11. Descriptive research strategy

11. Descriptive research strategy

1. Descriptive statistics

   • organise and summarise data simplify and interpret the inferential statistics
   • Functions:
     • Describe the characteristics of the sample – both numerically and graphically
     • indication of where a sample tends to sit
     • Can provide suggestion of whether the sample is representative of the population
   • Ratio and Interval Data
     • Ratio data has a true zero value (e.g., height or length), whereas interval data does not (e.g., temperature on the Celsius scale, year of birth)
     • Measures of Central Tendency
       • identifies a single score that defines the centre of the distribution of scores in the sample Goal identify the most typical
       • mode
         • most commonly occurring score
         • 2 peaks are bimodal distribution
         • can be used with categorical data
       • Median
         • score that divides a distribution in half when the scores are listed in rank order
       • Mean (most common used in research)
         • mathematical average of a set of scores, abbreviated as M
           • M = (sum of the scores) / (number of scores)
   • Frequency Distributions
     • A frequency distribution table consists of two columns of information: First column ► the scale of measurement Second column ► the frequency of individuals located in each category
     • Distribution characteristics
       • Kurtosis
       • Skewness
         • Highly skewed - median is best representation
         • generally not using SD but IQR
       • Normal Distribution
   • Dispertion
     • Range = (highest score) – (lowest score)
     • Interquartile range (IQR) (more common used) = range of the middle half of the distribution 4 quartiles: Q1, Q2, Q3, Q4
     • Standard deviation “SD” (most common) = the square root of the variance (the average distance of each data point from the mean)
   • Nominal and Ordinary
     •    •In nominal data there is no ranking between categories (e.g., one gender is not superior to another) •In ordinal data there is ranking between categories, however the spacing between the categories is not even or is unknown (e.g., military ranks)   
     • Common Descriptives:
       • Frequency distributions
       • Percentages (prevalence): (number of observations within a category/number of total observations)*100
       • Proportions: number of observations within a category/number of total observations
       • Mode: most frequently endorsed category
       • Illustrated graphically

2. Inferential statistics

   • help make generalizations about populations answer the general questions that initiated the research

3. Population vs Sample

   • Population
     • Elements can be people, objects, events, or observations based on measurable characteristics of  people, objects, or events (e.g., dieting behaviour, weight, gender, cortisol levels).   
   • Sample
     •   A subset of elements within a population   

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