Monitoring and Evaluation Studies - SPSS
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enPearson Correlation Coefficient Between Groups
http://mnestudies.com/research/pearson-correlation-coefficient-between-groups
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<h2>
Pearson Correlation</h2>
<p class="rtejustify">To illustrate how to compare correlation between two groups. The article would use dataset of <a href="http://www.hrnutshell.com/DownFiles/Islamic.sav"><strong>Islamic.sav</strong></a>. The Questionnaire was designed to evaluate the factors that affect people’s attitude towards Islamic banking. There may be situation when you need to compare the correlation coefficient between two groups. For instance in this dataset, we may need to compare the responses between male and female respondents. How to do it is described below If you wish to follow along with this example, you should start SPSS and open the <a href="http://www.hrnutshell.com/DownFiles/Islamic.sav"><strong>Islamic.sav</strong></a> file.</p>
<h2>
Correlation Coefficient between Two Groups</h2>
<h3>
Steps to compare Correlation Coefficient between Two Groups</h3>
<p>First we need to split the sample into two groups, to do this follow the following procedure</p>
<ol start="1" type="1">
<li>
From the menu at the top of the screen, click on <strong>Data</strong>, and then select <strong>Split File</strong>.</li>
<li>
Click on <strong>Compare Groups</strong>.</li>
<li>
Move the grouping variable (e.g. Gender) into the box labeled <strong>Groups based on</strong>. Click on <strong>OK</strong>.</li>
<li>
This will split the sample by gender.</li>
</ol>
<p class="rtejustify">Follow the steps in the article (<a href="http://hrnutshell.com/topics/topics-covered-group1-key-to-survival/research/data-analysis/item/278-running-pearson-correlation">Running Pearson Correlation</a>) to request the correlation between your variables of interest. The results will be reported separately for the two groups.</p>
<p class="rtejustify">It is Important to remember, when you are finished looking at males and females separately you will need to turn the <strong>Split File </strong>option off. It stays in place until you manually turn it off. To do this, make sure that you have the <strong>Data Editor</strong> Window open on the screen in front of you. Click on <strong>Data</strong>, <strong>Split File </strong>and click on the first button: <strong>Analyze all cases, do not create groups</strong>.</p>
<p class="rtejustify">The output generated from the correlation procedure is shown below.</p>
<h3>
Interpretation of output from correlation for two groups</h3>
<p class="rtejustify">From the output given above, the correlation between ATIB and SI for males was r=.262, while for females it was slightly higher, r=.293. Although these two values seem different, is this difference big enough to be considered significant? Detailed in the next section is one way that you can test the statistical significance of the difference between these two correlation coefficients. It is important to note that this process is different from testing the statistical significance of the correlation coefficients reported in the output table above. The significance levels reported above (for males: Sig. = .000; for females: Sig. = .116) provide a test of the null hypothesis.</p>
<p class="rtejustify">What might be confusing for you at this stage is that although the Correlation Coefficient for <strong>Male’s</strong> is low but it is still significant, but the coefficient for <strong>female group </strong>is slightly higher but it is still insignificant. The reason for this is the number of cases in each group. The sample size for male groups is significantly higher (N = 235) in comparison to female group (N = 30).</p>
<h3>
Statistical Significance for difference between Groups</h3>
<p class="rtejustify">While you now know how to find correlation coefficient in each of the groups, but still we do not know if the difference in relationship between groups is significant. This section describes the procedure that can be used to find out whether the correlations for the two groups are significantly different. Unfortunately, SPSS will not do this step for you, so it is done manually. Step by Step procedure to find out if the relationship is significantly different you can follow the following steps.</p>
<p class="rtejustify">First we will be converting the r values into z scores and then we use an equation to calculate the observed value of z (zobs value). The value obtained will be assessed using a set decision rule to determine the likelihood that the difference in the correlation noted between the two groups could have been due to chance.</p>
<p class="rtejustify">Before calculating the statistical significance you will check certain assumptions.</p>
<ol start="1" type="1">
<li>
It is assumed that the r values for the two groups were obtained from random samples and that the two groups of cases are independent (not the same participants tested twice).</li>
<li>
The distribution of scores for the two groups is assumed to be normal.</li>
<li>
It is also necessary to have at least 20 cases in each of the groups.</li>
</ol>
<p><strong>Convert each of the r values into z values</strong></p>
<p class="rtejustify">First step is to convert the correlation coefficients (r) into the Z scores. From the SPSS output, find the r value (ignore any negative sign out the front) and N for Group 1 (males) and Group 2 (females).</p>
<p>Males r1 =.262 N1 =235</p>
<p>Females r2 =.293 N2 =30</p>
<p>Using the following , find the <em>z </em>value that corresponds with each of the r values.</p>
<p>Males z1 =.266</p>
<p>Females z2 =.304</p>
<p><strong>Put these values into the equation to calculate zobs</strong></p>
<p class="rtejustify">The equation is provided below, put the respective values in the equation and make the necessary calculations.</p>
<p><strong>Determine if the zobs value is statistically significant</strong></p>
<p>If the zobs value that you obtained is between 1.96 and +1.96, this means that there is no statistically significant difference between the two correlation coefficients. We can only reject the null hypothesis (no difference between the two groups) <em>only </em>if your z value is outside these two boundaries. The decision rule therefore is:</p>
<ol start="1" type="1">
<li>
If 1.96 < zobs < 1.96: correlation coefficients are not statistically significantly different.</li>
<li>
If zobs is less than or equal to 1.96 or zobs is greater than or equal to 1.96: coefficients are statistically significantly different.</li>
</ol>
<p>In the example above, zobs value of .206, that is between the boundaries, so we can conclude that there is a no statistically significant difference in the strength of the correlation between ATIB and SI for males and females.</p></div></div></div>
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Sat, 12 Sep 2015 12:49:32 +0000MnE Expert176 at http://mnestudies.comhttp://mnestudies.com/research/pearson-correlation-coefficient-between-groups#commentsVariables, Hypothesis and Hypothesis Formulation
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<h2>
Hypothesis Development</h2>
<p class="rtejustify">Once it has been decided that a research be conducted to solve the problem, the researcher would develop a plan or a theoretical framework. Theoretical framework help identify the factors that are important to the study and aik in development of the hypothesis.</p>
<p class="rtejustify">A hypothesis can be defined as a logically conjectured (Formation/Expression of Opinion) between two or more variables exposed in the form of a testable statement. The Statement is a proposition and either is proved significant or is insignificant/Not Proven. Before explaining Hypothesis let us put some light on Variable and their types.</p>
<h2>
Variable</h2>
<p class="rtejustify">A variable is anything that can take varied values, for example age is a variable and can take age of different employees, Gender is a variable and can either be male or female, Designation is another variable in more abstract form Motivation is another variable, as motivation in people can range from very low to very high.</p>
<h3>
Types of Variables</h3>
<p class="rtejustify">Although there are 4 types of variables, but at present moment out interest is on the Dependent and Independent variables.</p>
<h4>
1. The Dependent Variable</h4>
<p class="rtejustify">Dependent Variable is the variable of primary interest, the ultimate aim is explain the variation of the dependent variable, or predict it. It is a variable whose values vary and are dependent on other factors. For Instance A President of an Organisation is concerned with the lack of loyalty of the staff, here the dependent variable is loyalty. Or another example could be that A Manager is concerned with the turnover rate among his employees, here the turnover rate is the dependent variable.</p>
<h4>
2. The Independent Variable</h4>
<p class="rtejustify">The Independent Variable is on that influences the Dependent variable and brings changes in its values, Variation of the dependent variable is influenced by the Independent Variable, a change in Independent variable changes that state/value of the dependent variable. Variable that influences the dependent variable and brings a positive or negative change in its values is independent variable.</p>
<p>For Instance</p>
<p><strong>Example 1: Employee Salaries and Turnover Rate</strong></p>
<p class="rtejustify">Here the Turnover Rate would be the dependent variable, as Organizations where Salaries are higher the turnover rate would be lower, and vice versa. The bringing a positive change in employee Salaries could significantly reduce employee turnover, making Salaries an Independent Variable whereas Turnover rate a dependent variable. </p>
<p><strong>Example 2: Training and Performance</strong></p>
<p class="rtejustify">In Example 2, Employee Training is termed as Independent variable and Performance is Dependent variable, because it can be hypothesized that the better the training provided the improved performance could be expected from the employee, thus Employee Performance is dependent on the effectiveness of training.</p>
<h2>
Hypothesis Formulation</h2>
<p>The expression of opinion/conjectured relationship between variables is formulated in the form of a statement that is then tested to see the significance of relationship. For instance the hypothesis for Example 1 could be formulated as</p>
<p><em>If the salaries of the employees are increased it will lower the turnover rate.</em></p>
<p>For example 2 the hypothesis that can be formulated is</p>
<p><em>A Successful training program improves the performance of the employees</em></p>
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Wed, 08 Jul 2015 22:15:32 +0000MnE Expert172 at http://mnestudies.comhttp://mnestudies.com/research/variables-hypothesis-hypothesis-formulation#commentsOne-Way ANOVA - Variance Analysis
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<p class="rtejustify">Where Independent Samples T-test was used to compare a sample across two groups, there might be situations when a dependent variable might be categorized on more than two variables and then the sample is to be compared across three groups. for instance Comparing Work Stress in Junior, Middle and Senior level employees, comparing Job Satisfaction in College, Graduate and Postgraduate Education level employees or making comparison of Commitment to Change in professions as Doctors, Engineers, Teachers, Bankers and Marketers.</p>
<p class="rtejustify">It is important to understand that we have one variable that is categorized/divided into various groups/samples and those samples are then compared with each other. This is the ultimate objective of One Way ANOVA (Analysis of Variance).Example of research question: Is there a difference in optimism scores for young, middle-aged and old participants?</p>
<h2>
What you need: Two variables:</h2>
<ul type="disc">
<li>
One categorical independent variable with three or more distinct categories. This can also be a continuous variable that has been recoded to give three equal groups (e.g. age group: participants divided into three age categories, 29 and younger, between 30 and 44, 45 or above)</li>
<li>
One continuous dependent variable (e.g. optimism scores).</li>
</ul>
<p>A few example scenarios/hypothesis in which we would use One-Way ANOVA are identified for the understanding of the readers</p>
<ol start="1" type="1">
<li>
The average sale of the new brand of gasoline is same in all the 3 metro cities.</li>
<li>
There are differences in Work Morale across 4 occupations.</li>
<li>
Is there a change in confidence scores over the 3 time periods?</li>
</ol>
<p class="rtejustify">It is important to note that in each of the above hypothesis, there is one continuous variable (Average Sale, Work Morale and Confidence Scores) that is compared across different groups (3 Metro Cities, 4 Occupations and 3 Time Periods). Now to run the One Way ANOVA, follow the following steps</p>
<p>Click on the Analysis Tab, Select <strong>Compare Means</strong> > <strong>One-Way ANOVA</strong></p>
<p class="rtejustify">Select the variable, that you would want to compare across different groups, In this case we would select stress with <strong>Intrinsic_Factors</strong> from the variable window and put it in the dependent list and would compare the variable across different occupations. The suggessted hypothesis for this test is that "The are differece in Stress with intrinsic factors across the 4 occupations". Adding the Variable the dialog box should look like</p>
<p class="rtejustify">Now Click on Option and Select <strong>Homogeneity of variance test</strong> and press continue</p>
<p class="rtejustify">Two Tables are shown in the output window, Here each of the tables are explained, the first table is the <strong>Test of Homogeneity of Variances</strong>, this table shows, if the Variances in the Data across the groups are similar or not, to explain it further, in this test we are checking Stress with intrinsic factors across the 4 occupations, Now the test would check if the Variances in the Data for Intrinsic Stress are same for each of the occupation groups i-e Banker, Teacher, Marketer and Engineer, if the value of <strong>Sig</strong> is greater than 0.05 we would say that <strong>Equal Variances are assumed</strong>, otherwise <strong>Equal Variances not Assumed</strong>. In this case we would say that variance in the data for stress are similar in the 4 occupations</p>
<p class="rtejustify">The Next table of ANOVA (Also shown below) shows that if differeces exist in the Stress with Intrinsic Job Factors across the four occupations or not, Sig value of 0.50 shows that there are differences across the four occupations, if the value would have been greater than 0.50, we would have inferred that there exist no differences in Stress with Intrinsic Factors across occupations. meaning all occupants feel similar kind of stress pertinent to the intrinsic job factors.</p>
<h2>
Reporting ANOVA Table</h2>
<p class="rtejustify">The ANOVA summary table suggests, the Stress relating to Intrinsic Job Factors across the four occupations under study differed significantly (F3,138 = 2.665, p = .050)<br />
Since now we know that differences do exist, we need to evaluate that between which occupations does the differences exist, and for this we would conduct a Post Hoc Analysis. for this purpose, Select One-Way ANOVA from the Menu and after selecting the Continuous variable add grouping variable, press Post Hoc button, you will see the following dialog box</p>
<p class="rtejustify">There are two groups, Equal Variances Assumed and Equal Variances Not Assumed, In this case the Test of Homogeneity of Variances revealed <strong>Equal Variance Assumed</strong>, so we select a test from Equal Variances Assumed, in this case we select LSD, you can select any, mostly LSD, Bonferroni, TUkey or Tukey's-b are used. After selection of LSD press continue, then press OK, apart from the other table new table of Multiple Comparisons is also displayed, that would make comparisons for Intrinsic Factors between each of the occupations.</p>
<p class="rtejustify">The above table shows the differences prevalent between two occupations, The Table Shows that No Differences exist in Stress with Intrinsic Factors between Banker and Teacher since the Sig. value is greater tha .05, however there are significant differences how Stresst with intrinsic factors affects Banker and Teacher, Since the Significance (Sig.) value is less than .05.</p>
<h2>
Reporting Multiple Comparisons Table</h2>
<p class="rtejustify">Post-hoc analysis [LSD] were conducted to explore differences pertinent to Stresst with Intrinsic Job Factors among the four occupations groups. There was a significant difference between Banker and Marketing Job [mean difference = .47636, p < .01]. however no differences were recorded between anyother occupations. Finally we would reject the null hypothesis and accept the alternate hypothesis.</p></div></div></div>
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Mon, 06 Jul 2015 20:59:20 +0000MnE Expert169 at http://mnestudies.comhttp://mnestudies.com/research/one-way-anova-variance-analysis#commentsHow to Use Pearson Correlation
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<p class="rtejustify">To illustrate how to use Correlation I would use dataset of <a href="http://www.hrnutshell.com/DownFiles/Islamic.sav"><strong>Islamic.sav</strong></a>. The Questionnaire was designed to evaluate the factors that affect people’s attitude towards Islamic banking. In this example I am interested in assessing the correlation between attitude towards Islamic banking and the Social Influence. If you wish to follow along with this example, you should start SPSS and open the <a href="http://www.hrnutshell.com/DownFiles/Islamic.sav"><strong>Islamic.sav</strong></a> file.</p>
<h2>
Example on Running Pearson Correlation</h2>
<h3>
The Problem:</h3>
<p>Investigate the relationship between Social Influence and attitude to Islamic banking.</p>
<p><strong>Null Hypothesis</strong></p>
<p><strong>H0:</strong>There is no association between Social Influence and Attitude towards Islamic Banking.</p>
<p><strong>HA:</strong>There is an association between Social Influence and Attitude towards Islamic Banking.</p>
<p><strong>Information Required:</strong></p>
<ul type="disc">
<li>
Two continuous variables (In this case, Social Influence and Attitude)</li>
</ul>
<h3>
Assumptions for Pearson Correlation</h3>
<ul type="disc">
<li>
At least two continuous variables (Interval or Ratio) or One Continuous variable and other is dichotomous scale variable</li>
<li>
If there is a dichotomous variable you should, however, have roughly the same number of people or cases in each category of the dichotomous variable.</li>
<li>
Normal distribution for Continuous Variable</li>
</ul>
<h3>
Steps to run Pearson Correlation</h3>
<ol start="1" type="1">
<li class="rtejustify">
Choose <strong>Analyze â†’ Correlate â†’ Bivariate</strong></li>
<li class="rtejustify">
Choose the variables for which the correlation is to be studied from the left-hand side box and move them to the right-hand side box labeled <em>Variables</em>. Once any two variables are transferred to the variables box, the <em>OK </em>button becomes active. We can transfer more than two variables, but for now we will stick to only two.</li>
<li class="rtejustify">
Select the variable <strong>ATIB</strong> (Attitude towards Islamic Banking) and <strong>SI</strong> (Social Influence). Press the Arrow button to the add the variable to the <strong>Variables:</strong> list box</li>
<li class="rtejustify">
There are some default selections at the bottom of the window; these can be changed by clicking on the appropriate boxes. For our purpose, we will use the most commonly used Pearson’s coefficient. <strong>Pearson</strong> checkbox is check from the <strong>Correlation Coefficient</strong> group box</li>
<li class="rtejustify">
Next, while choosing between one-tailed and two-tailed test of significance, we have to see if we are making any directional prediction. The one-tailed test is appropriate if we are making predictions about a positive or negative relationship between the variables; however, the two-tailed test should be used if there is no prediction about the direction of relation between the variables to be tested. In this case we will stick to two-tailed test.</li>
<li class="rtejustify">
Finally <strong>Flag significant correlations </strong>asks SPSS to print an asterisk next to each correlation that is significant at the 0.05 significance level and two asterisks next to each correlation that is significant at the 0.01 significance level.</li>
<li class="rtejustify">
<strong>Press OK</strong></li>
</ol>
<h3>
Output</h3>
<p>The output of the analysis is shown below, the results shows only one table</p>
<h3>
Interpretation of Output</h3>
<p class="rtejustify">For Pearson Correlation, SPSS provides you with a table giving the correlation coefficients between each pair of variables listed, the significance level and the number of cases. The results for Pearson correlation are shown in the section headed <strong>Correlation</strong>.</p>
<p class="rtejustify">The tables shows that a total of 265 respondents. First it is important to consider is the direction of the relationship between the variables. This is identified through a negative sign in front of the correlation coefficient value? A negative sign before the correlation coefficient means that there is a negative correlation between the two variables (i.e. high scores on one are associated with low scores on the other).</p>
<p class="rtejustify">The interpretation of relationship depends how the variables are scored. Checking the Questionnaire, it shows that higher scores on the construct <strong>Attitude towards Islamic Banking </strong>means positive attitude similarly higher scores on Social Influence means greater social influence. This is one of the major areas of confusion for students, so make sure you get this clear in your mind before you interpret the correlation output.</p>
<p class="rtejustify">In the example given here, the Pearson correlation coefficient (.267) indicating a positive correlation between <strong>Social influence</strong> and <strong>Attitude towards Islamic Banking</strong>. The more the social influence on people with regards to Islamic banking, the positive would be the attitude of people towards Islamic banking. To determine the strength of relationship we would use the table 11.1 presented earlier, using the table the correlation matrix shows that there is a Very low Positive between the two variables.</p>
<p class="rtejustify">The Correlation Coefficient can be used to assess how much variance the two variables share.</p>
<p class="rtejustify">This can be done by squaring the r value (multiply it by itself) also called the Coefficient of Determination, to convert this to â€˜percentage of variance’; just multiply by 100 (shift the decimal place two columns to the right).</p>
<p class="rtejustify">In our example we have the coefficient value of .267, two variables that correlate to get the coefficient of determination we square the r value and the result is .0712, and the percentage of variance is 7.12. This shows that Social Influence indicates 7.12% variance in Attitude towards Islamic banking.</p>
<p class="rtejustify">The next thing to consider is the significance level (listed as <strong>Sig. 2 tailed</strong>). This is a frequently misinterpreted area, so care should be exercised here. The level of statistical significance does not indicate how strongly the two variables are associated (this is given by r), but instead it indicates how much confidence we should have in the results obtained. The significance of r is strongly influenced by the size of the sample. In a small sample (e.g. n=30), you may have moderate correlations that do not reach statistical significance at the traditional p<.05 level. In large samples (N=100+), however, very small correlations (e.g. r=.267) as in our case, it may reach statistical significance. While you need to report statistical significance, you should focus on the strength of the relationship and the amount of shared variance (explained earlier).</p>
<h3>
Reporting Pearson Correlation</h3>
<p>Pearson product correlation social influence and attitude towards Islamic banking is very low positive and statistically significant (<em>r </em>= 0.267).</p>
<h3>
Correlation Matrix</h3>
<p class="rtejustify">Correlation is often used to explore the relationship among a group of variables, rather than just two as described above. In this case, it would be awkward to report all the individual correlation coefficients in a paragraph; it would be better to present them in a table also referred to as correlation matrix. SPSS results provide the table that can be made part of the thesis.</p>
<p class="rtejustify">In order to produce a correlation matrix showing relationships between more than two variables, you need to add more than two variable on which the relationships is intended to be studied. For our example we would add the 6 critical factors and attitude towards Islamic banking. Follow the steps mentioned above, add the factors between which the correlation is to be evaluated.</p>
<p class="rtejustify">Press OK, the following correlation matrix is displayed in the output window.</p>
<p class="rtejustify">The output gives correlations for all the pairs of variables and each correlation is produced twice in the matrix. The Correlations are repeated under the number 1 in the diagonal. You can consider the correlation in either of the diagonal. It would be better to present them in a table. One way this could be done is as follows:</p>
<p class="rtejustify">In each cell of the correlation matrix, we get Pearson’s correlation coefficient that shows the strengths of the relationship, which could be evaluated using the table described earlier, the significance is shows through asterisks right next to the correlation coefficient. A Single * shows that correlation is significant at .05 (5%) while ** shows that correlation is significant at .01 (1%). From the output, we can see that the correlation coefficient between ATIB and SI is 0.267 which is very low positive and significant at .01. Similarly the correlation coefficient between ATIB and RC is 0.485 which is and is low positive and significant at .01. Results for correlations between other set of variables can also be interpreted similarly. Coefficient not having the asterisks sign are not significant related and the strength of relationship is almost negligible.</p></div></div></div>
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Sun, 05 Jul 2015 13:02:01 +0000MnE Expert167 at http://mnestudies.comhttp://mnestudies.com/research/how-use-pearson-correlation#commentsReliability Analysis in SPSS
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<p class="rtejustify">How legitimate or justifiable a research is, that is based on an inconsistent instrument? Consistency of data is central to the concept of reliability. Reliability simply refers to the confidence a researcher places on the questionnaire to provide the same numeric value when the measurement is repeated on the same subject. How to do it is described below. If you wish to follow along with this example, you should start SPSS and open the <a href="http://hrnutshell.com/topics/topics-covered-group1-key-to-survival/research/data-analysis/item/download/58"><strong>Training Finalized.sav</strong></a> file.</p>
<h2>
Reliability Coefficient</h2>
<p class="rtejustify">Commonly used technique for assessing reliability is Cronbach’s <em>alpha </em>for internal reliability of a set of questions (Interval and Ratio Scale). Ideally, the Cronbach’s alpha coefficient of a scale should be above .7. Following guideline developed Gliem & Gliem (2003) by presented in the table can be a guide to evaluate the reliability coefficient.</p>
<div align="center">
<table border="1" cellpadding="0" cellspacing="0">
<tbody>
<tr>
<td width="216">
<p><strong>Cronbach’s Alpha value</strong></p>
</td>
<td width="156">
<p><strong>Interpretation</strong></p>
</td>
</tr>
<tr>
<td width="216">
<p><strong>Greater than .90</strong></p>
</td>
<td width="156">
<p>Excellent</p>
</td>
</tr>
<tr>
<td width="216">
<p><strong>Greater than .80</strong></p>
</td>
<td width="156">
<p>Good</p>
</td>
</tr>
<tr>
<td width="216">
<p><strong>Greater than .70</strong></p>
</td>
<td width="156">
<p>Acceptable</p>
</td>
</tr>
<tr>
<td width="216">
<p><strong>Greater than .60</strong></p>
</td>
<td width="156">
<p>Questionable</p>
</td>
</tr>
<tr>
<td width="216">
<p><strong>Greater than .50</strong></p>
</td>
<td width="156">
<p>Poor</p>
</td>
</tr>
<tr>
<td width="216">
<p><strong>Less than .50</strong></p>
</td>
<td width="156">
<p>Unacceptable</p>
</td>
</tr>
</tbody>
</table>
</div>
<h2>
Cronbach’s Alpha</h2>
<p class="rtejustify">Cronbach’s alpha values are quite sensitive to the number of items in the scale. With short scales (e.g. scales with fewer than ten items) it is common to find quite low Cronbach’s values (e.g. .5). Reliability is normally reported under the head of instrumentation in the methodology section. If your scale contains some items that are negatively worded (common in psychological measures), these need to be ‘reversed’ <em>before </em>checking reliability. For instance we have a Scale named “Work Morale” having the following questions answered on likert scale (1 – Strongly Disagree to 5 – Strongly Agree)</p>
<ol start="1" type="1">
<li>
The atmosphere at work is pretty good.</li>
<li>
Everyone around here looks forward to come to work.</li>
<li>
The Company is going places.</li>
<li>
There is no future for this company (R).</li>
<li>
We all pull together this company.</li>
</ol>
<p class="rtejustify">We can see that item 4 is in reverse order, it is negative while all other questions are positive. Now when you enter the response for item 4 into SPSS, you need to reverse the entry, for instance if the respondent has said 5, you will enter 1 into SPSS, similarly 4 will be exchange with 2 and vice versa.</p>
<p><strong>Procedure for checking the reliability of a scale </strong></p>
<ol>
<li>
1. Choose <strong>Analyze → Scale → Reliability Analysis</strong></li>
<li>
2. You will see Reliability Analysis dialog box.</li>
<li>
3. Select the items whose reliability is to be assessed from the variable list box. Select only the variables for One Construct (Scale) at a time. For this example we will select TNA1 to TNA4 related to scale Training Needs Analysis (Once Added your Dialog box should resemble the one in figure 8.1).</li>
<li>
4. Add the selected list of variables to <strong>items</strong> list box.</li>
<li>
5. Click on the <strong>Statistics </strong>button which will open a dialog box. Check <strong>Item, Scale,</strong> and <strong>Scale if item deleted </strong>from <strong>Descriptive Statistics</strong> group box. Click on <strong>Continue</strong> to return to the main dialog box then click on <strong>OK </strong>to run the analysis.</li>
</ol>
<p>The output from analysis is shown below:</p>
<h3>
Interpretation of Results</h3>
<p class="rtejustify">The output shows a number of tables. The first table shows the <strong>Case Processing Summary, </strong>showing the total number of valid cases and if any data was excluded from the analysis. The second table of <strong>Reliability Statistics</strong>is the table of interest, it gives the value of the Cronbach’s alpha and the number of items selected for the scale. For our scale of <strong>Training Needs Analysis</strong>Cronbach’s alpha value reported to be 0.900. This recommends that the scale is consistent and highly reliable.</p>
<p class="rtejustify">SPSS also provides us with descriptive statistics. The table titled <strong>Item Statistics</strong>gives item-wise mean and standard deviation values. <strong>Item-Total Statistics</strong>tableis important. The fourth column in this table, titled <strong>Corrected Item-Total Correlation</strong>gives an indication of the degree to which each item correlates with the composite score for the scale. The last column labeled <strong>Cronbach’s Alpha if Item Deleted</strong>can help improve the reliability of the scale. It shows if removing a certain item will improve the overall reliability of the scale, however in this particular case the Cronbach’s alpha won’t improve by removing any of the items.</p>
<p><strong>Please Check!</strong></p>
<ul type="disc">
<li class="rtejustify">
Check that the number of cases is correct (in the <strong>Case Processing Summary </strong>table) and that the number of items is correct (in the <strong>Reliability Statistics </strong>table). In this particular case both are correct, none of the cases are excluded and the number is items are 4 which are correct.</li>
<li class="rtejustify">
Check the <strong>Inter-Item Correlation Matrix </strong>for negative values. All values should be positive, indicating that the items are measuring the same underlying characteristic and account for the same construct. The presence of negative values could indicate that some of the items have not been correctly reverse scored.</li>
</ul>
<h3>
Improving Reliability</h3>
<p>The overall reliability of the scale can be improved by following a few simple guidelines:</p>
<ul type="disc">
<li class="rtejustify">
The <strong>Corrected Item-Total Correlation </strong>column in the <strong>Item-Total Statistics </strong>table provides an indication of the degree to which each item correlates with thetotal score. Low values (less than .3) here indicate that the item is measuring somethingdifferent from the scale as a whole. If scale’s overall Cronbach’s alpha istoo low (e.g. less than .7) and you have checked for reverse items that might not have been entered properly, It would be a good idea to consider removing items with low item-total correlations.</li>
<li class="rtejustify">
In the column headed <strong>Alpha if Item Deleted</strong>, the impact of removing each item from the scale is given. Compare these values in the column headed <strong>Alpha if Item Deleted</strong> with the alpha value obtained. If any of the values in this column are higher than the final alpha value, you may want to consider removing this item from the scale.</li>
</ul>
<h3>
Reporting Cronbach’s Alpha</h3>
<p class="rtejustify">It is normally reported in the methodology section where instrument are discussed. After discussing the scale i-e their Number of items in the scale, scale for response you can describe the reliability of the instrument. An example of how to report is shared below.</p>
<p class="rtejustify">“For recruitment there were ten items asking how the organizations recruit new employees. The reliability coefficient for recruitment was 0.787.”</p>
<p>(Source: Hashim, J. (2010). Human resource management practices on organisational commitment: The islamic perspective. <em>Personnel Review, 39</em>(6), 785-799.)</p>
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Sun, 05 Jul 2015 12:40:15 +0000MnE Expert166 at http://mnestudies.comhttp://mnestudies.com/research/reliability-analysis-spss#commentsScales of Measurement
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<p class="rtejustify">Scales of Measurement in a nutshell refers to various measures of the variables researchers use in their research, variables in the research are fall in one of 4 scales of measurement that will be discussed in this article. They hold prime importance in the Data Analysis, as the type of measure determine the kind of test to be used for data analysis. thus without understanding the concept of Scales of Measurement one could not have solid grasp of data analysis techniques.</p>
<!--break-->
<p class="rtejustify">Scales of Measurement in a nutshell refers to various measures of the variables researchers use in their research, variables in the research are fall in one of 4 scales of measurement that will be discussed in this article. They hold prime importance in the Data Analysis, as the type of measure determine the kind of test to be used for data analysis. thus without understanding the concept of Scales of Measurement one could not have solid grasp of data analysis techniques. Different items of the questionnaire are treated as variables, for instance Age is one variable,</p>
<p class="rtejustify">The Question "<strong>Do you Enjoy your work</strong>" is another varibale, Gender is another varibale or Occupation could be another varibale, All items are treated as separate variables in the <a href="http://hrnutshell.com/topics/topics-covered-group1-key-to-survival/research/data-analysis/item/download/24">Questionnaire</a>. All variables would fall in one of the 4 scales of measurement identified below</p>
<ul type="disc">
<li>
Nominal</li>
<li>
Ordinal</li>
<li>
Interval</li>
<li>
Ratio</li>
</ul>
<p class="rtejustify">The process of measurement involves assigning numbers to observations according to rules. The way that the numbers are assigned determines the scale of measurement. Each scale of measurement represents a particular property or set of properties of the abstract number system. The mathematical properties of the numbers we are going to analyze are important because they determine statistical techniques to be used.</p>
<p class="rtejustify">The properties of the abstract number system that are relevant to scales of measurement are identity, magnitude, equal interval, and absolute/true zero.</p>
<h3>
Identity</h3>
<p class="rtejustify">Identification refers to assignment of a number to respondents response, and these number are just for the sake of identification and the numbers itself cannot be used in mathematical operations thus numbers assigned are just to convery a particular meaning. for instance Assigning 1 to Male, 2 to Female, Here we could have assigned 1 to Female and 2 to Male, and that would have made no difference. Variable having Identity property where number are assigned to values of the variable for the purpose of identification are measured on Nominal Scale.</p>
<h3>
Magnitude</h3>
<p class="rtejustify">Moving one step ahead, a variable could have Identification and Magnitude as well, meaning that numbers have an inherent order from smaller to large. for instance Postion in Class, Level of Education or Rank in Organisation. Here the values of the variable have numbers for identification but also the values have some order for example the position variable has number 1, 2 and 3 for identification but all these 3 position have an order as well because the difference of Marks between 1st and 2nd could be 30 whereas difference between 2nd and 3rd could be of 50 marks, meaning on the continuum the difference is not the same. Variables having Identity and Magnitude are measured on Ordinal Scale.</p>
<h3>
Equal Intervals</h3>
<p class="rtejustify">It Means that difference between numbers anywhere on the scale are the same, for instance take the variable Position, now it is measured on Ordinal Scale but not on Interval Scale because the distance between 1st and 2nd position may well not be the same as 2nd and 3rd, or 3rd and 4th. Here the distance refers to the Marks obtained by the position holders. In Most business researches Likert Scale variables are taken as having equal interval. or any variable where the difference between two units is the same as difference between any of the following following or previous two units for instance the difference between 4 and 5 is the same as the difference between 76 and 77 i-e 1. Variables with Equal Intervals, Magnitude and Identification Properties are measured on Interval Scale.</p>
<h3>
Absolute/true zero</h3>
<p class="rtejustify">Means that the zero as a response represents the absence of the property being measured (e.g., no money, no behavior, none correct) but temperature on 0 is not absolute zero as it still has some effect and we cannot say no temperature. Thus putting a variable or user response in Nominal, Ordinal Interval or Ratio scale depends on the above mentioned properties it shows.</p>
<p class="rtejustify">Next we would discuss the <a href="http://www.mnestudies.com/research/types-measurement-scales">4 scales of Measurements</a>, Namely Nominal, Ordinal, Interval and Ratio in greater detail. If you have any queries please use the feedback link on top of the page and we would make sure we answer your queries.</p></div></div></div>
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Fri, 03 Oct 2014 07:50:31 +0000MnE Expert142 at http://mnestudies.comhttp://mnestudies.com/research/scales-measurement#commentsTypes of Measurement Scales
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<p class="rtejustify">Operations applied to various variables from the Questionnaires in the SPSS depends on Scale assigned to the variables. Assigning a particular scale of measurement depends on the numerical properties variable have, as discussed in the last article "Scales of Measurement".</p>
<!--break-->
<p class="rtejustify">Operations applied to various variables from the Questionnaires in the SPSS depends on Scale assigned to the variables. Assigning a particular scale of measurement depends on the numerical properties variable have, as discussed in the last article "Scales of Measurement".</p>
<p class="rtejustify">In today's article various scales that are used in data analysis are discussed.</p>
<p class="rtejustify">There are 4 scales of measurement, namely Nominal, Ordinal, Interval and Ratio, all variables fall in one of these scales.Understanding the mathematical properties and assigning proper scale to the variables is important because they determine which mathematical operations are allowed. That determines statistical operations we can use. Operations applied to various variables from the Questionnaires in the SPSS depends on Scale assigned to the variables. Assigning a particular scale of measurement depends on the numerical properties variable have, as discussed in the last article "Scales of Measurement".</p>
<p class="rtejustify">In today's article various scale that are used in data analysis are discussed. There are 4 scales of measurement, namely Nominal, Ordinal, Interval and Ratio, all variables fall in one of these scales. Understanding the mathematical properties and assigning proper scale to the variables is important because they determine which mathematical operations are allowed. That determines statistical operations we can use.</p>
<p class="rtejustify">The 4 scales are in the order of Nominal, Ordinal, Interval and Ratio scale with Nominal having least mathemathical properties, followed by Ordinal and Interval, whereas Ratio having most mathemathical properties.</p>
<h2>
Nominal Scale</h2>
<p class="rtejustify">From the Statistical point of view it is the lowest measurement level. Nominal Scale is assigned to items that is divided into categories without having any order or structure, for instance Colors do not have any assigned order, We can have 5 colors like Red, Blue, Orange, Green and Yellow and could number them 1 to 5 or 5 - 1 or number them in a mix, here the numbers are assigned to color just for the purpose of identification, and ordering them Ascending or Descending doesnt mean that Colors have an Order. The number gives us the identity of the category assigned. The only mathematical operation we can perform with nominal data is to count. Another example from research activities is a YES/NO scale, which is nominal. It has no order and there is no distance between YES and NO.</p>
<h2>
Ordinal Scale</h2>
<p class="rtejustify">Next up the list is the Ordinal Scale. Ordinal Scale is ranking of responses, for instance Ranking Cyclist at the end of the race at the position 1, 2 and 3. Not these are rank and the time distance between 1 and 2 may well not be the same as between 2 and 3, so the distance between points is not the same but there is an order present, when responses have an order but the distance between the response is not necessarily same, the items are regarded or put into the Ordinal Scale. Therefore an ordinal scale lets the researcher interpret gross order and not the relative positional distances.</p>
<p class="rtejustify">Ordinal Scale variables have the property of Identity and Magnitude. The numbers represent a quality being measured (identity) and can tell us whether a case has moreof the quality measured or lessof the quality measured than another case (magnitude). The distancebetween scale points is not equal. Ranked preferences are presented as an example of ordinal scales encountered in everyday life.</p>
<h2>
Interval Scale</h2>
<p class="rtejustify">A normal survey rating scale is an interval scale for instance when asked to rate satisfaction with a training on a 5 point scale, from Strongly Agree, Agree, Neutral, Disagree and Strongly Disagree, an interval scale is being used. It is an interval scale because it is assumed to have equal distance between each of the scale elements i.e. the Magnitude between Strongly Agree and Agree is assumed to be the same as Agree and Strongly Agree. This means that we can interpret differences in the distance along the scale. We contrast this to an ordinal scale where we can only talk about differences in order, not differences in the degree of order i-e the distance between responses.</p>
<h3>
Properties of Interval Scales</h3>
<p>Interval scales have the properties of:</p>
<ul type="disc">
<li>
Identity</li>
<li>
Magnitude</li>
<li>
Equal distance</li>
</ul>
<p class="rtejustify">Variables which fulfill the above mentioned properties are put in this scale. The equal distance between scale points helps in knowing how many units greater than, or less than, one case is from another. The meaning of the distance between 25 and 35 is the same as the distance between 65 and 75.</p>
<h2>
Ratio Scale</h2>
<p class="rtejustify">A Ratio Scale is at the top level of Measurement. The factor which clearly defines a ratio scale is that it has a true zero point. The simplest example of a ratio scale is the measurement of length (disregarding any philosophical points about defining how we can identify zero length) or money. Having zero length or zero money means that there is no length and no money but zero tempratue is not an absolute zero, as it certainly has its effect. Ratio scales of measurement have all of the properties of the abstract number system.</p>
<h3>
Properties of Ratio Scale</h3>
<ul type="disc">
<li>
Identity</li>
<li>
Magnitude</li>
<li>
Equal distance</li>
<li>
Absolute/true zero</li>
</ul>
<p class="rtejustify">These properties allow to apply all possible mathematical operations that include addition, subtraction, multiplication, and division. The absolute/true zero allows us to know how many times greater one case is than another. Variables falling in this category and having all the above mentioned numerical properties fall in ratio scale.</p>
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Sat, 26 Jul 2014 19:35:40 +0000MnE Expert103 at http://mnestudies.comhttp://mnestudies.com/research/types-measurement-scales#comments