Stressor Factors Among Lecturers in UCTS
- Dependent Variable (DV) = Stressor (Lecturer)
- Independent Variable (IV)
- IV1 = Administration
- IV2 = Students
- IV3 = Economy
- IV4 = Social Status
- IV5 = Facilities
An independent variable (IV) is a variable that stands alone and isn't changed by the other variables which are trying to measure. The independent variable is the factor that is given to the subjects within the experiment. This is controlled by the experimenting scientist.
A dependent variable (DV) is a variable in a mathematical equation or statement whose value depends on that taken on by the independent variable. The dependent variable is what you measure in the experiment and what is affected during the experiment. The dependent variable responds to the independent variable.
* IV is the cause to make DV to change/ happen and vice versa
Type of SSPS Analysis | Detail |
Correlation | - Statistical technique to determine the dependence between two or more variables. In another word, Correlation is a statistical technique that can show whether and how strongly pairs of variables are related.
- It is derived from the same Latin word correlation, which means relation.
- Correlations measure how variables are related.
- Data. Use symmetric quantitative variables for Pearson’s correlation coefficient. Before calculating a correlation coefficient, screen your data for outliers (which can cause misleading results).
- Assumptions. Pearson’s correlation coefficient assumes that each pair of variables is bivariate normal and it is a measure of linear association. Two variables can be perfectly related, but if the relationship is not linear, Pearson’s correlation coefficient is not an appropriate statistic for measuring their association.
- systematic changes in the value of one variable (DV) are accompanied by systematic changes in the other (IV)
- sig. = sigma value（show the significant）
- sig. value below than 0.001 = two variables are perfectly related in a positive linear sense = IVn will affect the DV
- sig. value more than 0.001 = two variables are perfectly related in a negative linear sense = IVn would not affect/ less influence to the DV
- In statistics, the correlation coefficient r measures the strength and direction of a linear relationship between two variables on a scatterplot. The value of r is always between +1 and –1. To interpret its value, see which of the following values your correlation r is closest to:
- Exactly –1. A perfect downhill (negative) linear relationship
- –0.70. A strong downhill (negative) linear relationship
- –0.50. A moderate downhill (negative) relationship
- –0.30. A weak downhill (negative) linear relationship
- 0. No linear relationship
- +0.30. A weak uphill (positive) linear relationship
- +0.50. A moderate uphill (positive) relationship
- +0.70. A strong uphill (positive) linear relationship
- Exactly +1. A perfect uphill (positive) linear relationship
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Linear and Multiple Regression | - linear regression models the relationship between a dependent variable and one or more explanatory variables (IV) using a linear function.
- If two or more explanatory variables (IV) have a linear relationship with the dependent variable, the regression is called a multiple linear regression. (but one from the several MLRs will be the most dominant IV)
- Multiple regression, on the other hand, is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.
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Frequencies | - a descriptive statistical method that shows the number of occurrences of each response chosen by the respondents.
- When using frequency analysis, SPSS Statistics can also calculate the mean/ median (for interval/ ratio data : test scores, age…) and mode (nominal data : for device type, device colour, ethnicity…) to help users analyse the results and draw conclusions.
- Example : gender, age, religion ……
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Part B
- Do minimum of 2 Frequencies Analysis according to the data given.
[pic 1]
[pic 2]
Age |
| Frequency | Percent | Valid Percent | Cumulative Percent |
Valid | 18-25 Years | 5 | 5.4 | 5.4 | 5.4 |
26-30 Years | 31 | 33.3 | 33.3 | 38.7 |
31-35 Years | 22 | 23.7 | 23.7 | 62.4 |
36-40 Years | 10 | 10.8 | 10.8 | 73.1 |
41-45 Years | 10 | 10.8 | 10.8 | 83.9 |
46-50 Years | 7 | 7.5 | 7.5 | 91.4 |
50 Years and above | 8 | 8.6 | 8.6 | 100.0 |
Total | 93 | 100.0 | 100.0 |
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Gender |
| Frequency | Percent | Valid Percent | Cumulative Percent |
Male | 49 | 52.7 | 52.7 | 52.7 |
Female | 44 | 47.3 | 47.3 | 100.0 |
Total | 93 | 100.0 | 100.0 |
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