Introduction
Normally, a dependent variable refers to the variables that are like to change given that they are influenced by the independent variable, (Borich, 2019). In this case, reaction time can be influenced, and it varies depending on the condition and gender, hence it is the dependent variable. On the other hand, independent variables are variables which do change, and they are constant but can influence the dependent variables. Therefore, in this case, both gender and the type of the condition are the independent variables.
In addition, the reaction time is a numerical variable. However, for the chi-square tests, it has been recoded to form a categorical variable. On the other hand, gender and the type of the condition are the categorical variables because they are given in two categories.
In terms of the variable predictions, there are chances that gender have a statistically significant association with reaction time. Moreover, reaction time is expected to vary depending on the condition type.
Formulate hypothesis
The following hypotheses are tested;
Null hypothesis:
- There is no statistically significant gender difference in reaction time, (to be tested by an independent sample t test).
- There is no statistically significant evidence to suggest that there is an effect of reaction time between the two conditions, (to be tested by a spearman correlation test).
- There is no statistically significant relationship between the two conditions to reaction time. (To be tested by a chi-square test).
Alternate hypothesis
- There is a statistically significant gender difference in reaction time, (to be tested by an independent sample t test).
- There is a statistically significant evidence to suggest that there is an effect of reaction time between the two conditions, (to be tested by a spearman correlation test).
- There is a statistically significant relationship between the two conditions to reaction time. (To be tested by a chi-square test).
Some of the advantages of experimental study design include the fact that it is easy to gain valuable insights of the construction methods used, (Maxwell, Delaney, & Kelley, 2017). Moreover, this design is subjected to some form of biasness, but this can be controlled. On limitations, the design is likely to demonstrated errors from the human nature, (Haselton, Nettle, & Murray, 2015). Sample size is too small hence cannot be generalize to other settings.
Furthermore, advantages of between groups is that there is a reduction in boredom due to several tests being performed. Moreover, the advantages of the within-subjects design is that participants are few hence chances of unearthing a true difference among the conditions is increased, (Francis, 2016).
Method
Research study design
For any research problem to be adequately solved, there is need for a good plan to be utilized in order to come up with solutions towards the identified issue under inquiry, (Creswell, & Creswell, 2017). On the same, in order to develop questionnaires and qualitative tools for eliciting information from the sampled populations in this research study, an empirical study design will be considered, (Neuman, 2016). One of the advantages of this design is because individual’s information, attitude and general opinions concerning a specific objective of the study can easily be obtained by identifying their characteristics as well, (Gibson, 2018). Moreover, (Blalock 2018) agree with this and further explain that the Empirical study design is used to describe and to interpret data. In this design, existing associations can be identified practices that prevail, believes. Moreover, existing trends as well as discovering new trends will be easily developed by use of this design. The design is pertinent to the study and enabled the researcher to conveniently gather data sample carefully drawn from the population and use the finding from the sample to make inferences about the population (Montgomery, 2017). The study design did not only enable the researcher to secure evidence of existing situation regarding reaction time among participants, but it also helped in establishing factors contributing to variation of the reaction time by gender and conditions. This enabled the researcher to gather data with an intention of making recommendation for improvement, (Bell, Bryman, & Harley, 2018).
Results
In order to design a study on how age will affect the reaction time among males and females, a prediction model have been generated. The findings in appendices section confirms that there is no statistically significant association between reaction time and other variables, (F=0.511, p>0.05). Similarly, it is predicted that age will not affect the reaction time.
An independent t test has been used to test the first null hypothesis, “There is no statistically significant gender difference in reaction time”.
An independent sample t-test was done to compare gender difference in reaction time. As shown in Table 1, the findings were that the female participants had a higher mean of reaction time score of 30308, with a standard deviation of 15816.8 and standard error of 4227.22 than the male participants who had a mean of 25378.39, with a standard deviation of 4705.79 and standard error of 1109.17.
According the results, there is an indication that the Equality of Variances from the Levene’s Test for was not statistically significant (p =.178 >.05). Hence, there was need to make an assumption of the variances from equal. Based on this, there is evidence of lack of differences that the variances of the means were not statistically significantly related; As a result, the homogeneity of variances’ assumptions were in one way or the other not violated. Due to this finding, the test statistics were read from the Equal variance row not assumed, confirming that the p.v >.05. In other words, on average the female reaction time score (M=30308, SE=4227.22), was not significantly higher than the male reaction time score (M= 25378.39, SE=1109.17), t (30) = – 1.258, p = 178 as shown by the findings of the study. This is presented in table 1a in the appendices section.
Hence from the results of the study, it was credible to conclude that gender has no significant influence on reaction time of participants. Therefore, we fail to reject the null hypothesis that, “there is no statistically significant gender difference in reaction time”.
The spearman Product-Moment correlation was used to test the hypothesis, “here is no statistically significant evidence to suggest that there is an effect of reaction time between the two conditions”. The result indicates that there is no significant correlation between reaction time and condition type. To note, the analysis also revealed that this relationship is not significant (r=-.264, p > 0.05).
Finally, a chi-square test was conducted to test the hypothesis, “There is no statistically significant relationship between the two conditions to reaction time”. Looking at the chisquare test table in the appendices, we fail to reject the null hypothesis that there is no statistically significant relationship between the two conditions to reaction time, (Chisquare=2.0, p>0.05). This means that it is just by chance that some conditions have a higher reaction time compared to other conditions.
Conclusion
In conclusion, the findings were that the female participants had a higher mean of reaction time score than the male participants. It was credible to conclude that gender has no significant influence on reaction time of participants. Therefore, we fail to reject the null hypothesis that, “there is no statistically significant gender difference in reaction time”. Moreover, there is no significant correlation between reaction time and condition type. To note, the analysis also revealed that this relationship is not significant. Additionally, we fail to reject the null hypothesis that there is no statistically significant relationship between the two conditions to reaction time implying that it is just by chance that some conditions have a higher reaction time compared to other conditions.
Discussion
From the results, it was credible to conclude that gender has no significant influence on reaction time of participants. Given the fact the there is no Relationship between gender and the reaction, it is just by chance that reaction times varies by gender. This implies that given equal settings, then the reaction time among genders remains the same. In addition, there is evidence of lack of differences that the variances of the means were not statistically significantly related.
Moreover, the result indicates that there is no significant correlation between reaction time and condition type. In other words, reaction time does not depend on the condition set for the participants. Finally, there is no statistically significant relationship between the two conditions to reaction time. This means that it is just by chance that some conditions have a higher reaction time compared to other conditions.
References
Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press.
Blalock Jr, H. M. (2018). Causal inferences in nonexperimental research. UNC Press Books.
Borich, G. D. (2019). Educational Psychology A Contemporary Approach.
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Francis, G. (2016). Implications of “Too Good to Be True” for Replication, Theoretical Claims, and Experimental Design: An Example Using Prominent Studies of Racial Bias. Frontiers in psychology, 7, 1382.
Gibson, L. (2018). Decision Making among Human Resource Professionals: A Quantitative Non-Experimental Cross-Sectional Study of Methods and Outcomes (Doctoral dissertation, Northcentral University).
Haselton, M. G., Nettle, D., & Murray, D. R. (2015). The evolution of cognitive bias. The handbook of evolutionary psychology, 1-20.
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective. Routledge.
Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
Neuman, W. L. (2016). Understanding research. Pearson.
