clinically significant range, the RDSI could be interpreted normatively. The item numbers of the six critical items are printed near the bottom of the front page of the protocol. Responses of “2” or higher on these items are clinically significant. Should an individual obtain scores of “3” or more on three critical items, further evaluation is indicated, irrespective of the total score. SUMMARY. The data in the manual suggest that the RDSI should live up to the authors’ claims. Psychometric properties are sound, despite a smaller norming sample than that used for the BDI. The level of detail in the manuals, particularly in the validity sections, exceeds that available in the BDI manual, and is an improvement. The RDSI is atheoretical; the BDI reflects Beck’s theory (e.g., Beck, 1973). The strength of the RDSI may be that it is a technical advance. Nevertheless, the uses and properties of the RDSI are similar to the BDI. The need for a new instrument to assess depression in a brief, time-sensitive format is debatable. Researchers and practitioners may be less likely to utilize a new measure, given the existing data and large literature supporting the BDI. It is reasonable to expect that additional research is needed for the RDSI to become a commonly accepted alternative to the BDI. REVIEWER’S REFERENCES Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56-62. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

 

 

Beck, A. T. (1973). Depression; Causes and treatment. Philadelphia: University of Pennsylvania Press. Reynolds, W. M. (1986). Reynolds Adolescent Depression Scale. Odessa, FL: Psychological Assessment Resources. Beck, A. T., Steer, R. A., & Brown, G. K. (1987). Beck Depression Inventory-II manual. San Antonio, TX: Psychological Corporation. Reynolds, W. M. (1991). Adult Suicidal Ideation Questionnaire: Professional manual. Odessa, FL: Psychgological Assessment Resources. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Kessler, R. C., McGonagle, K. A., Zhao, S., Nelson, C. B., Hughes, M., Eshleman, S., Wittchen, H., & Kendler, K. S. (1994). Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: Results from the National Comorbidity Survey. Archives of General Psychiatry, 51, 8- 19. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Reynolds, W. M., & Kobak, K. A. (1995). Reliability and validity of the Hamilton Depression Rating Inventory: A paper and pencil version of the Hamilton Depression Rating Scale clinical interview. Psychological Assessment, 7, 472-483. Kaplan, R. M., & Saccuzzo, D. (1997). Psychological testing (4th ed.). Pacific Grove, CA: Brooks- Cole. Reynolds, W. M., Flament, J., Masango, S., & Steele, B. (1999, April). Reliability and validity of the Physical Self-Concept Scale. Paper presented at the annual convention of the American Educational Association, Montreal, Canada.

*** Copyright © 2014. The Board of Regents of the University of Nebraska and the Buros Center for Testing. All rights reserved. Any unauthorized use is strictly prohibited. Buros Center for Testing, Buros Institute, Mental Measurements Yearbook, and Tests in Print are all trademarks of the Board of Regents of the University of Nebraska and may not be used without express written consent.

Dummy Variables, Regression Diagnostics, And Model Evaluation

QRA Week 10

Dummy Variables, Regression Diagnostics, and Model Evaluation

By now, you have gained quite a bit of experience estimating regression models. Perhaps one thing you have noticed is that you have not been able to include categorical predictor/control variables. In social science, many of the predictor variables that we might want to use are inherently qualitative and measured categorically (i.e., race, gender, political party affiliation, etc.). This week, you will learn how to use categorical variables in our multiple regression models.

While we have discussed a great deal about the benefits of multiple regression, we have been reticent about what can go wrong in our models. For our models to provide accurate estimates, we must adhere to a set of assumptions. Given the dynamics of the social world, data gathered are often far from perfect. This week, you will examine all of the assumptions of multiple regression and how you can test for them.

Learning Objectives

Students will:

· Analyze multiple regression testing using dummy variables

· Analyze measures for multiple regression testing

· Construct research questions

· Evaluate assumptions of multiple regression testing

· Analyze assumptions of correlation and bivariate regression

· Analyze implications for social change

 

Learning Resources

Required Readings

Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 2, “Transforming Variables” (pp. 14–32)

· Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, 8, and 9)

 

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press/Sage Publications.

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

· Chapter 6, “What are the Assumptions of Multiple Regression?” (pp. 119–136)

 

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press/Sage Publications.

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

· Chapter 7, “What can be done about Multicollinearity?” (pp. 137–152)

 

Multiple Regression: A Primer, by Allison, P. D. Copyright 1998 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

 

Warner, R. M. (2012). Applied statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

· Chapter 12, “Dummy Predictor Variables in Multiple Regression”

 

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

Fox, J. (Ed.). (1991). Regression diagnostics. Thousand Oaks, CA: SAGE Publications.

· Chapter 3, “Outlying and Influential Data” (pp. 22–41)

· Chapter 4, “Non-Normally Distributed Errors” (pp. 41–49)

· Chapter 5, “Nonconstant Error Variance” (pp. 49–54)

· Chapter 6, “Nonlinearity” (pp. 54–62)

· Chapter 7, “Discrete Data” (pp. 62–67)

Note: You will access these chapters through the Walden Library databases.

 

Document: Walden University: Research Design Alignment Table

 

Datasets

 

Document: Data Set 2014 General Social Survey (dataset file)

Use this dataset to complete this week’s Discussion.

Note: You will need the SPSS software to open this dataset.

 

Document: Data Set Afrobarometer (dataset file)

Use this dataset to complete this week’s Assignment.

Note: You will need the SPSS software to open this dataset.

Document: High School Longitudinal Study 2009 Dataset (dataset file)

Use this dataset to complete this week’s Assignment.

Note: You will need the SPSS software to open this dataset.

Required Media

Laureate Education (Producer). (2016m). Regression diagnostics and model evaluation [Video file]. Baltimore, MD: Author.

 

Note: The approximate length of this media piece is 7 minutes.

 

In this media program, Dr. Matt Jones demonstrates regression diagnostics and model evaluation using the SPSS software.

 

Accessible player

Laureate Education (Producer). (2016). Dummy variables [Video file]. Baltimore, MD: Author.

 

Note: This media program is approximately 12 minutes.

 

In this media program, Dr. Matt Jones demonstrates dummy variables using the SPSS software.

 

Accessible player

 

Discussion: Estimating Models Using Dummy Variables

You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables so they can be used in a regression model and how to properly interpret the coefficients. Additionally, you will gain some practice in running diagnostics and identifying any potential problems with the model.

To prepare for this Discussion:

· Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.

· Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.

 

By Day 3

Estimate a multiple regression model that answers your research question. Post your response to the following:

1. What is your research question?

2. Interpret the coefficients for the model, specifically commenting on the dummy variable.

3. Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?

Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

 

 

Assignment: Testing for Multiple Regression

You had the chance earlier in the course to practice with multiple regression and obtain peer feedback. Now, it is time once again to put all of that good practice to use and answer a social research question with multiple regression. As you begin the Assignment, be sure and pay close attention to the assumptions of the test. Specifically, make sure the variables are metric level variables.

 

Part 1

To prepare for this Part 1 of your Assignment:

· Review this week 9 and 10 Learning Resources and media program related to multiple regression.

· Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in the Learning Resources for this week.

· Based on the dataset you chose, construct a research question that can be answered with a multiple regression analysis.

· Once you perform your multiple regression analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

For this Part 1 Assignment:

Write a 1- to 2-page analysis of your multiple regression results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.

Use proper APA format, citations, and referencing for your analysis, research question, and display of output.

 

Part 2

To prepare for this Part 2 of your Assignment:

· Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.

· Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in this week’s Learning Resources.

· Consider the following:

 

· Create a research question with metric variables and one variable that requires dummy coding. Estimate the model and report results. Note: You are expected to perform regression diagnostics and report that as well.

· Once you perform your analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

 

For this Part 2 Assignment:

Write a 2- to 3-page analysis of your multiple regression using dummy variables results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.

Use proper APA format, citations, and referencing for your analysis, research question, and display of output.

By Day 7

Submit Parts 1 and 2 of your Assignment: Testing for Multiple Regression.

Application: Employee Resistance To Change

By the end of Week 7, you will submit a Change Management Plan for successfully managing change related to a fictitious company, called Hamilton Snacks, which is acquiring a smaller Oregon-based company, called Arlo’s Granola. To learn more about the acquisition, read the Change Management Case Study that is attached.

The following Assignment is designed to help you begin work on your Change Management Plan. Before you begin, consider the following statistic: 70–90% of acquisitions fail (Martin, 2016).  Why do so many acquisitions fail? One reason is because leaders grossly underestimate challenges related employee resistance to change.

In this Assignment, you will consider why employees may resist the change described in the case study and explore leadership strategies for reducing resistance.

Martin, R. L. (2016). M&A: The one thing you need to get right. Harvard Business Review94(6), 42–48.

To Prepare:

  • Read the article “Change Management Models: A Comparative Analysis and Concerns.” (ATTACHED) Consider the steps for successfully managing change.
  • Read the articles “Resistance to Change in Organizations”(Attached) and think about why employees resist change, how employees express their resistance to change, and leadership strategies for reducing resistance to change.
  • View the media resource “Communication Strategy for Leading Change. (TranscriptATTACED) Think about the importance of implementing a communication strategy to reduce employee resistance to change.
  • Read Chapter 11 in the Northouse text. Consider how adaptive leadership could be applied to reduce employee resistance to change.
  • Read the Change Management Case Study that is below. Think about why employees may resist the change described in the case study, and identify specific leadership strategies for reducing employee resistance to change.

By Day 7

Submit a 2 page paper that addresses the following:

  • Briefly describe three reasons why employees may resist the organizational change described in the case study.
  • Using the follower typologies in this week’s readings, describe two types of employees that are likely to resist the change and two types of employees that are likely to embrace the change, and explain why.
  • Recommend two leadership strategies for reducing employee resistance to organizational change. Provide specific examples of how each strategy would reduce employee resistance to change.

    Change Management Models: A Comparative Analysis and Concerns —BRIAN JOSEPH GALLI Long Island University-Post, Greenvale, NY 11548, USA

    IEEE DOI 10.1109/EMR.2018.2866860

    Abstract—To better understand change management, we compare some popular change management models in relation to project management and organizations in this study. After a brief introduction of five major models, various advantages and disadvantages are identified for each. Lessons and implications for organizations and management are also introduced.

    Key words: Change management, project management, change manage- ment models, Kurt Lewin, Kotter’s 8-Step, ADKAR, McKinsey 7-S, general electric CAP

    INTRODUCTION

    CHANGE is inevitable, whether it is personal or professional. Also, change is necessary in order to grow, especially in your professional career. Maintaining the same position ten to fifteen years later usuallymeans that change has been limited. However, we as individuals and organizations are creatures of habit, so change is not always easy. Professional changes are even trickier to deal with as a project manager or organization leader. In these positions, you are responsible for helping your team members and employees to reach their full potential and to produce great work. This goal is tricky because of the multiple personalities involved, but changemanagement may be a useful mechanism in this circumstance.

    A proactive organization and project management team customarily has a preset change management plan for project or organization structure, business systems/processes, or employee role change requirements. Change management consists of three layers: organizations, people, and projects. To fully understand the various change models, we must first understand why they are needed and what change management means at its core.

    Change management is “the application of a structured process

    and set of tools for leading the people side of change to achieve a desired business outcome; it is both a process and a competency” (Creasy, 2018). This situation requires an organization, project team, or individual to notice a need for change. Furthermore, it seeks to evolve from their current state to implement change/s to reach a desired state. Calling it a process means that once it is implemented, it can be used repeatedly, but calling it a competency means that it should generate an effective outcome for the majority of the time.

    Before a project team or organization can construct a viable change management plan, they should understand the available change models to find which is most effective for their project or organization. There are many recognized models available; in this article, we will focus on some of the more popular and theoretically sound models.

    A GENERAL CHANGE MANAGEMENT PROCESS

    As mentioned earlier, change management (CM) is evolving from a current state to a desired state. Before executing change, a series of phases need consideration. Figure 1 shows a general change management process from a project management perspective. In the

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    project planning cycle, the project manager has a process and change management model in place that is specific to their management style.

    The first phase involves identifying the need for a change. This means that either something has come up in the project that the team or manager would like to change or a different outcome arises than previously discussed. When this situation happens, the activities that take place are deciding the current, the future, and the transition state. A basic question is how it will affect the scope of the project and if the scope needs to be altered.

    In the second phase, the team or manager determines the change details. It is a process in the sense of how the team conducts certain tasks and activities will be changed. A question arises on whether there is a role change where a team member(s) will take on a new role or responsibility. On the other hand, is it an overall change to be based on client needs? Cost and risk analyses are performed in this phase to consider the feasibility of change based on time and financial resources.

    The next phase is when CM models roles begin. This plays a large role in how the change will be implemented. Stakeholders’ needs and interests

    require assessment, with commensurate communication to them, for effective change to progress. Whether the change is minor or major, the project manager will experience some resistance to the proposed changes from both team members and stakeholders. This is why the selected change management model is such a crucial part of the CM process; each model has methods in place to help curb resistance. This is also where the action, communication, and resistance plan for the CM process need to be created and tailored to the different stakeholder groups.

    Fourthly, there is the implementation stage. The transition state occurs and the plans are now put into motion, while a CM process has actually been formed. Lastly, the monitoring phase controls the changes and ensures that they are on track to get to the desired state. Any errors are caught and lessons are learned for future references to update the CM process, which helps to ensure success future CM process use.

    Now that we understand some of the generic CM process stages in a project environment, we will discuss in detail some of the models that are most commonly used. This comparative discussion includes their differences and similarities. Then, a recommendation that is based on

    some CM model strengths and weaknesses is made.

    CHANGE MANAGEMENT MODELS

    “Due to varying factors internal to an organization’s environment, not all changes are the same; therefore, management needs to use different change models and methodologies depending on the situation” (Schech-Storz, 2013).

    CM models typically utilize various theories. Variations in personnel and organizational cultures have led to various perspectives. Five popular and tested models are reviewed here, including Kurt Lewin’s Change Management Model, Kotter’s 8 Step Change Model, ADKAR Change Management Model, The McKinsey 7-S Model, and General Electric’s Change Acceleration Process (CAP).

    Kurt Lewin’s Change Management Model “Kurt Lewin and E.H. Schein, considered precursors of change management models, believe that the process of change involves three basic stages: the behavioral thaw (unfreezing), the change (transition) and the recrystallization of behaviors (change)” (Talmaciu, 2014).

    Lewin’s theory (Lewin, 1951) proposes that organizations need to have time initially to reflect on the change and organizational

    Figure 1. A general change management process. Source: http://www.adaptivehvm.com/changemangement.

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    involvement analysis prior to “unfreezing” the organization. Lewin made several assumptions for effective change. His first assumption was that there needs to be a change motivator or else the change does not occur. The second assumption was that employees are at the heart of changes within the organization. Then, his third assumption was that those affected by the change need to adapt, incorporate the new processes into their routine, and discontinue past practices. Lastly, Lewin postulates that even with desirable goals, resistance to change is common. For a change to be effective, replacing organizational behaviors and attitudes must reinforce it.

    Figure 2 summarizes Lewin’s theory. There is an initial understanding that the organization or project process needs to be changed. Initial understanding requires an in-depth analysis for what is and what isn’t working. A plan then needs creation.

    The CM process has now entered its transition phase. This phase is where the resistance from employees will begin to take place, as well as hiccups, because the employees are not used to the new changes. When this occurs, it is important to have resources readily available for team members or employees to ease the transition. These resources can be in the form of training, instructions, or simply having access to the project manager or department manager to make inquiries. In the third phase of refreezing, according to Levasseur (2001), the model requires change agents to work actively with organizational personnel to install, test, debug, use, measure, and enhance the new system.

    Kotter’s 8 Step Change Model Kotter’s 8 Step Change Model (Kotter, 1996) expanded Lewin’s original change theory. Kotter believed that “Leadership must create and sustain the kind of changes needed for successful organizations

    to compete in the current competitive world” (Kotter, 1996).

    The eight steps in the model include: 1. Create a sense of urgency. 2. Create a core coalition. 3. Develop and form a strategic

    vision. 4. Communicate and share vision

    plans. 5. Empowering employees to act

    on the vision. 6. Generate short-term wins. 7. Consolidate gains and produce

    more change. 8. Initiate and set new changes.

    Figure 3 below shows an example of how the model operates. In step one, the project team or organization realizes the need for change, which is where they create a sense of urgency to get the ball rolling. Kotter (2012) stated in the Harvard Business Review that “creating a sense of urgency is critical to increasing the organization’s awareness that it needs strategic adjustments and that there are always opportunities in sight.” In the second step of creating core coalition, Kotter notes that for “effective change to happen, a team of effective leaders must develop into a coalition to build urgency around the need for change. People must know change is necessary” (Kotter, 1996).

    Developing a strategic vision requires formulating a clear and sensible transformation vision. The transformation vision is required to align objectives and to progress as a group (Calegari, 2015). Change will not be successful without a well- developed strategic vision because the project team or organization does not have an overall roadmap for the change process. Also, the employees must understand why the change is needed in order to support it.

    Effectively communicating the strategic vision is the next step. Management and the CM team

    Figure 2. Kurt Lewin’s change management model.

    Figure 3. John Kotter’s model.

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    should share the vision of change to get employees and team members onboard. The CM team needs to get the employees to see the need for the change. This step is crucial because if not handled properly, there could be fundamental resistance from employees, and team members can feel left out.

    In step five, to empower employees means to allow them to try new ideas and approaches. Communication alone is never sufficient. Employees need support in removing obstacles to the vision (Kotter, 1996). Meanwhile, step six, sees that the changes and progress is made with significant outcomes and sharing is needed. Short-term wins help, and demonstrating that the change effort is constructive is important. These wins help the CM team to test the vision against real conditions and to make necessary adjustments.

    Step seven requires that the organization or project team should consolidate gains and produce more change. Not allowing complacency and continuous progress is a goal. Change efforts often fail because participants revert back to their prior habits, usually failing to continue change implementation. Finally, there is the initiation of new change. In this stage, the goal is to institutionalize the change and to anchor it in the organizational culture (Kanter, 2003).

    ADKAR Model The ADKAR Model (Hiatt, 2006), as opposed to the previous models, focuses on people change adaptation, as opposed to the change itself. The ADKAR model is sequenced by how an individual experiences the change. The ADKAR lifecycle begins after identifying a change. From this initiation point, there is a framework and sequence

    for managing the people side of change (Hiatt, 2006). The acronym stands for five goals that the model aims to accomplish. These are: 1. Awareness 2. Desire 3. Knowledge 4. Ability 5. Reinforcement

    Figure 4 shows the ADKAR Model sequence. We now consider the factors that affect the 5 steps. Awareness is when an organization or project team informs employees of a need for change. The primary issue at this stage is determining the level of change for a specific project. Desire from the employees and project team requires the motivation to participate in the change along with the ability to perform necessary changes. Thus, employees need knowledge of how to change and what the change entails. ADKAR continues to Ability, which are the skills required to implement change on a day-to-day basis. Reinforcement is then needed to maintain and sustain change in the organization or project (Hiatt, 2006).

    The McKinsey 7-S Model The McKinsey 7-S Model was developed by Tom Peters, Richard Pascale, and Robert Waterman Jr., while McKinsey & Company employees. The model analyzes seven organization or project team aspects, highlighting the changes to be made. The 7 S Model consists of: 1. Strategy 2. Structure 3. Systems 4. Skills 5. Staff 6. Style 7. Shared Goals

    Figure 5 shows a McKinsey 7-S Model and its linkages. Strategy

    involves transforming the organization from the current position to the new position, as identified by the objectives. The structure identifies and defines the roles, responsibilities, and accountability relationships (Singh, 2013). Systems are formal procedures of the organization or project team. They include management control systems, performance measurement/reward systems, planning, budgeting, resource allocation systems, and information systems. The systems influence behavior because they are the mechanisms that affect resources available for a given entity, as well as the processes by which individuals are rewarded and groups measured (Spaho, 2014).

    Skills are the ability of employees and team members to do the organization’s or project team’s work. The staff possesses the skills, which is the model element. Also, this element looks at the way in which the company hires and retains staff into the organization or project team. Lastly, shared goals are the central organizational beliefs and attitudes helping employees to understand the organizational purpose, as well as how it will affect the internal and external environments.

    General Electric’s Change Acceleration Process Model (CAP) General Electric Company came up with its own version of a CM model to transform how people accept, operate, and employ new business strategies. The CAP Model allows an organization to manage business model change implementation. GE recognized a need for the model, since the success or failure of a new business project deals with both acceptance and quality. They represent this with the

    Figure 4. The ADKAR change model stages.

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    equation Q � A ¼ E. (Polk, 2011). The equation means that good quality work with good acceptance will result in effective change or results.

    The model has 7 steps, see Figure 6 (Neri & Mason, 2008) and include: 1. Leading Change: A champion

    who will drive change is identified. A champion who sponsors the change management program initiates most successful change initiatives; the champion must be publicly visible, committed to the change.

    2. Creating A Shared Need: The team identifies the reason for a

    change, makes certain reasons for a change, makes certain the reasons are widely understood, and overcomes resistance to change.

    3. Shaping AVision: The team delineates a desired outcome of change and conveys it to key stakeholders.

    4. Mobilizing Commitment: Key stakeholders are identified; resistance analysis is performed; actions are developed to gain support and commitment.

    5. Making Changes Last: The team institutes appropriate systems and structures to sustain results.

    6. Monitoring Progress: Realistic benchmarks are set and measured.

    7. Changing Systems & Structures: Changes are integrated into the organization’s culture.

    COMPARATIVE FINDINGS Each Model’s Strengths and Weaknesses Lewin’s model is a simple and effective three-step process, which makes it attractive for large organizations and project teams to use. Analyzing aspect changes is easy to do. The three major steps are transparent enough for change

    Figure 6. GE’s change acceleration process. Source: Holloway (2015) leading and engaging sustainable change.

    Figure 5. The McKinsey 7-S model. Source: Hughes (2012).

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    management novices to understand how to do the change from start to finish. However, a disadvantage is that the model does not detail how to deal with the human part of the change, which is a common limitation of most methods. People resistance to change could potentially impact the organization/project team if not handled correctly. Another disadvantage is that the unfreezing phase can be time-consuming and costly if planned poorly or with minimal top management support.

    The Kotter model, in comparison to Lewin’s model, provides greater direction on how to implement change. It further incorporates the people side of change. Unlike Lewin, Kotter gives advice on which point in the process to communicate with employees in the model. The advice on including employees is effective for organizations with a traditional managerial hierarchy. While the model includes employees, it comes across as a top-down approach. The employees do not have input or the option to share ideas before strategic vision creation. Another disadvantage occurs if a step is skipped or executed incorrectly. This affects other steps and leaves the organization and project team to delay or regress. As a result, there could be wasted time and effort.

    The ADKARmodel’s advantage is the relatively increased focus of employee and project teammember acceptance of change. The process starts and ends with them as the forefront of change, so this characteristic is extremely important in choosing a CM model. The disadvantage of using this model is that since it focuses primarily on the people side of the change, it is better suited for project teams and environments, as opposed to large- scale organizations with complex processes.

    The McKinsey 7-S model advantage occurs in showing the weakness and

    strengths in seven core dimensions of the organization or project team. This characteristic provides managers with an opportunity to more clearly identify where the need for change lies. However, the disadvantage of this model is that it can be time- consuming and tedious to go through all of the levels. Since it is a complex model, it would be difficult to implement in a large organization. Another disadvantage is that instead of focusing the entire model on the people side of change, it really only focuses on the skills and staff portions of the model.

    The advantage of GE Change Acceleration Process method is its flexibility. When management utilizes this model, they must understand that it can exist in a nonlinear fashion, as various elements change in important to the CM team and their constituents. The disadvantage of the CAP Model is in its requirement of a strong leader, otherwise the model weakens. The leader must be able to get everyone onboard and committed to making the change.

    Model Comparisons In a careful review of these five models, one thing becomes abundantly clear with every one. No matter the model, change will only be successful if communicated and accepted by employees or project team members. It is also critical that an organization or project team should be able to manage CM effectively with appropriate support, knowledge, and resources. CM has a lot of moving parts to it, so management must understand all resistant forces. Failure to do so can be costly, decrease loyalty, reduce the probability of reaching goals, waste money, or squander resources. Nevertheless, not all resistance to change is bad because it forces management to check their vision or roadmap to help identify problem areas. Resistance also provides management with information about the intensity of an employee’s

    emotions on the issues or provides a means of releasing emotions.

    Each of the models grasp the basic concept of CM, which is starting at a current state and realizing a need for change, entering the transition phase, implementing the change, and then getting to their desired state. Three of the five models (Kotter’s, McKinsey, and CAP) provide the substantial details on beginning, managing, and sustaining change. This level of detail provides clarity and structure, such as if Lewin’s Model is not managed properly, things can easily go awry.

    Some models focused more on the process of executing change itself, rather than on the people dimension. Furthermore, Lewin’s and Kotter’s models were the most limited on the people aspect. ADKAR had the greatest focus on employees and team members, but it is limited when seeking large-scale implementations.

    Failure to effectively understand and manage CM models contributes to why change management initiatives are branded as nebulous and trivial undertakings. Thus, it is critical that the selected CM model reinforces change and is linked to a successful and sustainable implementation (Holloway, 2015).

    The initial goal of this paper was to find the most effective model. After all of the research was done, it was clear that the most effective model is contingent. There are a couple of perspectives on the most effective model because of the differing characteristics of project teams and broader organizations.

    For large organizations, our perspective is that the most effective CM model is likely to be General Electric’s Change Acceleration Model. This is the most effective because it was designed with large organizations in mind. The large quantities of people in large

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    organizations need to have many who are committed to a change to work successfully. As a result, this model separates the steps into enough detail to manage change elementally and in smaller pieces. Most importantly, it monitors the progress of the change before implementation.

    Since a project has the constraint of a schedule, the CM process that it employs needs to be extremely effective to not throw the project into overruns or scope creep. The most effective model that can support this critical element is likely to be Kotter’s 8 Step Change Model. This model concurrently incorporates relevant change agents, stakeholders, and team members to carry out an effective change, which expedites the CM program.

    These perspectives are very general, and we are only mentioning the effectiveness, advantages, and disadvantages in a broad overview. There are many other items to consider. This section provides a starting point for managers to consider what is appropriate for their organization and/or project team. Also, the insights provided are based on the broad literature in this area.

    DEALING WITH CHANGE MANAGEMENT MODELS: ORGANIZATIONAL AND MANAGERIAL CONCERNS

    There are many concerns and implications at various levels of the organization and managerial layers. Some of these dimensions are obvious, and the research literature confirms them. However, these concerns and issues bear repeating. They are meant to aid managers and organizations to remember that a broader picture for the application of these models is needed, especially before they are consumed by the minutiae of CM models and implementation.

    Strategic and operational change is a constant concern to remain competitive. Top-down and bottom-up leadership approaches are necessary. Additionally, strong and well-directed visions could aid change management. Poorly delivered organizational policies and a misalignment between top-down and bottom-up philosophies will doomCM.

    Adequate training is needed for management and leadership to oversee their approach. Furthermore, leadership training on various aspects and importance of overall performance is necessary. Inadequate leadership development programs, skills, and supervision are major concerns for these CM models. With the proper training, a leader can see that the focus should be on managing these variables, their concepts, and models, rather than being concerned over short-term profits and costs alone.

    Financial elements and resources allocation will limit organizational CM programs. Focusing on short-term problems may not produce long-term solutions. The network and complexity of organizations means that unintended consequences will arise; managers should be aware of these to think systematically and holistically (Clancy, 2018).

    At the managerial level, leadership and collaboration play critical roles. Management should broaden mentoring and leadership skills for every department or team to identify weaknesses. Gap analysis and benchmarking, to identify change needs and weaknesses against standard and industry practices, is always a tricky proposition. The relationships of models, factors, and tools beyond the CM models presented here can become a difficult to integrate. Thus, awareness and care are needed when various tools are sought to be integrated with these CM models.

    Team thinking and buy-in goes beyond the individual. Project teams, as well as project and organizational leadership, need to determine the type of training content. Understanding the choices is the first step.

    Team performance evaluation is important for their effectiveness. This is beyond training, measuring, and monitoring the various aspects of teams. Essentially, knowledge and expertise in delivering on the vision is necessary. Also, linking project and team performance and effectiveness to broader business performance is necessary. Linking these performance metrics and goals to CM programs is a non- trivial task.

    Even with all of these caveats, managers should be wary when implementing CM programs where paralysis occurs. Sometimes focusing on the many peripherals and preparation may cause CM efforts to drag out. As a result, motivation and moral need careful examination.

    Engineering and technical professions typically prefer analytical solutions and have these skills. Providing the project management and engineering community with an overview of these CM models is a first stage. Then, selecting the appropriate one for your situation is critical, especially based on culture and skills of project and engineering managers.

    Decision tools can be integrated throughout these CM models; project managers and engineers have a variety of analytical tools at their disposal. Similar to broader managerial concerns, falling prey to poorly integrated tools used for CM can cause difficulties and barriers to progress. Focusing too much on the analytics and not enough on the

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    culture can be a trap for many analytical thinkers.

    CONCLUSION

    Overall, this paper examines five popular CM tools. Each tool has a slightly different perspective. Although we provide some direction and concerns, it is ultimately your environment that will determine which is best for you. Whether at the organizational or project management level, change is ubiquitous. However, be aware that the model itself can be perfect for the organization or company, but without the willingness or desire to change

    from employees and team members, the process to implement change will almost always fail.

    Poor leadership is a big influence on the success of the change. This led me to my final thoughts on the topic of CM: people are the changes, not the models, and people will only change if they see and feel the need to do so. Thus, it is so important to effectively communicate the need for change and to include employees, as well as team members, to feel part of the change.

    Resistance is normal, but it comes into play when employees feel left out

    or that someone is telling them how to do their jobs. This is where conflict arises. A proactive change management leader will address his/ her project team members’ concerns immediately to ensure that they are comfortable with getting onboard with the changes.

    This overview is intended to build your knowledge, lessons from the research, and long history of CM models. The references provided present details into some of the various aspects of the issues presented here. Finally, these references prove to be valuable resources.

    REFERENCES

    Calegari, M. F., Sibley, R. E., and Turner, M. E. (2015). A road map for using Kotter’s organizational change model to build faculty engagement in accreditation. Academy of Educational Leadership Journal, 19(3), 31–43.

    Clancy, T. (2018). Systems thinking: Three system archetypes every manager should know. IEEE Engineering Management Review, 46(2), 32–41.

    Creasy, T. (2018). An Introduction Guide to Change Management. Retrieved April 2, 2018, from www.prosci.com

    Hiatt, J. M. (2013). Employees Survival Guide to Change: The Complete Guide To Surviving and Thriving During Organizational Change. Loveland, CO, USA: Prosci Research.

    Holloway, S. D. (2015). Leading and Engaging Sustainable Change: Achieving Organizational Transformation through the Transformative Methodologies of the Change Acceleration Process and Lean Six Sigma (Order No. 10014007). Available from ProQuest Central.

    Hughes, J. (2012). Paper E2 enterprise management. Journal of International Financial Management, 44, 39–42.

    Kanter, R. M. (2003). Challenge of organizational change: How companies experience it and leaders guide it. New York, NY, USA: Free Press.

    Kotter, J. (2012). Accelerate!. Harvard Business Review, 9–12. Kotter, J. P. (1996). Leading change. Cambridge, MA, USA: Harvard Business School Press.

    Levasseur, R. E. (2001). People skills: Change management tools – Lewin’s change model. Interfaces, 31(4), 71–73.

    Lewin, K. (1951). Field theory in social change. New York, NY, USA: Harper & Row. Neri, R. A. et al. (2008). Application of six sigma/CAP methodology: Controlling blood-product utilization and costs. Journal of Healthcare Management, 53(3), 183–95.

    Polk, J. D. (2011). Lean six sigma, innovation, and the change acceleration process can work together. Physician Executive Journal, 37(1), 38–42.

    Schech-Storz, M. D. (2013). Organizational change success in project management: A comparative analysis of two models of change. ProQuest Dissertations and Theses, 20–25.

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    Singh, A. (2013). A study of role of McKinsey’s 7S framework for achieving organizational excellence. Organization Development Journal, 31(3), 39–50.

    Spaho, K. (2014). 7S Model as a framework for project management. Paper presented at the 8th International Scientific Conference on Economic and Social Development, 450–464.

    Talmaciu, I. (2014). Comparative analysis of different models of organizational change. Valahian Journal of Economic Studies, 5(4), 78–80. Retrieved April 7, 2018.

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