...T-test: Salaries of Female and Male Human Resource Managers Christy Newman Argosy University T-test: Salaries of Female and Male Human Resource Managers An independent-samples t-test using the raw measurement data presented was completed to check the difference, if any, of the salaries between female and male human resource managers, t(18) = -0.408, p = 0.688, but no significant difference was found (Female Mean = 62.2; Male Mean = 63.7). The preparation for a statistical analysis test began with a well-developed, clear research question: What is the difference, if any, between the salaries of female and male HR Directors? Next, the null and alternative hypotheses must be defined. The null H₀ is that the salaries of female human resource managers = salaries of male human resource managers. The alternative Ha is that salaries of female human resource managers ≠ salaries of male human resource managers. The next step in the process is to determine the appropriate statistical test and sampling distribution. Since σ is unknown and the number of salaries being tested is less than 100, the t-test will be used. Because we are comparing data from two different groups, the two-sample t-test will be used. We do not know if the variance is different or equal, so we will use the two-tailed t-test for two-sample assuming unequal variances. Subsequently, we have to choose the Type 1 Error rate. For this t-test, α = 0.05, which is the standard rate used by statisticians for research...
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...BUS 310 Notes regarding Two-Sample t-Tests and ANOVAs In Chapter 9, we learned how to conduct a t test of a hypothesis when we were testing the mean of a single sample group against some pre-determined value (i.e., the 21.6 gallons of milk consumption as the national average). This week, in Chapter 10, we will see how to test hypotheses that involve more than one sample group—such as testing to see if males are significantly taller than females. If we have two groups, then the technique that we will use will still be a t test. If we have more than two groups, then we will have to use a different test called Analysis of Variance (ANOVA, for short). The good news is that the decision rules for hypothesis testing that we learned last week are still exactly the same: Set #1: If the absolute value (ignore any negative sign) of the test statistic is greater than or equal to the critical value, then you reject the null. If the absolute value of the test statistic is less than the critical value, you do not reject the null. Set #2: If the p value is less than or equal to α, reject the null. If the p value is greater than α, do not reject the null. (Remember that we must either reject or not reject the null—we never accept the null.) In order to conduct these tests, we will need to use the data analysis feature of Excel, which probably is not installed for you, but that’s OK, because it’s available and pretty simple to install—just follow these steps: ...
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...following variables a. Profits/(Total Assets-NPA) b. NPA/Total Assets c. Advances/Deposits d. (Total Assets-NPA)/Number of Branches REQUIRED DATA: Data is collected from www.moneycontrol.com and www.rbi.org on Profits, Total Assets, NPAs, Total Liabilities, Deposits, Advances, Bank’s Capital, number of branches, Ownership Variables (Public , Private( Foreign and Domestic) DESCRIPTION: Data is collected on Profits, Total Assets, NPAs, Total Liabilities, Deposits, Advances, Bank’s Capital, number of branches, Ownership Variables (Public , Private( Foreign and Domestic). The return of the stock for each month is calculated by the following formula: Rt =closing value of stock on date t-closing value of stock on date (t-1)closing value of stock on date (t-1) Where Rt - return of the stock. 1) Profits/(Total Assets-NPA) public and private bank Queone= Profits/(Total Assets-NPA) Group Statistics | | ownership | N | Mean | Std. Deviation | Std. Error Mean | | 1 | 18 | .050343 | .1305157 | .0307629 | | 2 | 10 | .147825 | .4362644 | .1379589 | Descriptives | | | | | | | | | | N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | Minimum | Maximum | | | | | | Lower Bound | Upper Bound | | | 1 | 18 | .050343 | .1305157 | .0307629 | -.014561 | .115247 | .0056 | .5627 | 2 | 10 | .147825 | .4362644 | .1379589 | -.164259 | .459910 | .0052 | 1.3894 | Total | 28 | .085158 | .2764600 | .0522460 |...
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...than 5. Then check that the Pearson Chi-Square is significant. Lastly look at the “Adjusted Residual” to see if it is greater than 1.96 or less than -1.96. One-Sample T-test Analyze Compare Means One-Sample T-test Choose the scale (interval or ratio) variable on the left to be tested and move it to the right window In the “Test Value” box enter the value you to which you want to compare the mean Click “OK” Output: SPSS gives you a 2-tailed significance value. For a one tailed test you double the significance value. Independent Samples T-test Analyze Compare Means Independent Samples T-test Choose the scale (interval or ratio) variable(s) on the left to be tested and move it to the upper “Test Variable(s)” right window Choose the nominal variable on the left and move it to the “Grouping Variable” box at the bottom right. Click on the “Define Groups” button to tell SPSS which groups to compare. Click “OK” Output: You must examine the Levene’s test to determine which significance value to use. If Levene’s test is significant you use the “Equal variances not assumed” significance value. Otherwise use the “Equal variances assumed” significance value. One-way ANOVA Analyze Compare Means One-way ANOVA Choose the scale (interval or ratio) variable(s) on the left to be tested and move it to the upper “Test Variable(s)” right window Choose the nominal...
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...(CIB pdf formfields Demoversion) BASIC ANALYSIS: A GUIDE FOR STUDENTS AND RESEARCHERS ASSOCIATE PROFESSOR DR ERNEST CYRIL DE RUN DR LO MAY CHIUN HERIYADI KUSNARYADI BASIC ANALYSIS: A GUIDE FOR STUDENTS AND RESEARCHERS 1 (CIB pdf formfields Demoversion) PREAMBLE This book was originally written as notes for my students of EBQ2053 Research Methodology at Universiti Malaysia Sarawak. Nevertheless, as we looked through it and with the various courses and seminars that we have given, we began to realize that what was being said was universal for all researchers, either those just starting out at 2nd year university of seasoned well published researchers. We all need to know the basics. Nevertheless, at the same time, even seasoned researchers tend to forget some methods that they do not always use. Therefore the idea for this book, as a handout for students yet at the same time a quick guide and reference for the seasoned researcher. Please note that we are using SPSS v15 and AMOS v4. May it be of help to all who strive to better themselves. This book is dedicated to or my darling wife, Doren, and my dearest son, Walter. Associate Professor Dr Ernest Cyril de Run 16 November 2007 BASIC ANALYSIS: A GUIDE FOR STUDENTS AND RESEARCHERS 2 (CIB pdf formfields Demoversion) 1. What is SPSS? SPSS refers to computer software named Statistical Program for Social Sciences and it comes in various versions and adds on. It is software and not a method of analysis....
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...Running head: ORGANIZATIONAL COMMITMENT 1 Organizational Commitment of Part-Time and Full-Time Employees Julia A. Teahen Baker College ORGANIZATIONAL COMMITMENT Abstract In recent years many educational institutions have increased their use of part-time adjuncts, especially with the introduction of distance learning courses. With this growing use questions about the efficacy of part-time and distance faculty have arisen. This paper tests whether organizational commitment, as described by Mowday (1979), differs between three groups of faculty: full-time, part-time, and part-time who work full-time for another organization. The 2 authors used a 15 item, seven-point scale instrument to measure commitment of a sample of 479 full and part-time faculty at two midwestern universities. Those that worked full-time for another organization are more committed to organizations for whom they work part-time than are those working only part-time. No significant difference in commitment was observed between full-time and part-time employees. ORGANIZATIONAL COMMITMENT Organizational Commitment of Part-Time and Full-Time Employees With the rapid growth of non-traditional educational institutions and enrollment, many educational institutions have added significant numbers of part-time adjuncts to teach courses. With this growth a number of questions and rhetoric have arisen about the quality of instruction from part-timers (Fulton, 2000; Leatherman, 1998; Rewick, 2001), "overuse...
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...MAN 5246 Organizational Behavior Module 2, Fall 2015 Pluto Candy Company Team Case Exercise Pluto Candy Company Memorandum Date: December 11th, 2015 To: Judith Callahan, PhD Vice-President, Operations From: Employee Attitudes Task Force Re: Employee Attitudes Cc: Clyde Tombaugh, President This memorandum is a response to Judith Callahan’s Pluto Strategic Initiative Program. Our Employee attitude task force has worked diligently and precisely to determine where the Pluto Candy Company stands in terms of achieving a satisfied and committed workforce. Our results and analysis are based off the Employee Attitude Survey and can be found below. Please feel free to contact any member of our Employee Attitudes Task Force for further questions or clarifications. Question 1: One question I’d like your team to consider is whether we should care about employees’ attitudes. Although a satisfied workforce is one of our strategic goals, we need to be sure, before we contemplate specific actions, that such actions are warranted. In order to determine whether we should care about employees’ attitudes, our group conducted a correlation between overall job satisfaction and average performance. We found that there was a statistically significant correlation of 0.359 between the two. Therefore, it is important that Pluto Candy Company should care about its employees’ attitudes. Refer to appendix A for detailed data analysis. Question...
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...Consumer Attitude towards ‘Green’ Purchases *Tanushree Shrivastava Research Scholar(IIPS, DAVV, Indore) Mobile 9926248388 moonskiess@yahoo.com **Dr. Preeti Singh Reader, IIPS, DAVV, Indore Mobile 9425349044 purnima4@rediffmail.com Abstract The Theory of Planned Behavior, based on the value – attitude – behavior hierarchy, has been proven as a reliable instrument for measuring green purchasing behavior. This study examines the application of a sub-section of Theory of Planned Behavior, namely the measurement of Attitudes towards Green Purchases of management students in Jabalpur City. Ecological affect has a greater impact on their attitude formation than does Ecological Knowledge. To check if this is also true for these consumers, management students (n=41) were surveyed to measure their ecological affect, ecological knowledge and attitudes towards green purchases. Students with high ecological affect showed a statistically significant difference on their attitudes towards green purchases score than the students with low ecological affect. Ecological knowledge level did not show a significant difference in attitudes towards green purchases. Differences between ecological knowledge and ecological affect and suggestions for future research are discussed. Keywords: Planned Behaviour, Green Awareness, Green Purchase, Ecological Knowledge, Purchase Attitude. Consumer Attitude towards ‘Green’ Purchases Introduction ...
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...This workshop is a follow-up of the workshop in which you developed your proposal. Now you have the major task ahead of fully analysing the data you brought with you from the field and writing your research report. The report should contain feasible and useful recommendations, based on the findings of your study concerning how to solve the problem investigated. As in the first workshop there will be presentations, group work sessions and a few plenaries. In this workshop, however, group work will take up most of the time. The presentations will be concentrated in the first week, which will be devoted to data analysis. The second week will be fully reserved for report writing, with only two presentations to guide you. Toward the end of that week an important plenary is planned in which each group will present a summary of its main findings and recommendations. A selected group of policy-makers and health managers who requested the study or have a direct interest in the topic and some interested researchers will be invited to comment on your presentation during that plenary. The modules for this workshop cover several major tasks, which are schematically presented in the diagram on the next page of this module. This diagram is presented again at the beginning of each subsequent module, to indicate which task is the focus of the presentation. We will now briefly introduce each of these tasks. Although we trust that all groups have put great effort in the processing...
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...Organizational Environment and Employees Performance With Reference to Multan, Pakistan Muhammad Arif Bahauddin Zakariya University, Multan Email:- arifkhokhar50@gmail.com M.Irfan Khan Bahauddin Zakariya University, Multan Email:- iffibzuen@gmail.com Faisal Saeed Bahauddin Zakariya University, Multan Email:-faisalsaeed364@yahoo.com Muhammad Zain Bahauddin Zakariya University, Multan Email:-muhammadzain_12@hotmail.com Seemab Zehra Bahauddin Zakariya University, Multan Email:-seemabzahra@rocketmail.com ABSTRACT This study measures the impact of organizational environment on the employees’ performance. We have conducted a general survey by well structured questionnaire through convenience sampling. Survey has been conducted across a sample of 100 employees. We have selected six various organizations of Multan as sample case including HBL, Bank Al Habib, Toyota Motors, Mehran Traders and Packages Limited and data has been collected from the employees of various department of the above mentioned organizations. We have taken five major factors (work load, satisfaction, job insecurity, creativity and counterproductive work behavior to measure the influence of organizational environment on the performance of employees. The mean analysis of employees suggests that organizational environment has a strong influence on the performance of employees. It is very interesting to know that there exists no significant difference between both male and female...
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...comparing both data sets. The Phillies have a mean number of wins at 79.72 and the Mets with a mean of 77.28 wins. So, by looking at these data sets from a comparative angle, the Phillies come out on top with an average of about 2-3 wins per season. Taking a look at the medians, you are able to see that Phillies also have a higher mean of 80.5 wins as opposed to the Mets’ 78 wins. By looking at the easy to find statistical values you are able to see which team has been statistically better throughout the past 45 years. After I took a look at the descriptive statistics I ran three different tests: the F-test, the t-test, and the empirical tests to test for each data set’s normality. The first test that I ran in Excel was the F-test, which is a test comparing statistical models to identify the model that best fits the population from which the data was sampled. My results of the F-test came out with a high variance for each team: the Phillies with 133.41 and the Mets with 220.21. The reason for this is most likely based on the fact that there is a wide range of win numbers throughout the years. Wins from the Phillies have a range of 47 and the Mets have a wider range of 67. The degrees of freedom are at 90 which is found by adding the number of observations in each data set and subtracting 2. Continuing, the F-Stat was calculated to be approximately .60583 and...
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...sample t-test, independent samples t-test, paired samples t-test, ANOVA and Chi-test to achieve those goals. How much are potential patrons willing to pay for the entrees? Is the $18 amount from the forecasting model correct? [pic] Graph 1: The frequency of the price of evening meal entrée item alone Based on the process of data screening, I personally believe that 60 data are logically inconsistent. 60 data relating to the price of evening meal entrée customers would like to pay amount to 999 dollars, which has a low possibility in reality. In this case, those data should not be included during analysis. In other words, I accept the null hypothesis and conclude that the average price of evening mean entrée is not significantly different from 18 dollars. Ho: μ=18 H1: μ≠ 18 |One-sample statistics | | |N |Mean |Std. Deviation |Std. Error Mean | |What would you expect an average evening meal entree |340 |$18.8353 |$9.82784 |$.53299 | |item alone to be priced? | | | | | |One-sample Test | | |Test Value...
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...during "dangerous" scenarios. All 20 participants are 18, but only half of them have a driver's license. What statistical test would we use to compare the number or words spoken for people with and without licenses. | | | | | Selected Answer: | Independent samples t test | Answers: | Dependent samples t test | | Independent samples t test | | | | | * Question 2 10 out of 10 points | | | We want to know how having kids affects happiness. We surveyed 100 couples that were expecting a child in the next three months. We went back and surveyed those same 100 couples one year later. What test would we use to compare their previous scores with their current scores? | | | | | Selected Answer: | Dependent samples | Answers: | Dependent samples | | Independent samples | | | | | * Question 3 10 out of 10 points | | | We are doing a dependent samples t-test and we're in the middle of step 4 of our null hypothesis test. We determined that D̅ = .25, SD̅ = .05, n = 9Calculate tD/ SD= Answer | | | | | Selected Answer: | 5 | Answers: | 5 | | 15.006 | | .0166 | | None of the above | | | | | * Question 4 10 out of 10 points | | | I just used Excel to do a two-tailed dependent samples t-test and it gave me the output that I pasted below. How would I list the results of my t-test? Hypothesized...
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...Abstract This paper explores the various areas of basic statistics including: descriptive statistics, correlations, t-tests for independent samples, t-test of dependent samples and data mining used during the research process. In this 2 pages summary of those listed methods, I will identify the keys aspects of its usage, importance in the research process, provide examples of its usage and value and how it will be used my future research projects throughout this coursework. Meaning Use of Statistics Understanding the use of statistics requires one to understand the experimental design or how the research is conducted. Knowledge about the methodology allows use to input and interpret the results of the values. Statistics values are not just random numbers but values that have been generated out of research. Basic statistic values are tools utilized to assist with answering the questions of what, why, and how. Understanding the reasoning for using statistics will better help one’s understanding of basic statistics. Descriptive statistics is a quantitative description of data collection sometimes referred to as inferential statistics. Descriptive statistics are used to summarize the sample and measures of values as they form the basis of quantitative analysis of data (Criswell, 2009). Utilizing descriptive statistics draws conclusions by extending beyond the data known. It utilizes judgments of the probability that are observed between...
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...in making your decisions on rejecting or not rejecting the null hypothesis. 1 Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean. (Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S) Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries? Males Females Ho: Mean salary = 45 Ho: Mean salary = 45 Ha: Mean salary =/= 45 Ha: Mean salary =/= 45 Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances, having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us. Male Ho Female Ho Mean 52 45 Mean 38 45 Variance 316 0 Variance 334.667 0 Observations 25 25 Observations 25 25 Hypothesized Mean Difference 0 Hypothesized Mean Difference 0 df 24 df 24 t Stat 1.96890383 t Stat -1.9132...
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