DUE 3/9/19  8 P.M EST BE ON TIME AND ORIGINAL WORK Have SPSS SOFTWARE AND G POWER DONT ASK IF YOU DON’T HAVE IT READ INSTRUCTIONS CAREFULLY DATA ATTACHED AND HELPFUL STEP BY STEP GUIDE!! In research, calculating power is essential. The larger a sample size, the more powerful a study may be. It can be helpful to calculate the minimum sample size needed for basic statistics—such as the t-test—without the aid of software. Sample size calculation is based on the statistical testing method used. Remember, sample size denotes the number of variables examined in data collection. You use the software G*Power located to do your calculations. Calculating power based on sample size, rather than calculating sample size based on power, is useful when using secondary data where the sample size is already fixed. For this Assignment, you practice manually calculating basic sample size. You also practice calculating power with the assistance of G*Power software located in the Learning Resources. Using the  Dataset (SPSS document) ATTACHED

Power analysis is an essential aspect of research, particularly in determining the appropriate sample size for a study. The power of a study refers to its ability to detect a statistically significant effect, should one exist. In other words, power represents the probability of correctly rejecting the null hypothesis when the alternative hypothesis is true.

When conducting a power analysis, researchers typically specify four key elements:
1. The desired level of statistical significance (α), which is commonly set at 0.05.
2. The effect size, which reflects the magnitude of the effect being studied.
3. The desired power level (1-β), which is commonly set at 0.80 or 0.90.
4. The sample size, which represents the number of participants or observations in the study.

Calculating the minimum sample size required to achieve a desired level of power can be done using statistical software, such as G*Power. This software allows researchers to determine the necessary sample size for different statistical tests, based on the specified parameters.

However, in some cases, researchers may need to manually calculate the sample size without the aid of software. For example, when working with secondary data where the sample size is fixed, it may be necessary to determine the power based on the available sample size.

In this assignment, you will practice both manual calculation of sample size and using G*Power software to determine power.

To begin, you will need to access the provided dataset, which is in SPSS format. SPSS, or Statistical Package for the Social Sciences, is a widely used software for statistical analysis.

First, let’s focus on calculating the minimum sample size needed for basic statistical tests, such as the t-test. The sample size calculation is determined by the specific statistical test being used.

For instance, in a t-test, the sample size calculation is based on the following formula:

n = (Zα + Zβ)^2 * (σ^2) / δ^2

where:
– n represents the sample size
– Zα represents the critical value corresponding to the desired level of significance (α)
– Zβ represents the critical value corresponding to the desired level of power (1-β)
– σ^2 represents the population variance
– δ represents the desired effect size

By substituting the values for Zα, Zβ, σ^2, and δ into the formula, you can manually calculate the minimum sample size needed for a t-test.

Furthermore, you will also practice calculating power using the G*Power software. G*Power provides a user-friendly interface for power analysis and sample size determination for a wide range of statistical tests.

By inputting the necessary parameters, such as the effect size, significance level, and desired power, G*Power will calculate the sample size required to achieve the desired level of power.

Overall, this assignment will provide hands-on experience in both manual calculation of sample size and using G*Power software to determine power. By mastering these skills, you will be better equipped to design studies with appropriate sample sizes and sufficient power to detect meaningful effects.