License Exam: Community Practice

Community Practice

SWOT Analysis from Community Toolbox

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SWB / Happiness : Variables Which Are Correlated refined

Based on DeNeve and Cooper's (1998). The happy personality: A meta-analyses of 137 personality traits and subjective well-being. Psychological Bulletin, 124, 197-229 findings:

The highest correlates with subjective well-being are:

1. r=.30 Personality traits, namely trust, emotional stability (neuroticism?), desire for control, hardiness, locus of control-chance (believing events happen by chance), and tension (experiencing negative emotions).
2. r=.29 Affiliation (wanting to relate to others)
3. r=.29 Perceived Control
4. r=-.27 Neuroticism
5. r=.25 Internal Locus of Control (crediting or blaming self for events that happen)
6. r=.23 Social Desirability (responding in a manner one believes will lead to approval from others)
7. r=.20 Sociability (relating well with others)
8. r=.20 Extraversion

From Lykken & Tellegran. (1996). Happiness is a stochastic phenomenon. American Psychological Society, 7(3), 186-189

• Genetics also account for 80% of stable SWB
  

From Lucas, Richard E.; Fujita , Frank (2000). Factors influencing the relation between extraversion and pleasant affect. Journal of Personality and Social Psychology, Vol 79(6), Dec, 2000. pp. 1039-1056.

• Extraversion (r=.38 and up to .80 when other variables controlled for)
  

From DeNeve & Cooper (1998)

• Conscientiousness (r=.20)
  

SPSS Guide: Counting Responses with Frequency Tables

Using a sample of 25 children's responses about their favorite cereals.  The comma delimited data set is available below.

1a. What percentage are males? Females?

 

          61.54% are male, 34.62% are female.  It looks like, in this data, that one gender value was coded as "1" which is a mistake, and which adds a value to the 25 to equal 26.

1b. Which cereal was preferred by most children?

---

Data File in comma delimited format:

Cereal,Like,Gift,Gender
Ghostly Shadows,crazy,squirt gun,M
Ghostly Shadows,Like,squirt gun,M
Ghostly Shadows,not part,squirt gun,M
Canary Crunch,not part,ring,M
Turtle Treats,crazy,squirt gun,F
Turtle Treats,crazy,ring,F
Turtle Treats,Like,ring,F
Ghostly Shadows,crazy,ring,m
Canary Crunch,not part,squirt gun,F
Turtle Treats,crazy,squirt gun,F
Ghostly Shadows,Like,ring,F
Canary Crunch,not part,squirt gun,F
Turtle Treats,Like,ring,M
Ghostly Shadows,crazy,whistle,F
Canary Crunch,don't know,ring,M
Turtle Treats,crazy,whistle,F
Turtle Treats,Like,ring,F
Ghostly Shadows,Like,squirt gun,F
Turtle Treats,Like,ring,F
Turtle Treats,crazy,can't decide,M
Canary Crunch,Like,ring,F
Turtle Treats,crazy,squirt gun,M
Ghostly Shadows,Like,ring,F
Turtle Treats,Like,ring,F
Turtle Treats,crazy,ring,F

Variables Predicting Happiness

From CGH Systems Book:

•  Overall Ideas:  Positive Mental State: contentment, joy, overall satisfaction, positive mood, absence of negative mood, living according to true self (inner daemon).   Bliss, power, knowledge, optimism.  Feelings: self-esteem, hopefulness, capability, resilience/hardiness, decision-making ability, intrinsic motivation.   NOT: sex, age, having children, location, race, income (these can be controlled for). Except for when there is extreme financial hardship.
• Predictors of Happiness: self-esteem, social support, efficacy, relatedness (feeling socially included), loving relationship with a spouse, personality traits such as extroversion and openness, community involvement, optimism, ack of neuroticism, spiritual or religious, autonomy, competence having goals and values, ability to adapt (change has a short impact), making progress, using effort, pursuing goals, lack of rumination, adequate resources, (Myers, 1992; Costa & McCrae, 1980; Heady & Wearing, 1992; Diener, Suh, Lucas, & Smith, 1999). Hardiness & Resiliency (Siebert, 1996)
• Positive Mental Health: ego strength, intrnisic motivation, optimism (Antonovsky, 1993; Maddi, 1999a, 1996b).  Mental Health: feelings of vitality, subjective well-being, problem solving abilities, excercising control over environment and self, self-understanding and insight.  Jung variables: psychic ingeration, wholess, or sense of self.  Also, personality types. NF, SF, ST, NT types.  Maslow: Self-actualization.

Statistical Terminology: Why different statistics are used

Descriptive Statistics

Mean: a measure of the general tendency, calculated by adding all the scores and dividing by the number of scores. The advantage over other measures of tendency is that it uses all scores; disadvantage is that it's influenced by extreme scores. It should be used with interval or ratio levels of measurement.

Mode: A measure of central tendency which is the most frequently occurring score in a distribution.

Median: A measure of central tendency that is the middle score of a set , placed in size order. Advantage: not influenced by extremes; Disadvantage: it doesn't use the arithmetic values of the intervals between scores. It requires an ordinal level of measurement and should be used instead of the mean when the distribution of data on an interval scale is skewed.

Standard Deviation: The square root of the variance. It measures the dispersion of the scores around the mean in a normal distribution.

Variance: A measure of dispersion that is eq. to the sum of the squares divided by the degrees of freedom.

Range: Simplest measure of dispersion; measuring the difference b/n the highest and lowest scores. Sometimes the value 1 is added to the difference to reflect that the range is inclusive of the end points.

Inferential Statistics

t-test: finds the difference b/n means b/n two groups. In a one-sample t-test, observed mean is compared to expected mean of the population.

ANOVA (Analysis of Variance): tests for sig. diff. b/n the means. Same results as t-test.

Repeated Measures ANOVA: successive observations from the same source (dog, or person, such as in a longitudinal study). Another e.g. is when the same person is given a treatment of vodka then tested on memory, then comes back in 5 days and is given a placebo.

Correlation: measure of the relation between two or more variables. Correlation coefficients range from -1.00 (perfect negative correlation - negative correlation!) to +1.00 (perfect positive correlation). A correlation of 0.00 means A LACK OF CORRELATION.

Multiple Regression: analyzes the relationship between several independent (or predictor) variables and a dependent (or criterion) variable.

Factor Analysis: reduces the number of variables, and then detect the structure of relationships between varaibles -- that is, to classify the variables. E.g., if we are set to measure people's satisfaction, and ask in a survey how much they like their hobbies and also how much time they spend on their hobbies, the two varaiable will probably have a HIGH correlation, which mean we are probably being redundant with both variables - best to condense.

One can summarize the correlation between two variables in a scatterplot. A regression line can then be fitted that represents the "best" summary of the linear relationship between the variables. If we could define a variable that would approximate the regression line in such a plot, then that variable would capture most of the "essence" of the two items. Subjects' single scores on that new factor, represented by the regression line, could then be used in future data analyses to represent that essence of the two items. In a sense we have reduced the two variables to one factor.

Discriminant Analysis: used to discriminate between two or more naturally-occurring groups. E.g., a school researcher may try to find out which variables differ b/n groups of students who decide to go to college and those who don't, and figure out which variables (SAT score, GPA, etc) predict each.

Chi Square Test: A test that uses the chi-square statistic to test the fit between a theoretical frequency distribution and a frequency distribution of observed data for which each observation may fall into one of several classes.

f(x) = {1/[2^/2 * (/2)]} * [x^(/2)-1 * e^-x/2]

= 1, 2, ..., 0 <>

where
is the degrees of freedom
e is the base of the natural logarithm, sometimes called Euler's e (2.71...)
(gamma) is the Gamma function

More Literature Review

Yuan, Anastasia S. Vogt, RACIAL COMPOSITION OF NEIGHBORHOOD AND EMOTIONAL WELL-BEING. Sociological Spectrum; Jan/Feb2008, Vol. 28 Issue 1, p105-129.

Living in a higher percentage same-race neighborhood improves the emotional well-being of Blacks and Hispanics; although, it is related to different aspects of emotional well-being. Social support explains part of this relationship for Blacks.

Singh, M. Predicting General Well-Being From Emotional Intelligence and Three Broad Personality Traits. Journal of Applied Social Psychology; Mar2008, Vol. 38 Issue 3, p635-646.

EI was related but distinct from the 3 personality variables. Trait-EI demonstrated the strongest correlation with job satisfaction, but predicted general well-being no better than Neuroticism. In regression analyses, trait-EI predicted l variance in the well-being criteria, beyond the 3 personality traits. It was concluded that trait-EI may be useful in examining dispositional influences on psychological well-being.

Ryan, R. ON HAPPINESS AND HUMAN POTENTIALS: A Review of Research on Hedonic and Eudaimonic Well-Being. Annual Review of Psychology; 2001, Vol. 52 Issue 1, p141, 26pgs.

Lit. review of well-being. Social class and wealth as predictors of well-being; Physical health and its relations to well-being; Research on differences in well-being across time and place.

Refining the Research Area

I'm using the steps included in Mildred Patten's Proposing Empirical Research. It is set up with instruction on different topics and walks you through the whole research process, from choosing a topic of study on into conducting the study and drawing conclusions.

TOPIC 1: What is empirical research?

1. What will you observe? personality, culture, and happiness.
2. The main variable is : answers to questions regarding happiness, and Big Five personality tests.
3. Other variables: secondary data.
4. Whom might you observe? general representation of people in different states & regions.
5. How can you observe this variable? Surveys and personality testing.
6. When might you make the observations? Using secondary data, observations already made.

TOPIC 2: Identifying Broad Problem Areas

1. Problem Area: personality, happiness, and culture
2. Description of interest: I question how personality and happiness is passed on through culture. I also question whether a "good fit" between culture and personality makes people happier. By moving can we make a better fit? I believe so, because I did it myself, moving from the Deep South to New England, based on my personality traits.

TOPIC 3: Identifying and Combining Variables

1. Topic area: personality + culture = happiness?
2. Is the area in the mainstream of social work? Culture is, and we are learning more about personality through the Big Five theories, happiness (well-being) is one of our core concerns.
3. Is there a substantial body of literature on the problem area? Yes, I think so. Prof. Hudson has directed to me some references he used in his paper on the geography of mental health, dealing with happiness and personality, and region. He assures me there is lots of new research on connections between personality and happiness. I also know that Refrow has done much study on culture/place and personality.
4. Is the problem area timely? Oh, yes! This is an emerging area that is getting a lot of attention right now. And, also needs further study, which I want to do.