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The results of the test between subjects shows no significance, which means that the presence of a relationship has no influence on the evaluation.

But I would also like to test this seperate for men and women.

Well, It is a test between subjects. I first tested this hypothesis: The presence of a relationship between manager and employee positively effects the performance evaluation of the employee.

The results of the test between subjects shows no significance, which means that the presence of a relationship has no influence on the evaluation.

But I would also like to test this seperate for men and women.

Did you included the null-hypothesis that there is no effect between a manager and an employee in your significance test?

I have attached a file with my outputs, I hope that's what you were asking for.

Basically what I did was an experiment with to scenarios. The first one described a friend (this means that there is a relationship present) and the second a relation that is strictly professional.

I deleted the responses of participants who didn't aswered the questions correctly or failed the manipulation check.

No, I think didn't do that at all. I simply put the data in SPSS (Analyze>General Lineair Model>Univariate). With performance as the DV and relation and gender as Fixed Factors.

I'm assuming you did included the null-hypothesis. However, my question is what is the probability or assumption of the null-hypothesis based on? For example, suppose there's a hypothesis that states that smokers get lung cancer and a null-hypothesis that smokers don't get lung cancer. We can conduct studies for this, but we can't calculate significance because it isn't really that specific. However, if the hypothesis said that smokers have a 75% chance of getting lung cancer and the null-hypothesis said that smokers don't have a 75% of getting lung cancer, then we can calculate significance because it refers to a specific population (e.g. 75%) instead of the whole population. If smokers really do have a 75% chance of getting lung cancer, then we would expect 75% of smokers in our studies to have the disease. Makes sense?

Hypothesis 1: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

Hypothesis 2: The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee.

Then, you adviced me to do a one-tailed t-test for just only women and another one-tailed t-test for just only men. In SPSS this is the independent samples T test (Analyze>Compare Means>Independent samples t test),

For example, I have selected all the women in my sample and performed an independent samples T test. You can find the output in the attachment. The output shows a p-value of 0,532 (2-tailed). Based on an Alfa of 5% I can accept the null hypothesis, which was (after adjustment): The presence of a relationship between a female manager and employee has no effect on the performance evaluation of the employee.

For men, the p-value is 0,626, which also means that we should accept the null hypothesis (The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee).

So this means that gender has no effect on the performance evaluation in the presence of a relationship.

There is another thing that confuses me I thought this is a 2x2 design, because:

Performance = Gender x Relation

But a friend of mine says this is a 2x1 design, based on my original three hypotheses, which are:

- Hypothesis 1: The presence of a relationship between manager and employee effects positively the performance evaluation of the employee.

- Hypothesis 2: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

- Hypothesis 3: The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee.

Is she

Thank you!!

Yes! Thank you, this makes sense to me.

I know my hypotheses are not specific enough but I would like to check if I understood you well based on the hypotheses I already have set up and these were:

Hypothesis 1: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

Hypothesis 2: The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee.

I understand that my two hypotheses are not consistent with each other. So for both hypotheses the null hypothesis should be: there is no effect and the alternative hypothesis should be: there is an effect.**Right?**

Hypothesis 1: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

Hypothesis 2: The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee.

I understand that my two hypotheses are not consistent with each other. So for both hypotheses the null hypothesis should be: there is no effect and the alternative hypothesis should be: there is an effect.

Then, you adviced me to do a one-tailed t-test for just only women and another one-tailed t-test for just only men. In SPSS this is the independent samples T test (Analyze>Compare Means>Independent samples t test), **right?** Because, my DV is performance and my IV is relation.

For example, I have selected all the women in my sample and performed an independent samples T test. You can find the output in the attachment. The output shows a p-value of 0,532 (2-tailed). Based on an Alfa of 5% I can accept the null hypothesis, which was (after adjustment): The presence of a relationship between a female manager and employee has no effect on the performance evaluation of the employee.

**Right?**

One-tailed p-value=two-tailed p-value/2. Thus, p=0.532/2=0.26. Therefore, the one-tailed p-value is 0.26

For men, the p-value is 0,626, which also means that we should accept the null hypothesis (The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee). **Right?**

So this means that gender has no effect on the performance evaluation in the presence of a relationship. **Right?**

There is another thing that confuses me I thought this is a 2x2 design, because:

Performance = Gender x Relation

But a friend of mine says this is a 2x1 design, based on my original three hypotheses, which are:

- Hypothesis 1: The presence of a relationship between manager and employee effects positively the performance evaluation of the employee.

- Hypothesis 2: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

- Hypothesis 3: The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee.

Is she**right?**

Performance = Gender x Relation

But a friend of mine says this is a 2x1 design, based on my original three hypotheses, which are:

- Hypothesis 1: The presence of a relationship between manager and employee effects positively the performance evaluation of the employee.

- Hypothesis 2: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

- Hypothesis 3: The presence of a relationship between a male manager and employee has no effect on the performance evaluation of the employee.

Is she

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I have another question. You said:

The reason why I advice you to do a one-tailed t-test is because the effect within those two alternative hypothesis seem to make a prediction in only one direction. I can, of course, be wrong because it is possible that the effect could go to the other direction as well. For example, a positive effect could refer to the employee doing better in its performance whereas a negative effect could refer to the employee doing bad in its performance.** If positive and negative effect apply, then you should you use a two-tailed t-test.** If it's either positive or negative, you should use a one-tailed t-test.

H1: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

Using a two-tailed t-test, because the performance could be affected positively or negetively by the presence of a relation. How do I know if this effect is negative or positive?

Dear Musibrique,

Assuming H0: The presence of a relationship between a female manager and employee has no effect on the performance evaluation of the employee.

H1: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

Using a two-tailed t-test, because the performance could be affected positively or negetively by the presence of a relation. How do I know if this effect is negative or positive?

Assuming H0: The presence of a relationship between a female manager and employee has no effect on the performance evaluation of the employee.

H1: The presence of a relationship between a female manager and employee positively effects the performance evaluation of the employee.

Using a two-tailed t-test, because the performance could be affected positively or negetively by the presence of a relation. How do I know if this effect is negative or positive?

What would you define as a positive/negative correlation?

Once you answered, ask yourself this:

What correlation are you exactly looking for in your research hypothesis? Are you only looking for a positive correlation between a female manager and employee or a negative correlation or both correlations?

Hope that helps!

A female manager gives her employee a rating for his/her performance. The scale of the rating is 0-100. So when there is a relationship between the female manager and employee, in this case the rating should be higher than in the case where there is a strictly professional relation. So "positive" means a higher rating of performance.

I'm looking for the correlation between the given performance rating and the status of the relation.

The Pearson's correlation is positive does this has anything to do with it?

If you perform a t-test and the p-value is 0,532, then you accept the null hypothesis. But is the p-value is 0.001 then you should accept H1.

When you accept H1, I would say yes there the relation positively effects the performance rating, because there is a positive correlation between employee's performance rating and the status of the relation.

Or am I wrong?

Correct! So the correlation between employee's performance rating and the status of the relation is positive.

If you perform a t-test and the p-value is 0,532, then you accept the null hypothesis. But is the p-value is 0.001 then you should accept H1.

When you accept H1, I would say yes there the relation positively effects the performance rating, because there is a positive correlation between employee's performance rating and the status of the relation.

Or am I wrong?

When you accept H1, I would say yes there the relation positively effects the performance rating, because there is a positive correlation between employee's performance rating and the status of the relation.

Or am I wrong?

As for the conclusion, you can say there is a relationship if your independent t-test is statistically significant; however, it is important to know that the relationship may not be practically significant as you think. This is one problem with t-tests. They don't tell you how strong the relationship is, only evidence. You can, however, use Pearson's correlation to determine how strong is the effect. You can also calculate the effect-size as well, but that may be too advance for you.