statistical research paper

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PHYSICAL SMILE AND ANXIOUS MOOD 13

Student’s Name

Institutional Affiliation

Physical Smile And Anxious Mood

ABSTRACT

People who experience mood disorders often go to hospitals for treatment and use a lot of medicine to improve the situation. Sometimes, their condition persists whereas at other times even when medicine is working it causes detrimental side effects. We conceptually engaged 67 participants in a study where small smile for 20, big smile for 22, and no smile was 25. Using the 25 questionnaire, one-way ANOVA process analysis, and between-subjects design, it was possible to test the hypothesis if participants are made to physically smile as widely as they can, they will feel less anxious than the small smile and no smile group. The findings in this study suggest that forced smile and laughter positively impacts on people with mood disorders.

INTRODUCTION

The King James Bible “A merry heart does good like a medicine, but a broken spirit dries the bones.” (Proverbs 17:22). Such a statement ignites the need to establish the reality of the religious assertion.

Statement of research problem: Depression, anxiety, bipolar disorder, irritability, affective liability and other mood disorders are serious problems that result in a lot of challenges in the life of a human being. According to DeRubeis, Lorenzo-Luaces & Strunk (2016) mood disorders are among the most unadmirable conditions that affect human beings. These conditions create discomfort in the life of the person affected hence resulting in both short-term and long-term effects. Unless they are controlled mood disorders could result in suicide, loneliness, self-injury, sleep disorders among other issues. People with anxiety and major depression problems undergo a lot of medication, they take a lot of pills some of which have side-effects.

Besides, after exposure to DBT book has driven me to like and have an interest in issues of facial expressions and mood. My psychology councilor told me to act like I am smiling so that it can make me feel happier and I been practicing this. So far, I find it does help me feel happier. If my facial expression is sad or mad, it will make me sad or mad. If smiling can decline depressed anxiety and mood, then it would be essential to research into to it so that a cheap, simple, and healthy prescription saves people with mood conditions as well as those who have the potential of developing such.

The research Question:

1. Do facial expressions change people’s mood?

2. Can the physical act of smiling as widely as you can decrease anxious mood more than a smaller smile and no smile?

Literature review

Borod (2006) opines that the brain of a human being is set to have a positive response to a smile or laughter hence generate ‘feel-good’ chemicals that can serve in people with depression. The wiring is sturdy hence the brain responds when one smiles to self in the mirror or stimulates laughing enthusiastically. Smiling and feeling triggers a reciprocal good feeling for the body mimics what the mind holds (Takanagi, 2007). A smile or laughter ignites the release of a neurotransmitter, dopamine which generates a happiness

Lin, Hu, & Gong (2015) conducted a study on eleven undergraduate student that had minor depression in which there that then generates compassion, joy, bonding, tolerance, unconditional love, and generosity, a therapy to bad moods, are three groups of samples within the Duchenne, Standard and no smile group. As such, the study illustrated that Duchenne smile declines the event-related potentials (ERP) amplitude. In the long run a smile helps an individual with depressed mood feel better.

Another study conducted by Neuhoff & Scharfer (2002) researches into the impact of forced laughter on mood in comparison to laughter with smiling and howling as mood-improving activities. Among the twenty-two adults of ages 21 to 43 who took part, it was noted that howling never greatly improved mood but laughing and smiling enhanced substantial impact on mood. Thus, if forced to laugh or smile, an individual can have a positive influence on depression.

Kleinke, Peterson, & Rutledge (1998) on the other hand, used 129 participants who looked at photos of individuals involved in negative or positive facial expressions. The samples fall into three groups that included a control group (maintaining neutral facial expressions), those looking at themselves in a mirror while engaging a positive facial expression and those who are not viewing themselves in a mirror while engaged positive facial expression. The communication of facial expression was video recorded after which the researcher found out that when partakers involved positive facial manifestation, they will have a positive mood.

Padberg, Juckel, et al (2001) uses a repetitive transcranial magnetic stimulation to experiment on prefrontal cortex modulation of emotions. In the test, the focus was on checking is self-rated mood and emotionally induced facial expressions using nine participants aged 24 to 38.Through computerized evaluation of self-rating on mood, it was realized that there are lateralized changed in the facial expressions following the stimulation while such changes of subjective mood ratings never exposed a hemispheric lateralization. Thus, an integration of the rTMS and facial expressions need to be studied on cortical modulation of human emotions.

In another research by Gehricke & Shapiro (2000) eleven female patients aged 21 to 37 with major depressed and eleven patients aged 20 to 38 with nondepressed were used in examining social contextual differences in activities of facial muscle and self-reported emotion. The participants had to imagine sad and happy situations without and with visualizing others. There was reduction of brow and cheek region in depresses as compared to nondepressed patients during sad and happy imagery while self-reported emotion exhibited no group variations. For both groups, happy imagery brought about smiling and self-reported happiness while sad imagery presented a lot frowns and self-reported sadness. There was an increase in smiles and self-reported happiness when one is happy in comparison to happy-solitary imagery in the two groups. There was no social context variation in frowning despite the fact that there was an increase in self-rated sadness in the sad-social versus sad-solitary imagery.

The rationale and purpose of the investigation: A study into feelings or emotions and how they enhance healthiness is essential as it aims at acquiring a better solution towards mood disorders.

METHOD

Participants: We had 110 participants participated in our Research, After we delete the ones who didn’t finish the study and failed attention check question, only 67 of them left. And they are unevenly distribute in each group, 25 in control group, 22 in big smile group, 20 in small smile group.

The participants were 67 with 23 male, 44 female. In terms of age, 60 participants were aged 18-30, 5 were aged 31-45, while 2 were aged 45 above. Consider the distribution below:

The study uses between-Subjects design since within-Subjects design situations have an effect dependent variable. When a participant physically makes a big smile on the face, they will experience improved mood, and it affects the other small smile responses.

The study will have control group who will not smile at all. The process will ensure there is internal validity since in case there is a change in anxious mood amongst the big/small smiling condition, confirmation that this was due to the smile and nothing else will be possible.

The study will rely on Clinical Anxiety Scale, it has 25 questions to measure currently anxiety feeling. each questions have 5 levels with having “None or a little of the time,” “Some of the time,” “A good part of the time,” and “Most or ALL of the time.” See Appendix for the questionnaire.

Order of Procedure: In the study process, First we informed participants what they need to know about this research, and instruct them how to do it. They been informed there will be utilization of a survey in form of a questionnaire before and after the 10-second task. Each participant will fill it out as accurately and candid as possible. The responses to the questionnaire will have to be as reflective of the current state. When the next is clicked, it will offer direction to the first questionnaire.  

Then Qualtrics Random assign to three groups, between our pre-test and post test, there is a 10 second task Random assign to three groups. Pre-test and Post-test were a Clinical Anxiety Scale with 25 questions. In the end, we have geographic questions and debrief.

RESULTS

Descriptive Statistics

The Inferential Statistics show that there is a difference of situations based on the smile size. In the statistical analysis, the median is the control group is 6.00, whereas that of big smile is 8.00 and that is small smile is 9.00. On the other hand the mean of the three are 4.68, 7.73 and 15.80 respectively. The variance are 30.477, 43.827, and 428.589 whereas the standard deviations are 5.521, 43.827 and 20.702 in that order. This means that hierarchically, smiling more and greatly enhances less depression. The 95% Confidence Interval for Mean is…..

Control Group:

Mean: 4.68;

Medium: 6.00;

SD: 5.521;

SE: 1.104

Big smile Group:

Mean: 7.73;

Medium: 8.00;

SD: 6.620;

SE: 1.411

95% Confidence Interval for Mean (Lower Bound: 4.79 Upper Bound: 10.66)

small smile Group:

Mean: 15.80;

Medium: 9.00;

SD: 20.702;

SE: 4.629

95% Confidence Interval for Mean (Lower Bound: 6.11 Upper Bound: 25.49)

Inferential statistics

P-value from the one-way ANOVA came out to be 0.013, which is less than p=0.05. Therefore, we reject the null hypothesis, which states that there is no difference between the three levels

Post Hoc Test: p-value for the comparison between the small smile and control group came out to be 0.011, which is less than p=0.05. Hence, we reject the null hypothesis, which stated that there is no statistically significant difference between the small smile and control group

Post Hoc Test: p-values for the comparison between the big smile group versus the control group came out to be 0.678, which is greater than p=0.05, so we failed to reject the null hypothesis.

Post Hoc Test: p-values for the comparison between the big smile group versus the small smile group came out to be 0.095, which is greater than p=0.05, so we failed to reject the null hypothesis.

A correctly formatted table or figure

My raw SPSS output

DISCUSSION

Re-statement of your hypothesis

Null hypothesis: The level of smile does not affect the level of anxious mood. Alternative hypothesis: The level of smile does affect the level of anxious mood ----If participants are made to physically smile as widely as they can, they will feel less anxious than the small smile and no smile group. (greater difference between the pre-test and post-test)

· IV: Size of smile

· Its levels have three levels: no smile, big smile, and small smile.

· DV: The difference scores between the pre-test and post-test results for the Clinical Anxiety Scale. (Scale of measurement: interval scale)

A brief interpretation of your results and implications of those findings 


Result: P-value from the one-way ANOVA came out to be 0.013, which is less than p=0.05. Therefore, we reject the null hypothesis, which states that there is no difference between the three levels. In Post Hoc Test, p-value for the comparison between the small smile and control group came out to be 0.011, which is less than p=0.05. Hence, we reject the null hypothesis, which stated that there is no statistically significant difference between the small smile and control group.

Implication: Bigger smiles do not induce greater changes in anxious mood; Even though Small Smile group induce greater changes in anxious mood, But in small smile group there have 2 participants score is extremely high, It might because they are try to rush finish survey, or because at that moment they feel our task really help them decrease anxiety. But it might not or hard to happen again at second time.

A brief critique of your methodology identifying the limitations of your research

Confounds . Different anxious level；different location (in the office or home)；different time of the day doing survey; Geographic factors: Uneven numbers of gender and age group; and other unobservable confounds like socioeconomic status, intelligence, personality traits and so on

Participant Bias --- Participants might find out what this experiment is about, it is a Demand Characteristics.

Validity of measurement Deficiency – we did not correctly measure participant’s current anxious mood. Some of them confuse the questions of the survey.

Third, External Validity threat

Anxiety and mood disorders are serious illnesses that have severe effects on the patients. Seeking a solution to this problem rather than usage of medicine, which cause side effects, is better. The study was conducted with the aim of determining the relationship between smiling/laughing with depression. Based on the hypothesis: If participants are made to physically smile as widely as they can, they will feel less anxious than the small smile and no smile, the study has.

Future directions for research

The study was limited by time and utilization of one study approach. It is suitable that in future anyone conducting a research on smile and depression utilizes both observation and surveys so as to acquire reliable findings. Besides, other related facial expressions should be tested to prove whether it is a smile or other variables that enhance low depression in individuals.

First, we need to avoid Internal Validity Threat, control Confounding variables, (for example, all participants doing at the lab at the same time)

Second, avoid the Construct Validity Threat, build a better Operationalize Definition, (for example, instead of 10 second smile at only one time, we could ask them smile in certain time on several consecutive days.) and avoid Participant Bias, have a reliable and cleared Measurement. (Like use better anxiety scale)

Third, avoid Sampling Bias, such as more participants, and try to use probability sampling to make the samples represent the target population.

References

Borod, M. (2006). Towards a better laughter life: a model for introducing humor in the palliative care setting. J Cancer Educ, 21(1), 30-34.

DeRubeis, R. L., Lorenzo-Luaces, L. & Strunk, D.R. (2016). Mood disorders. (G. R. J. C. Norcross, Ed.) 31-59. doi: 10.1037/14862-002

Lin, W., Hu, J., & Gong, Y. (2015). Is it helpful for individuals with minor depression to keep smiling? An event-related potentials analysis. Social Behavior and Personality: An International Journal, 43(3), 383-396. doi:http://dx.doi.org.libproxy1.usc.edu/10.2224/sbp.2015.43.3.383

Walter M, Hänni B, Haug M, et al Humour therapy in patients with late-life depression or Alzheimer’s disease: a pilot study. Int J Geriatr Psychiatry. 2007; 22 (1):77-83.

Takayanagi K. Laughter education and the psycho-physical effects: introduction of smile-sun method. Jpn Hosp. 2007 Dec; (26):31-35.

Appendix