Left prefrontal cortex activity, hobbies, and psychological well-being

Left Prefrontal Cortex: Hobbies and Serenity -- is There a Connection?

LEFT PREFRONTAL CORTEX

Over the past 40 years, brain imagery of the left prefrontal cortex region (LPFC) indicates a positive association between attention and serenity. Inferences, therefore, seem to be that the greater the amount of attention dedicated to a hobby, the greater the amount of serenity the individual possesses. Although Csikszentmihalyi has conducted research on 'flow' and shown that that significant correlation exists between focus and serenity, little if any research seems to exist on the connection between hobbies and serenity. This essay seeks to fill that lacuna.

The Left Prefrontal Cortex

Scientists have been intrigued by the LPFC. Implicated in significant components of psychological well being, researchers have recommended engaging in behaviors that are characteristic of that organ (Mental Health Weekly, 2004). The seat of human power and reasoning, the frontal cortex is located at the rostral end of the frontal lobe that receives input from the dorsomedial nucleus of the thalamus. Compared with the functions of sensory and motor cortical areas, the functions of the prefrontal cortex is relatively poorly understood, but because it is so well developed in humans, it is assumed that the PFC is responsible for characteristics such as self-awareness and capacity for complex planning and problem solving.

Of the frontal cortex, the left seems to be particularly involved in analytical planning, whilst the right reserves itself for focusing on emotions. In fact, Luria (1982) conceptualized the frontal lobes as master executive of afferent and efferent signals and was highly interested in the contributions of the LPFC to planning and the intentionality of behavior.

An abundance of research implicates the LPFC as necessary for obtaining peace of mind:

[The] left prefrontal activation appears to be associated with a constellation of positive attributes, including reduced levels of the stress hormone cortisol, and reductions in other biological and immune parameters that are associated with negative affect. (Davidson, 2000, p.12).

Two theories - the approach withdrawal opinion (Davidson, 1994; 1998) and the valence-arousal model (Heller & Nitshcke, 1998; Shankman & Klein, 2003) -- account for the findings. Research, on the whole, seems to support the approach-withdrawal perspective. Davidson's model posits two separate systems of motivation and emotion -- one for approach behaviors and one for withdrawal (Davidson, 1994) and that both systems are represented by separate neural circuits that involve different regions of the frontal cortex. Greater activity in the LPFC is hypothesized to indicate approach behavior, whereas greater activity in the right PFC is associated with activation of withdrawal (Davidson, 1994; 1998). Damage in either of these systems produces emotional syndromes such as depression and anxiety disorders (Davidson 1998). The other model, the valence-arousal theory, is similar in that it also predicts associations between hemispheric asymmetries and emotional disorders, but differs in that it proposes that asymmetries for posterior regions of brain (Shankman & Klein, 2003), specifically in the right posterior regions of the brain are associated with anxiety (Heller, 1993).

In a similar sense, repeated neuroimagery show happiness to be a prime locus of the LPFC. As Davidson observes:

The prefrontal cortex appears to play a critical role in the uniquely human capacity to modulate emotions… Damage to the left side of the brain, which leaves the right side in control, [is] more often associated with a negative mood, including symptoms of uncontrollable crying and other indicators frequently associated with depression. Damage to the right side of the brain, when the left [is] spared, [is] reported to be associated with a very different, more positive constellation of mood reactions. (Davidson, 2000; p.13)

Research in infants seems to confirm that some individuals seem to be genetically primed for happiness. Higher LPFC in infants less than a year old predicted that they would not cry when subjected to minor stress, whereas those with lower levels of activity in their LPFC reacted hysterically (Davison, 1984). In fact, animal studies have shown that the happiness producing neurotransmitter, dopamine, mediates the transfer of signals associated with positive emotions between the left prefrontal area and the limbic emotional circuits of the brain. Empirical research has shown that applying repetitive transcranial magnetic stimulation (rTMS) to the left PFC has been found to relieve depression in individuals who withstand medication or other treatment. Apparently, the LPFC is less active in people with depression, and rTMS elevates it (Helmuth, 2001). Similarly, Robinson and Downhill (1995) have noted a correlation between depression and damaged left frontal lobe regions, and have observed that the closer the lesion is to the frontal axis, the greater the depressive symptoms. Depression has been, repeatedly, coupled with decreased activity in LPFC (Allen, Iacono, Depue, & Arbisi, 1993; Debener et al., 2000; Henriques & Davidson, 1991), whilst anxiety is consistently found with increased activity in right prefrontal regions of brain (Nitschke, Heller, Palmieri, & Miller, 1999). Other abnormalities such as panic disorder (Wiedermann et al., 1999), and social phobia (Davidson, Marshal, Tomarken, & Henriques, 2000) have also been found to correlate with either decreased activation in LPFC or increased activation in the right PFC. It must be noted, however, that some studies have failed to find an association between EEG asymmetry and depression (Reid, Duke, & Allen, 1998), and that there are paradoxical unexplained findings that the LPFC also processes the negative emotion of anger (e.g., Van Honk & Schutter, 2006; Harmon-Jones, 2004; Harmon-Jones, Lueck, Fearn, & Harmon -- Jones, 2006; d'Alfonos, van Honk, Hermans, Postma, & De Haan, 2000). My assumption for this is that anger indicates a certain energy and alertness symptomatic of the left cortex. The right cortex, on the other hand, is too passive to respond to or emit this emotion.

The left prefrontal cortex also shows indication to resilience (Curtis & Ciccheti, 2007; Ethan, 2007), but then no wonder since resilience and serenity are closely knitted. Being able to shut off a negative emotion appears to be a very adaptive feature and something that individuals who show this pattern of left-sided brain activity do exceedingly well (Davidson, 2000).

Kross, Egner, Ochsner, Hirsch, and Downey (2007) scanned the brains of highly sensitive individuals who reacted intensely to rejection, and compared results with those who were more 'thick-skinned'. Low rejection-sensitive individuals displayed significantly more activity in the left inferior and right dorsal PFC. This, the authors suggested, strongly indicate that the left prefrontal structures are indicative of resilience to rejection. Interestingly enough, depersonalization (a certain pathological disorder that includes loss of emotional feeling) displays enhanced activation in the LPFC.

Experiment 1

Using stratified random sampling, subjects had been randomly selected from a pool of 1,303 JJC Business students. All subjects had been applied the Brief Serenity Scale that tested general serenity levels as a personality trait. They had also been surveyed regarding their favorite hobby. Data was then entered in an SPSS data set and regression performed to test for correlation. Although there was no need to divide this group into subgroups since the focus was on whether a correlation could be traced between one variable and another not between one comparison group and the other, SRS had been performed in selecting the initial people. This was so in order to gain as diversified a sample as possible (from 'high', "medium', and 'low' socio-economic sectors (since some students come from stressed backgrounds and this may reflect attention dedicated to their hobbies). I chose stratified random sampling since it addresses the problems of the simple random sampling approach and best models the original population.

Categorization of hobbies itself was divided into three groups and distinguished by their coding;

1-5 referred to hobbies that demanded low attention (such as singing, cooking, and eating); 5-10 referred to the mediate category where more moderate attention was accorded hobbies. Hobbies in this category included bicycling, swimming, and shopping. 10+ referred to hobbies that demanded high attention, the category including tennis, golf, and aviation.

Categorization and coding of hobbies was designed by 4 objective double-blind researchers who were non-cognizant of the characteristics of the research. Disputes were arbitrated by one other objective individual. Hobbies that could not be agreed upon were dropped and participants were asked to select another hobby.

Stratified random sampling

I used a sampling interval where I selected potential respondents from a sampling frame of 1303 students, but unlike simple random sampling, the JCC Business students had already been divided into strata (i.e. separate subpopulations), since I wanted these individuals to represent all sectors of the socio-economic population. I relied upon their stated demographics when doing so (i.e. students had been asked to record their socioeconomic strata when filling out their brief serenity survey according to class / salary / profession that they approximated their parents to belong to. (Alternatives of salary ranges had been supplied and students had been asked to check the box that they felt best simulated their situation). The strata had been divided into 'high', 'medium', and 'low' income backgrounds. Each stratum had 434 names. I then proceeded using simple random sampling from each stratum. The sampling interval here was 434/100 = 4.34 (round number, 4). Using random number tables, I selected a number between 1 and 4 to give the seed number to start with. It was 2. I then selected every 2nd person on the list, then the 6th (2 +4), the 8th (6+2) and so forth. The three strata ended up not contributing an equal number to both gender, but the number was in proportion to the expected sizes of strata in the population. There are proportionately fewer high earning people than low earners and the eventual sample reflected these proportions.

Explanation for sample size

It is usually the case that the bigger the sample the better the estimate since standard error decreases with increase of sample size. Three considerations are involved in sample size: level of precision (or margin of error), confidence level, and degree of variability. The margin of error is the amount of error that I can tolerate: a lower margin of error requires a larger sample size. For level of precision, or sampling error (otherwise known as margin of error), I chose 6.16.

The confidence level refers to the amount of uncertainty that I can tolerate. Higher confidence levels require a larger sample size. For confidence level I chose 80%,

The degree of variability or response distribution refers to the variance of the population. Since the population is a normal bell-shaped distribution, I chose 50%.

The calculation was performed on the sample size calculator provided by Raosoft (http://www.raosoft.com/samplesize.html)

A good-sized sample is needed for multiple regressions, although for the second study where it is descriptives that are being analyzed (i.e. mean, frequencies, standard deviation and so forth) any sort of sample size (of course with conditions) would suffice. Sudamn (1976) suggests that a minimum of 100 participants would be adequate for each major group or subgroup in the sample (and that a sample of 20 to 50 elements is necessary). Similarly, Kish (1965) says that 30 to 200 elements are sufficient when the attribute is repent 20 to 80% of the time (i.e. The distribution approaches normality).

Method

Subjects

The participants were 100 JCC 3rd year graduate business students randomly selected from a population of 1303 individuals. The sample was mixed with 53% of the population being male (n=53) and 47% of the population being females (n=47%), all of them coming from a diversified ethnic population and representing diverse socio-economic backgrounds.

Inclusion criteria included the fact that individuals spend at least 3 hours per week on their hobby and at least 1 hour of continuous ongoing absorption in that hobby so that attentional span could be measured and that habitual level of comfort with hobby was assured. The individual had to be also acquainted with that hobby for at least 3 years previous to being tested. All of this ensured that the participants had sufficient experience and level of comfort with the hobby that ensured that their expertise was at a mature enough level. Exclusion criteria involved anyone who used the hobby as their job and/or spent more than 5 hours per week on that specific hobby and more than 3 hours of continuous absorption in the activity. This ensured that expertise with hobby stayed at a mean level and that expertise did not resemble specialization. In this way, the groups could be equally matched and attention span more reliably gauged. Engagement with hobby in other words, resembled more of an academic pursuit than of professional substance and attenioanl span for categories of hobbies was expected to be equally matched between groups. Other inclusion criteria included not practicing mindfulness mediation nor any other relaxation system; being in stable health (e.g. no hospitalization or major illnesses in the previous 3 months); no major crises within the previous 3 months; and being perfect English speakers. The previous three conditions were criteria for the brief Serenity Survey, whilst the latter served as determinant for understanding instructions.

Procedure

Of the 1303 JCC business students, a sampling size of 100 was randomly selected by stratified random sampling as described above. Following a telephone screening interview where it was ascertained that randomly selected participants represented required inclusion and exclusion criteria, the study coordinator scheduled an appointment to explain he study and to conduct the informed consent process. In cases where one or more of the participants failed to represent criteria, replacements were randomly selected until the required sample size had been reached.

Individuals choosing to participate in the study were given a battery of self-report instruments to complete at home and return by mail within that same month. Items included work (to se e whether that correlated with hobby) and hours of work, socio-economic demographics, year of graduate study, preferred hobby and details of that hobby as well as amount of time per week associated with that hobby, years acquainted with that hobby, and the respondent's best guess as to the average continuous amount of time he generally gave to that hobby at one go. Participant was also asked to state whether he engaged in other activities when involving him with hobby (e.g. listening to music at that time, talking to other and so forth). He or she was also asked to rate the approximate amount of attention accorded to hobby and several alternatives were given here for individual to respond to. The Brief Serenity Tool was applied only after receipt of these baseline measures.

Participants scores received from the Brief Serenity Tool as well as their responses to the survey were entered into an SPSS Data set and a Pearson and Spearman correlation then performed. The question here was: How does attention with hobby influence attention span. It was hypothesized that as attention span increases, score of serenity (or level of serenity) increases too.

I used both correlation and regression data to assess results since each test tells us something slightly different about the data.

Description of Instrument

Roberts and Fitzgerald (1991) completed a concept analysis of serenity revealing ten critical attributes of serenity including: inner haven of peace and security, detachment from excessive desires and emotions; and acceptance of situations that cannot be changed. As an outcome of this analysis, they defined serenity as a spiritual experience of inner peace that is independent of external events. A subsequent conceptual model developed by Roberts and Whall (1996) postulates that serenity is a learned, positive emotion that decreases perceived stress and improves health.

The 22 item brief version of the Serenity Scale created by Kreitzer et al. (2009) includes all of the items from the largest of Roberts and Aspy (1993) original factors, including the Inner Haven (9 items). The brief version also includes all items from the original Trust factor (4 items) and most of the items from the Acceptance factor (4 items). The remaining items represent the original factors of Perspective (2 items), Benevolence (2 items), and Present-Centeredness (1 item).

According to Kreitzer et al. (2009), their brief Serenity Scale has small to moderate positive correlations (r = .3 to.5, representing 10% -- 25% shared variation) with concepts of positive affect and mindful awareness, and small to moderate negative correlations with measures of negative mood and distress.

Data of scores as well as response to survey on hobbies was entered on a data set (see Appendix).

Key to Data on Statistical SPSS set.

CAT refers to Hobby

Serenity to the scores measured by the Brief Serenity scale.

Level of profession and number of hours in that profession (i.e. where a full-time job or part-time were also assessed so as to ascertain that the hobby was on an academic level and did not represent a profession.

Gender -- male and female was translated into numeric rather than string form as male = 1 and Female = 2.

Results

The first thing to notice (see figure 1) is that most hobbies fell within the moderate range indicating that most people sampled preferred hobbies that demanded some degree of attention and that fell within the category of bicycling, roller-blading, swimming, and so forth. 2.00 occurs 37 times. Next in order is 1.00 which is the category of least attention such as casual reading, doll-collecting or any other similar collection form as well as jogging, hiking and so forth where distraction is usually although not always, conjoined to hobby. This least demanding category was preferred by 34% of the individuals. 23% of the participants -- the smallest sector -- preferred hobbies that demanded maximal attention and generally, although not necessarily, belonged to the category defined as 'high-thrill'. This would include mountain climbing and car-racing on the one hand and music, painting (depending on intensity), yoga and similar exercises on the other.

Figure 1: Frequency of Hobbies

CAT

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1.00

34

34.0

34.0

34.0

1.50

1

1.0

1.0

35.0

2.00

37

37.0

37.0

72.0

2.50

3

3.0

3.0

75.0

3.00

23

23.0

23.0

98.0

3.50

2

2.0

2.0

Total

When it came to evaluating the hypothesis that increase of attention span on hobby (labeled CAT) correlates with score of serenity (namely, that there is a direct connection, or correlation between attention focus and hobby), interesting results were discovered.

Over here, I used both correlation and regression to assess results since each test tells us something slightly different about the data. The Pearson test is the most widely bivariate test used and is employed when variables are normally or close to normally distributed (as they are in our case). Even though the Spearman is often used for variables that are not normally distributed it works well as a good corroborative test, and this is the reason that I used it.

Table 1 represents the Pearson test, whilst Table 2 represents the Spearman test. Notice that both have a correlation score of 1 indicating a perfect (significant) correlation. In fact, the Pearson coefficient is .838, whilst the Spearman coefficient is a close .801. They are positive therefore the outcome informs us that there is a strong positive correlation between attentional focus and serenity. In other words the greater the amount of attention accorded a hobby, the stronger the individual's level of serenity. Thus our hypothesis is supported. There is a strong relationship in the predicted direction (it is positive) between level of attention and score of serenity (that indicates individual's level of serenity) in the total population from which the sample was drawn.

Table 1 Relationship between Hobbies (CAT) and Score (Pearson)

Correlations

CAT

SCORE

CAT

Pearson Correlation

1

-.021

Sig. (2-tailed)

.838

N

SCORE

Pearson Correlation

-.021

1

Sig. (2-tailed)

.838

N

Score =direct and strong .838 (1)

2 tailed sig. P

Table 2 Relationship between Hobbies (CAT) and Score (Spearman)

Correlations

CAT

SCORE

Spearman's rho

CAT

Correlation Coefficient

1.000

-.026

Sig. (2-tailed)

.801

N

SCORE

Correlation Coefficient

-.026

1.000

Sig. (2-tailed)

.801

N

Score =direct and strong .801 (1)

2 tailed sig. P

I also applied regression and correlation tools to the data analyzing the linear relationships between the variables and finding the line that "best fits" the data (i.e. that keeps errors from the line to a minimum). See the Scatter-dot graph of Figure 2 representing the linear relationship between Hobbies (CAT) and Score. Hobbies serves as the independent variable and score as the dependent, meaning that level of serenity depends on amount of attention accorded to hobby. The correlation coefficient tells us the amount of variation in one variable (the dependent variable) that is explained by the other viable (the independent variable).

The R-square statistic to the right of the graph tells percentage of variation in the dependent variable (i.e. serenity) that is explained by intensity of attention on hobby. Here, the amount of variation is 4.312 (or 43%). Thus, intensity of attention predicts 43% of the variation in levels of serenity found in our sample.

Table 2, the Model (or summary of the whole) tells us that 0.042 of the variability in the data is accounted for by differences between the treatments (see .21 R. In Table 2; summary; and 0.042 in the ANOVA. Table 3)

Finally, the final Table (Table 4) presents notation of the standard error and the slope of the line (b intercept) that describes the relationship between the independent and dependent variable (i.e. between focus of attention and serenity). Table 4, summarizing the whole shows that the standard error between the actual population and the sample is 3.4 (actually, not too large) and that there is a significant correlation between attentional focus and serenity (.838). In short, therefore, we can conclude that correlation for the data indicates that intensity of attention accorded to hobby and level of serenity were significantly related, r= +.838, n=100, p

On the whole, therefore, we can predict with moderate certainty that the sample images the general population and that selecting a hobby of category 3, or one that necessitates a great degree of attention promotes serenity. In other words, the more the attention accorded the hobby, the greater the level of attendant serenity.

Figure 2. Scatter-dot of linear relationship between Hobbies (CAT) and Score

Table 2. Regression analysis. Summary of the whole

Model Summary

Model

R

R Square

Adjusted R. Square

Std. Error of the Estimate

1

.021a

.000

-.010

26.68006

a. Predictors: (Constant), CAT

R =.021

Table 3: ANOVA

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

30.096

1

30.096

.042

.838a

Residual

69758.904

98

Total

69789.000

99

a. Predictors: (Constant), CAT

b. Dependent Variable: SCORE

ANOVA = .042.

Table 4: Description of Standard Error and b Intercept

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

47.245

7.062

6.690

.000

CAT

-.697

3.388

-.021

-.206

.838

a. Dependent Variable: SCORE

Discussion

Over the past 40 years, brain imagery of the left prefrontal cortex region indicates a positive association between attention and serenity. Inferences, therefore, seem to be that the greater the amount of attention dedicated to a hobby, the greater the amount of serenity the individual possesses. Although Csikszentmihalyi has conducted research on 'flow' and shown that significant correlation exists between focus and serenity, little if any research seems to exist on the connection between hobbies and serenity. This essay sought to fill that lacuna by sampling a population sample of 100 students extracted from a population of 1303 JJC Business students. Males were 53% of the population and females were 47% of the population. Stratified random sampling was used, the Brief Serenity Scale was employed and a survey conducted on demographics and on hobbies. Pearson and Spearman regression was then conducted to assess correlation and variance.

Results indicated that a remarkably significant correlation exists between level of intensity of focus on hobby and level of serenity and that there is a positive connection between the two. Most interesting of all was that the more demanding the hobby (i.e., the greater the amount of attention it necessitated) the higher the score of serenity.

Limitations included the fact that some hobbies belong to a gray area where some individuals would accord little attention to that hobby, whilst other individuals would mindfully labor for results. Photography and drawing / painting, music and other crafts would be a fine example here. For maximum results, therefore, a survey would be in order and more complex research that transcends the perimeters of this study.

Also of interest would be to conduct a more complex study, preferably triangulated, where other variables conducive to serenity could be taken into account, and attention controlled for. It may be found that serenity is an outcome of one or more factors other than (or in addition to) attenional focus, and that connection between the amount of attention inserted in a hobby and consequent serenity level is minimal or non-existent. Correlation does not indicate causality.

Other populations, too, have to be measured as well as longitudinal study conducted to assess the permanence of the serenity levels; whether absorption in the hobby causes transient serenity or whether its effects (as this study hypothesizes) are more potent and enduring.

Experiment 2

The previous study indicated that most individuals seem to prefer involvement in hobbies that lie along the mid range attentional span (such as swimming, cooking, and ice-skating). This in itself is interesting. Possibly, more interesting still would be investigation of the possibility that each gender, on the whole, inclines to specific hobbies.

Although the LPFC is itself differentiated, it is well-known that neural differences exist between the two sexes and that activities and states of mood show differentiation between the genders. It is, for instance, well-known that depression has greater neural effects on females than it does on males (e.g. Davidson, 1994) and, similarly, that there are gender differences in prefrontal cortex activation when word images are registered (Buckner, Raichle, & Petersen, 1995) . It would be interesting, therefore, to find out whether distinct differences exist between males and females both in the type of hobbies that they select -- namely whether men are more prone to selecting highrisk and/or attentional tasks and whether women prefer more low-risk less-focused activities and whether differences are manifested in their accompanying serenity level. Ramifications of such research would be absorbing. It may provide explanantion for gender differences in depression as well as providing clinical intervention for treating depression. It may be that all a depressed individual needs is an absorbing hobby. As far as I know, no previous research has been conducted on possible gender differnces in hobbies. Certainly, there are indivdiuals from both genders who participate in so-called female hobbies (such as males who sew and females who hunt), but it would be interesting to discover whether some hobbies are endorsed more by one gender than they are by the other.

Method

Stratified random research was conducted on a sample of 100 students who had been selected by simple random sampling from 1,303 JJC Business students. The stratified random sampling divided these students into two groups, 53% male (n=53), 47% females (n=47). The data that had been previously collected regarding their hobbies was used and statistical assessment carried out to assess the data. I order to ensure validity of data, missing data and outliers were first carefully recorded, recoded, and included. The objective here was to discover whether gender differentiation could be indicated between gender selection of the various hobbies and, if so, whether inclination leaned towards any specific category. In other words, whether males showed inclination to engage in more thrilling hobbies than did females; whether females or males showed greater tendency to selecting hobbies that demanded more attention than did the obverse sex, and, in short, whether comparisons or contrast could be traced between gender distribution of hobbies.

Key to Data on Statistical SPSS set.

Categorization of hobbies itself was divided into three groups and distinguished by their coding;

1-5 referred to hobbies that demanded low attention (such s singing, cooking, and eating); 5-10 referred to the mediate category was more moderate attention was accorded hobbies. Hobbies in this category included bicycling, swimming, and shopping. 10+ referred to hobbies that demanded high attention, the category including tennis, golf, and aviation.

Categorization and coding of hobbies was designed by 4 objective double-blind researchers who were non-cognizant of the characteristics of the research. Disputes were arbitrated by one other objective individual. Hobbies that could not be agreed upon were dropped and participant was asked to select another hobby.

Our question devolves on the thesis whether or not gender differences will be discovered between the types of hobbies chosen. Whether females, fore instance, will incline towards less attenional-demanding hobbies and men prefer more attentionally-demanding hobbies, or whether the reverse will be the case. Or whether no differentiation will be discovered between gender selection of various hobbies.

The hypothesis states that there will be no or little gender differentiation indicated between categories of hobbies, whereas the null hypothesis states that gender differentiation will be indicated in one or other direction in difference of hobbies chosen.

Results

Exploring some of the univariate data is helpful for getting a more rounded picture of the study.

As Table 1 shows the ages of the participants (n=82) ranged from 18 years to 38 years with a variance of 3 years in between. That is not much. The mean age is 20.

Table 1: The ages of participants

Descriptive Statistics

N

Minimum

Maximum

Mean

Std. Deviation

Age

82

18.00

38.00

20.4146

3.27712

Valid N (listwise)

82

Investigating their pattern of employment, we see the following descriptives from Table 2:

Description of employment (see Table 2)

Employment

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

2

2.4

2.4

2.4

F

16

19.5

19.5

22.0

O

3

3.7

3.7

25.6

P

46

56.1

56.1

81.7

U

15

18.3

18.3

Total

82

16 participants were full-time employed, 46 were employed part-time, 15 students were unemployed (a huge number!) with three showing 'other'. Most, consequently, had part-time employment, indicating that they likely (although not necessarily) had more time for hobbies than those who had to juggle both full-time employment and study.

As per the pie chart, we see that the overwhelming amount of the population (53.7%) preferred category 1 (i.e. low demanding hobbies) whilst, 24.4% preferred moderate hobbies, with only 3.7% of the sample who evidenced selection for the most absorbing category of hobby.

Pie chart indicating percentage of sample correlation with hobby interest.

Key: blue= low-attention category

Green= moderate attention category

Brown= high-attention category.

The frequency table (Table 4) supports this fact with 53.7% (n=44) preferring low-range hobby, 24.4% (n=20) of the sampled population preferring moderate attention hobbies, and only 3 individuals (i.e. 3.7% of the population) claiming their favorite hobby as lying in the most demanding attention realm.

Table 4: Proportion of participants to hobby categories

Hobby

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

1.00

44

53.7

65.7

65.7

2.00

20

24.4

29.9

95.5

3.00

3

3.7

4.5

Total

67

81.7

Missing

System

15

18.3

Total

82

Table 3 (Box plot) starts with information on hobbies showing differentiation of the categories according to age. Significant results only showed in age groups 18, 19, 21 and 23.

Age group 18, 19, and 23 showed gradation between the first 2 categories, with age group 23 showing an equally distributed mode between lesser demanding and moderate demanding categories. Whilst age group 21 seemed to prefer more moderate attention to more demanding attention with the mean equally distributed. Age group 21 in other words seemed to be the most inclined towards more demanding hobbies. There are outliers too.

The greatest amount of variation seems to exist in the age categories 19 and 21

Our next step is to proceed to the bivaraite data where I tested the null and alternate hypothesis (regarding gender differentiation in hobbies) and ran a chi-square and t-test on the data.

As the bar chart shows, Category 1 (1.00-5.00) shows, more females than males are attracted to categories that indicate low to moderate hobbies. As intensity of attention progresses, male are more interested with solely men tapering off for the most demanding hobbies and a dramatic peak at 7.00, which is the upper ranges of the more moderate zone.

Bar chart indicating gender distribution according to hobby category.

Key: blue=male

Green=female

The graph above indicates a kurtosis or a positive skew with the tail of the distribution slanting off to the right. The distribution is slightly skewed.

The cross-tabs using the chi-square statistic tests for hypothesis about the shape or portion of a population distribution and shows likelihood ratio with significance 0.88 indicating existence of null hypothesis (i.e. that there are 88 chances out of 100 of occurrence occurring i.e. Of finding differentiation between gender selection in hobby). In other words, our hypothesis that gender differentiation is non-existent is incorrect.

Table 5: Chi square on gender differentiation

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

19.473a

15

.193

Likelihood Ratio

22.816

15

.088

N of Valid Cases

72

a. 28 cells (87.5%) have expected count less than 5. The minimum expected count is .44.

Chi-square shows non-likelihood of alternate hypothesis occurring. Stated formally, the chi-square value is not in the critical region, therefore we must accept the null hypothesis, and state that there seems to be gender differentiation with hobby.

Finally, the chi-square was reinforced by an independent samples t-test. that tested for significance in mean difference between the gender (see Table 6). As noted significant difference does exist (p >0.5) with a small equal variance (3.14) and a standard error that consists of .134.

Table 6: T-test showing significance in mean difference between the genders

Independent Samples Test

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

lowattention

Equal variances assumed

27.531

.000

3.144

65

.003

.42125

.13398

.15366

.68883

Equal variances not assumed

3.433

61.691

.001

.42125

.12270

.17595

.66654

General Discussion

Study 1 indicated that most individuals seem to prefer involvement in hobbies that lie along the mid range attentional span (such as swimming, cooking, and ice-skating). This in itself is interesting. Possibly, more interesting still would be investigation of the possibility that each gender, on the whole, inclines to specific hobbies.

Investigation of the data carried out on the 100 students indicated that the hypothesis that no gender differentiation occurs in regards to hobbies was proved incorrect and that there is strong male inclination towards hobbies that necessities strong attention levels. In fact, the more intense the attention demanded the more males, as opposed to females, seem to be drawn towards such hobbies. Females, on the other hand, gathered towards the 1st and second categories of least demanding and moderate demanding categories of hobbies, whilst men were featured less towards these lower ranges but pooled more towards the upper ranges of the moderate category and then increased.

General contributions of the study offer clinical and practical benefits on a localized and global scale. The indications that the more attenionally-demanding the hobby the more region of the left prefrontal cortex it absorbs hence the greater the serenity it activates are enthralling to a spectrum of health-care practitioners that include social workers, therapists, the medical field, and, in fact, anyone who works with depressed individuals whatever their level of depression. All therapists would have to do is point client to a hobby that soaks up a lot of his attention and client's LPFC region would respond to activate serenity levels.

On an individual basis, too, individual wishing to elevate their mood or seek to sustain their happiness level would search for a hobby that demands their attention. Hobbies are a popular mechanism for well being and a much-solicited intervention. This study shows that it is not merely the general fact that a hobby is important, but also the type of hobby that is selected. The more absorbing the hobby, the greater the advantage it affords the person in that serenity levels is boosted and the person feels enhanced contentment and happiness.

On a theoretical level, depression exists disproportionably higher in females than in males and various explanations are speculated for such imbalance. Connecting attention with serenity (with images and studies of the left prefrontal cortex indicating correlation between the two), it may happen to be that males unintentional selection of more demanding hobbies cause them to be less open to depression than females are. This is an interesting thought and worthy of further investigation.