for sir-excellence

Date

Reaction Time – Are men or women Faster?

Introduction

Reaction time has a long history of being studied. Reaction times have been studied since the mid 1900’s (Silverman, 2010, para 2). Reaction time is the measure of response to stimulus such as simple tasks. Reaction times vary depending on the type of tasks involved. For instance, “simple reaction time (RT) is shorter than a recognition reaction time, and choice reaction time (CRT) takes even longer because the subject must choose a specific response corresponding to the stimulus” (Norton, Norton, & Lewis, 2016). Reaction time is important in day to day life because speed of response and decision making are very important, especially in emergency situations.

It has been debated whether men or women have faster reaction times to reaction time experiments or whether other factors influence results, such as athleticism or age. There have been many types of experiments on reaction time and the focus varies. In an article testing reaction times in the Olympic games and comparing them to gender specifically, it was found that “Women not participating in the Olympic Games have been reported to exhibit slower simple reaction times than men” (Lipps, Galecki, & Aston-Miller, 2011). There have also been studies that test whether auditory or visual reaction times are faster. In a study conducted with first year medical students, the study showed that in “both sexes the RT to the auditory stimulus was significantly less (P < 0.001) as compared to the visual stimulus” (Jain, Bansal, Kumar, & Singh, 2015).

Simple reaction time is also influenced by the level of physical activity one is involved (Beashel & Taylor, 2001). Men are mainly known to be more physically involved and active than women. According to some scientists, people who are active have a faster simple reaction rate when compared to people who are less involved in activities. Men are known to carry out heavy duties as compared to women who carry out less heavy duties. This in turn, may affect how quickly men and women react to reaction time tasks.

It is hypothesized that men will have a faster reaction time in a simple reaction time task than in women regardless of the age. According to Kosinsky, “males have faster reaction times than females, and female disadvantage is not reduced by practice” (2010, para. 20). This study will test the hypothesis to see if that is correct. With the advancements of science, it has been possible to get a more accurate reading of subjects’ reaction times by using computer software. Computer software has the ability to accurately record how long a participant takes to respond to the given cue. When designing a computerized reaction time task, it is important to take into account differences in things like color preferences. Factors like hand dominance, age, and gender all affect results. According to Kalyanshetti, “visual reaction time with red color elicits a faster response when compared to green” (2014).

Roeckelein describes in his book that the women in a study “reacted slowly to a light reaction time task as compared to men regardless of the men and women’s age” (2000, pg. 209). This study takes 160 participants, both men and women, and tests them on a computer that records the results. Each participant is tested alone, without interruption. Since this study uses a simple reaction time (SRT), “there is only one stimulus and one response” (Kosinsky, 2010, para 1).  Participants will hit a key when the color red appears on the screen. As results are compared, it will be decided whether there is a difference in the reaction times of men and women and if men have faster reaction times in this experiment.

Methods

In order to determine the rate at which men and women respond to certain stimuli, it is important to consider certain methods used to collect the data for analysis. Most of these methods are selected based on previous research methods. The aspect of reaction times requires effective and efficient methods to come up with reliable data. Using methods which have not been proven can provide misleading results. A simple reaction time (SRT) test is an important technique that will help in determining the response of the participants. As stated earlier, the number of participants is 160 and comprises both men and women. The number of men tested is 79 and that of women is at 81. All participants will be tested separately and as soon as the participant sees the color on the screen they will have to make a choice. The computer will play a major role in recording the reaction time to recognize the color and the ability to make a choice.

Method1: Simple Reaction Time – this method captures the ability of the participant to move his or her finger to the buttons in order to make a choice. Similarly, it gives the reaction of the participant. The procedure is as follows;

1. Place finger 8cm above button

2. Start the color motion on the screen

3. Record the time it takes to hit the button using the computer

4. Repeat

Method 2: The choice reaction time test – the participant will have to make a choice and the time taken to make a choice will be captured by the computer under the choice column. The procedure is the same as in method 1.

Method 3: the recognition time – the Go/No-Go time is important and it is given by the time that it takes the participants to identify the red color as it appears on the screen. This motor speed will be important since the colors will keep changing on the screen. The procedure is the same as in method 1.

Results

The experiment has some variables which are quite important. The gender, age and times of the experiments are some of the notable variables that are used in the experiment. Based on these variables, it is easy to determine the level of alertness and the motor speed of the participants as the red color appears on the screen. These variables can be explained as follows;

Independent variable – The gender, time, and the times of the experiment are acting as the independent variables and their existence in the experiment does not depend on another.

Dependent variable – The dependent variables are the Go/No-Go, simple and the choice times.

The recorded data is as follows for both men and women.

Male data

Female data

Analysis of Results

The analysis of female results

The female participants have the following outcome of the mean and standard deviation.

Simple reaction times for the female

The plots when plotted implies that most of the data at a 95% significant level (represented by the blue dotted line) will all be evenly distributed about the mean of 0.694086 (the red line). At the age 17 the female participants have a simple reaction time of 0.163351.

The regression analysis implies that the variation between the values of the Rsq= 25.06% when the significance is take at 0.001

GNG reaction time

The optimization based on a regression analysis shows that at the age of 17(tallest bar) the females are quick in recognizing the light. The time it takes is 0.197056.

Choice Reaction Time

At the significance level of 95% and mean time of 0.619852 the quickest respondents are between the age of 17 and 25 where by the mean of the response is at 17.

The relationship between the change time and the age is shown by the following graph.

Also, the report on the choice time vs. age indicates the dominance occurring and the age of 17 (tallest bar).

Analysis of results the male

The male participants had the following results on the mean and the standard deviations of the data collected.

Simple reaction times for male

The simple, GNG, and choice reaction times are as follows: 17 years in all the cases with means of 0.832519, 0.426342 and 0.500962 respectively.

The distribution of the data about the means is further shown in this table at 95% significance level.

Furthermore, the choices among the men are distributed as follows:

Given the significance level of 0.001, the percentage variation in the values of the SRT and the age is given by Rsq= 59.79 at the significance level of 95%.

Conclusion

The statistical methods used is quite instrumental in determining the validity and the reliability of the data obtained during the experiment of the 160 participants. Furthermore, the distribution of the dependent variable in the graphs, shows the data values and the regression. The women in this experiment have more active and short recognition time of the red light as it appears on the screen. This implies that women have a better recognition capability of the light as compared to men. This experiment also showed that the simple, go/no go, and choice times of the individuals below 25 years is quite quick in both genders compared to those above 26. In a nutshell, the reaction times vary significantly based on the gender and the age of the participants.

References

Beashel, P., Sibson, A., & Taylor, J. (2001). The world of sport examined. Cheltenham: Nelson Thornes.

Jain, A., Bansal, R., Kumar, A., & Singh, K. D. (2015). A comparative study of visual and

auditory reaction times on the basis of gender and physical activity levels of medical

first year students. International Journal of Applied & Basic Medical Research,5(2),

124-127. doi:10.4103/2229-516X.157168

Kalyanshetti, S. B. (2014). Effect of Colour of Object on Simple Visual Reaction Time in

Normal Subjects. Journal Of Krishna Institute Of Medical Sciences (JKIMSU), 3(1), 96-98.

Kosinski, R. J. (2010). A Literature Review on Reaction Time. Retrieved July 8, 2016, from

http://biae.clemson.edu/bpc/bp/Lab/110/reaction.htm

Lipps, D. B., Galecki, A. T., & Ashton-Miller, J. A. (2011). On the Implications of a Sex

Difference in the Reaction Times of Sprinters at the Beijing Olympics. Plos ONE, 6(10), 1-5. doi:10.1371/journal.pone.0026141

Norton, K., Norton, L., & Lewis, N. (2016). Effects of Short-Term Physical Activity

Interventions on Simple and Choice Response Times. Biomed Research International, 1-8 8p. doi:10.1155/2016/5613767

Roeckelein, J. E. (2000). The concept of time in psychology: A resource book and annotated bibliography. Westport, Conn. [u.a.: Greenwood Press

Silverman, I. (2010). Simple reaction time: It is not what it used to be. The Journal of

Psychology, 123(1), 39-50. doi:1. Retrieved from http://www.jstor.org/stable/10.5406/amerjpsyc.123.1.0039 doi:1

1st Quartile20.000

Median26.000

3rd Quartile38.000

Maximum55.000

27.08431.854

23.21331.000

9.34312.762

A-Squared2.39

P-Value<0.005

Mean29.469

StDev10.787

Variance116.352

Skewness0.721136

Kurtosis-0.530349

N81

Minimum17.000

Anderson-Darling Normality Test

95% Confidence Interval for Mean

95% Confidence Interval for Median

95% Confidence Interval for StDev

52.545.037.530.022.5

Age

F

r

e

q

u

e

n

c

y

p

e

r

G

N

G

t

i

m

e

Median

Mean

323028262422

95% Confidence Intervals

Summary Report for Age

GNG time Vs. Age of female

1st Quartile20.000

Median26.000

3rd Quartile37.000

Maximum58.000

26.93531.900

23.00030.664

9.58313.142

A-Squared2.60

P-Value<0.005

Mean29.418

StDev11.082

Variance122.810

Skewness0.853229

Kurtosis-0.259028

N79

Minimum17.000

Anderson-Darling Normality Test

95% Confidence Interval for Mean

95% Confidence Interval for Median

95% Confidence Interval for StDev

54484236302418

Age of the male

F

r

e

q

u

e

n

c

y

p

e

r

s

i

m

p

l

e

t

i

m

e

Median

Mean

323028262422

95% Confidence Intervals

Summary Report for Age_1

simple time vs. age

Predicted Y0.366024Age_117

95% PI(0.16322, 0.56883)

Goal: Minimize GNG_1Solution: Optimal Setting

320.46107(0.25999, 0.66216)

340.46674(0.26541, 0.66808)

360.47077(0.26930, 0.67223)

380.47314(0.27164, 0.67465)

400.47387(0.27237, 0.67537)

420.47295(0.27143, 0.67448)

440.47038(0.26868, 0.67209)

460.46617(0.26399, 0.66835)

480.46031(0.25718, 0.66343)

500.45280(0.24805, 0.65755)

140.33589(0.12847, 0.54332)

520.44364(0.23637, 0.65091)

540.43283(0.22191, 0.64376)

560.42038(0.20443, 0.63633)

580.40628(0.18373, 0.62883)

600.39053(0.15960, 0.62147)

620.37314(0.13188, 0.61439)

160.35639(0.15235, 0.56043)

180.37524(0.17342, 0.57707)

200.39245(0.19191, 0.59298)

220.40801(0.20807, 0.60794)

240.42191(0.22209, 0.62174)

260.43418(0.23416, 0.63419)

280.44479(0.24442, 0.64515)

300.45375(0.25301, 0.65450)

XPredicted Y95% PI

605040302010

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Age

G

N

G

Prediction Plot

dashed lines show the 95% prediction interval.

The red fitted line shows the predicted Y for any X value. The blue

To obtain additional predicted values, right-click the graph and use the crosshairs tool.

Multiple Regression for GNG_1

Prediction and Optimization Report

Regression of GNG

1st Quartile20.000

Median26.000

3rd Quartile37.000

Maximum58.000

26.93531.900

23.00030.664

9.58313.142

A-Squared2.60

P-Value<0.005

Mean29.418

StDev11.082

Variance122.810

Skewness0.853229

Kurtosis-0.259028

N79

Minimum17.000

Anderson-Darling Normality Test

95% Confidence Interval for Mean

95% Confidence Interval for Median

95% Confidence Interval for StDev

54484236302418

Age

F

r

e

q

u

e

n

c

y

p

e

r

C

h

o

i

c

e

t

i

m

e

Median

Mean

323028262422

95% Confidence Intervals

Summary Report for Age_1

Choice vs. Age

statistically significant (p < 0.10).

The relationship between Y and the X variables in the model is

YesNo

00.1> 0.5

P < 0.001

model.

59.79% of the variation in Y can be explained by the regression

LowHigh

0%100%

R-sq = 59.79%

for Simple_1.

the X variables that correspond to a desired value or range of values

Simple_1 for specific values of the X variables, or find the settings for

If the model fits the data well, this equation can be used to predict

X1^2, X2^2, X1*X2

X2: Time In Experiment_1

X1: Age_1

relationship between Y and the X variables:

The following terms are in the fitted equation that models the

6

0

4

0

2

0

4

2

0

1

0

0

0

5

0

00

Age_1Time In Expe

Is there a relationship between Y and the X variables?

Comments

Simple_1 vs X Variables

not in the model.

represents an X variable

A gray background

Multiple Regression for Simple_1

Summary Report

% of variation explained by the model

statistically significant (p < 0.10).

The relationship between Y and the X variables in the model is

YesNo

00.1> 0.5

P < 0.001

model.

25.06% of the variation in Y can be explained by the regression

LowHigh

0%100%

R-sq = 25.06%

for Simple.

the X variables that correspond to a desired value or range of values

Simple for specific values of the X variables, or find the settings for

If the model fits the data well, this equation can be used to predict

X1: Age

relationship between Y and the X variables:

The following terms are in the fitted equation that models the

6

0

4

0

2

0

5.0

2.5

0.0

1

0

0

0

5

0

00

AgeTime In Expe

Is there a relationship between Y and the X variables?

Comments

Simple vs X Variables

not in the model.

represents an X variable

A gray background

Multiple Regression for Simple

Summary Report

% of variation explained by the model