SPSS provides a wide range of statistical tests for quantitative research and analysis. It is a popular software used to explore and interpret quantitative data. Many different tests are available, but some of the most common are listed below.
New researchers should familiarise themselves with these important tests before starting their research and analysing results from a quantitative perspective.
While choosing a statistical test in SPSS, consider the number of variables you are analysing, the type of data for each variable (such as Nominal, Ordinal, or Scale), and whether your data meets the requirements of parametric tests. The following table summarises some common tests in SPSS to help you select the right one for your analysis.
Let's now look into these key SPSS tests.
Pilot testing is a small-scale trial run of a research study that takes place before the main study begins. Its primary purpose is to assess and improve the research design, methods, and tools to ensure the main study proceeds without issues and in a right direction.
It is not meant to test hypotheses or produce results that can be generalised. It just validates a particular approach and method. It is important to understand that pilot test's sampling is not included in the main study's data collection.
Purpose of Pilot Testing
Pilot testing is essential for checking the reliability and validity of your research tools, especially questionnaires and surveys. These measures determine how consistent and accurate your data will be.
Reliability
Reliability indicates how consistent a measure is. If you give the same test to the same person multiple times, their scores should be similar. Internal consistency is the most common way to assess this in a pilot study.
Cronbach's Alpha (α) is the standard statistical measure for internal consistency. It evaluates how closely related a group of items is. The value ranges from 0 to 1.
Excellent: > 0.9
Good: > 0.8
Acceptable: > 0.7
Poor: < 0.6
A score of 0.70 or higher is typically seen as acceptable for a research tool. A very high alpha (e.g., > 0.95) may indicate that some items are redundant and can be removed.
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Validity
Validity refers to the accuracy of a measure, indicating whether your tool is indeed measuring what it is intended to measure. While there are various types of validity, pilot testing mainly helps with a few key aspects.
Content Validity is often assessed by having a panel of experts review your tool. They check if the questions or items sufficiently cover all aspects of the concept you wish to measure. For instance, a survey about "job satisfaction" should include questions on pay, work-life balance, and relationships with colleagues to ensure good content validity. A Content Validity Index (CVI) of 0.8 or higher is generally considered acceptable.
Face Validity is a basic, non-statistical measure. It simply asks whether your instrument seems to measure what it intends to measure at first glance. For instance, a survey about English Language learning with words like "reading", "writing" and "speaking" would have good face validity. This is generally checked by participants in the pilot test.
These tests assume that your data follows a specific distribution, usually a normal distribution.
T-tests are used to compare means between two groups. Types include:
One-sample T-test: Compares a sample mean to a known population mean. For instance, you might use this to see if the average IQ score of your student group differs significantly from the national average of 100.
Independent-samples T-test: Compares the means of two separate, independent groups. An example would be to determine if there is a significant difference in writing scores between male and female students.
Paired-samples T-test: Compares the means of a single group at two different times or under two conditions. This test is useful for "before and after" studies, such as testing if a new diet plan significantly changes a person’s weight.
ANOVA (Analysis of Variance) is an extension of the t-test and compares the means of more than two groups. It tests if there is a statistically significant difference between group means but does not indicate which specific groups differ.
One-way ANOVA: Compares means of one independent variable with several levels. For instance, this can show if there is a difference in exam performance based on varying levels of test anxiety (low, medium, high).
Repeated-measures ANOVA: Compares means from the same group across multiple time points.
Two-way ANOVA: Used when you have two or more independent variables. This allows you to examine the individual effects of each variable and their interaction effect on the dependent variable. For instance, you could explore how listening and speaking together affect English Language proficiency level.
These tests do not assume a normal distribution and are often used with ordinal or nominal data.
Chi-square Test: The Chi-Square test examines the relationship or association between two categorical variables. It compares the observed frequencies in your data to the expected frequencies if there were no relationship between the variables.
Chi-Square Test of Independence: This popular type determines if there is a significant association between two categorical variables. For instance, you could test if there is a relationship between low literacy level and poverty.
Chi-Square Goodness of Fit: This tests if the observed proportions of a single categorical variable differ from a set of expected proportions.
Mann-Whitney U Test: A non-parametric alternative to the independent-samples t-test.
Wilcoxon Signed-Rank Test: A non-parametric alternative to the paired-samples t-test.
Kruskal-Wallis H Test: A non-parametric alternative to the one-way ANOVA.
Regression and Correlation
These tests analyze relationships and predict outcomes between variables.
Correlation measures the strength and direction of a linear relationship between two or more continuous variables. The result is a correlation coefficient, Pearson's (r), which ranges from -1 to +1.
A value of +1 indicates a perfect positive correlation (as one variable increases, the other increases).
A value of -1 indicates a perfect negative correlation (as one variable increases, the other decreases).
A value of 0 indicates no linear relationship.
For instance, correlation can explore the link between the number of hours a student studies and his/her exam scores. A positive correlation would imply that more study hours lead to higher scores.
Linear Regression predicts the value of a continuous dependent variable based on one or more independent variables.
Simple Linear Regression uses one independent variable.
Multiple Linear Regression involves two or more independent variables.
Logistic Regression predicts the probability of a categorical outcome.
✍ By: Raja Bahar Khan Soomro
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