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

Detailed Literature Review

Andrew, P. Researching the geography of mental wellbeing. Journal of Mental Health; Aug2007, Vol. 16 Issue 4, p439-443.

LINK ON PC

Discusses the relation of geography research on mental wellbeing research. Describing a variety of methods of analysis, and why that type was chosen. Describes how the field has grown to include spatial information, as well as other data -- space as related to incidences and statistics - especially in GIS systems. For instance, it can now be found: ‘‘mental health differences in Wales are partly explained by the level of regional social deprivation’’ or that there is a "clustering of childhood leukemia around a nuclear power plant." A geographer can add a new element to the research questions when statistics exist in spaces, and tell us about the "role of context on a phenomenon." Also, we may be able to determine needs assessments for an area, and change the location where we provide services to more closely meet the need.

Multi-disciplinary approach, which can use professional geographers. Digital mapping and GIS systems (analysis of geographically-referenced data) are now being used at the desktop computer level. E.g., a GIS of disease would have the spatial location (geo-coordinates) of each incident together with information about the disease (disease name, age, gender, date, outcome, etc. GIS allows for review of possible spatial clustering of diseases/incidents.

Other layers of information can be added from disparate sources (census race and age, local unemployment, county income, climate, weather, hwy networks, public transp., hospital records of MH incidences) And, then, you can find patterns of information.

May began "medical geography" in 1950, and MH geography began to be used in 1970s. In this work, it's been stressed that professional geographers can add an overall layer of knowledge and interpretation to the field of study.

REFERENCES:

Gatrell (2002). Geographies of Health (book)

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.

Cognitive Aspects of Compulsive Hoarding. Journal article

 Steketee, G., Frost, R. O., Kyrios, M. (2003).  

       Cognitive aspects of compulsive hoarding. 

       Cognitive Therapy and Research 27(4), 463-479.

Hoarding has been defined as the acquisition of and failure to discard possessions that are useless or of limited value, resulting in the clutter that renders living spaces unusable for their intended purpose and causes significant distress and impairment (Frost & Hartl, 1996).

The purpose of the current study is to examine whether beliefs play a role in the development of compulsive hoarding, and whether hoarders differ in their belief systems as compared to non-hoarders (i.e., other participants with OCD and also community controls). The study adds evidence to the theory that hoarders are influenced by several cognitive deficits that may lead to compulsive behavior.

Not only did the researcher find that hoarders tend to be older, but they also found that their small sample of hoarders were primarily women. Self-report and data analysis show that exaggerated beliefs about the the importance of possessions, the increased emotional attachment they feel toward their possessions, problems with memory, and the need for control play a part in the development of compulsive hoarding behavior.

People who hoard are more likely to believe that their possessions are part of themselves and feel distress when faced with the decision whether to discard their possessions. Hoekstrata, Visser, and Emmelkamp (1989) found limited data showing that people experiencing OCD had histories of attachment problems, as displayed by parental rejection and overprotection), and recent evidence points out that hoarders have difficulty forming and maintaining relationships with people.

It has also been found that people with OCD disorders display an increased need for control. (I would imagine this might be a consequence of the disorder, as well as a condition involved in the formation of the disorder). Kyrios (1998) proposed that those with OCD lack a basic sense of security, so that, fearing disorganization and losing control, they establish unrealistic and unattainable strategies to feel certainty and control over their world. It is proposed in the current study that hoarders attempt to gain a measure of control over their environment by controlling their possessions.

The study also adds evidence to our knowledge base that people who hoard experience memory and organizational difficulties, and therefore fear making decisions about discarding possessions. They report being worried about making the wrong decisions and forgetting, feeling overly responsible for remembering things, the need to keep a record of information which helps them in "being prepared" , and finding emotional comfort in having their possessions under their direct control or in plain sight.