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This is a jotted down noted from my Psychological Statistics class last year., Study notes of Psychological Data

Topics on this note are about the different types of statistics. Inferential and Descriptive. Different study designs and their definitions.

Typology: Study notes

2021/2022

Uploaded on 04/06/2023

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PSYCH STAT
STATISTICAL TECHNIQUES MAIN FUNCTIONS:
1.) They provide ways of summarizing the
information that we collect from a
multitude of sources.
2.) Branches of Statistics
Inferential Statistics
- About the economy of effort in
research
- About the confidence with which
we can generalize from a sample to
the entire population.
Ex: The mean SAT score of an 11th
graders in the US.
Descriptive Statistics
- Describe, show, and summarize the
basic features of a dataset found in
a given study, presented in a
summary that describes the data
sample and its measurements.
Ex. Popularity of different leisure
activities by gender.
3.) The amount of data that researcher can
collect is potentially massive.
Variable
- vitally important in statistics
- is anything that varies and can be
measured
Ex: Gender, Eye color
Scores numerical values
MAJOR TYPES OF MEASUREMENT
1.) Score/Numerical Measurement
- The assignment of a numerical
value to a measurement.
- Includes most physical and
psychological measures.
2. Nominal/Category Measurement
- deciding to which category of a variable a
particular case belongs.
- appropriate to refer to it as “Qualitative
Measure”
Ex. Occupation, Gender
Lorry Driver is a name or verbal description of
what sort of case should be placed in that
category.
FORMAL MEASUREMENT THEORY
1.) Nominal Categorization
- Placing of cases into named
categories
- Nominal clearly refers to “name”
- - exactly the same as nominal
measurement or categorization
process
2.) Ordinal (or rank) measurement
- Values of the numerical scores tell
us other than which is the smallest
to which is the largest.
- Sometimes called as “rank
measurement”
3.) Interval or equal-interval measurement
- The intervals between numbers on
a numerical scale are equal on size.
4.) Ratio Measurement
- Has an absolute zero point that is
measured as 0
- Zero is the smallest distance one
can have.
THE DIFFERENCE SCALES OF MEASUREMENT
NOMINAL CATEGORIES
a.) Involves putting a variable into a small
number of categories
b.) The categories do not correspond to
numerical values
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STATISTICAL TECHNIQUES MAIN FUNCTIONS:

1.) They provide ways of summarizing the information that we collect from a multitude of sources. 2.) Branches of Statistics  Inferential Statistics –

  • About the economy of effort in research
  • About the confidence with which we can generalize from a sample to the entire population. Ex: The mean SAT score of an 11th graders in the US.  Descriptive Statistics –
  • Describe, show, and summarize the basic features of a dataset found in a given study, presented in a summary that describes the data sample and its measurements. Ex. Popularity of different leisure activities by gender. 3.) The amount of data that researcher can collect is potentially massive. Variable
  • vitally important in statistics
  • is anything that varies and can be measured Ex: Gender, Eye color Scores – numerical values MAJOR TYPES OF MEASUREMENT 1.) Score/Numerical Measurement
  • The assignment of a numerical value to a measurement.
  • Includes most physical and psychological measures.
  1. Nominal/Category Measurement
  • deciding to which category of a variable a particular case belongs.
  • appropriate to refer to it as “Qualitative Measure” Ex. Occupation, Gender Lorry Driver – is a name or verbal description of what sort of case should be placed in that category. FORMAL MEASUREMENT THEORY 1.) Nominal Categorization
  • Placing of cases into named categories
  • Nominal clearly refers to “name”
    • exactly the same as nominal measurement or categorization process 2.) Ordinal (or rank) measurement
  • Values of the numerical scores tell us other than which is the smallest to which is the largest.
  • Sometimes called as “rank measurement” 3.) Interval or equal-interval measurement
  • The intervals between numbers on a numerical scale are equal on size. 4.) Ratio Measurement
  • Has an absolute zero point that is measured as 0
  • Zero is the smallest distance one can have. THE DIFFERENCE SCALES OF MEASUREMENT NOMINAL CATEGORIES a.) Involves putting a variable into a small number of categories b.) The categories do not correspond to numerical values

Ex: American Team, British Team, Australian Team ORDINAL MEASUREMENT a.) Scores can be ordered from smallest to larges b.) Only rank order is implied. Ex: rather like 1st. 2nd. 3rd^ etc. in a race. Knowing that someone came second does not indicate how far or how many second they were behind the winner. INTERVAL MEASUREMENT a.) Size of the difference between scores is an indication of magnitude Ex: If Bill was 5 seconds behind the winner, Fred was 7 seconds behind the winner etc. then these times are based on equal interval scale of measurement – that is an interval of 1 second. RATIO MEASUREMENT a.) Like interval measurement but allows ratios to be calculated between scores meaningfully Ex. If Tom took 50 seconds and Bill took 100 seconds then it can be said that Tom took half the time than Bill did or that Tom is twice as fast as Bill.

  • There has to be a meaningful zero to the measurement Nominal Category Data
  • There is just one sort of nominal category data Score Data
  • Ordinal or rankable data
  • Interval data
  • Ratio data

CHARACTERISTICS OF VARIABLES

RESEARCH DESIGNS

  • Extraneous variables- all factors that the researcher might not have accounted for but which may have influenced the social interactions.
  • Confounding variable - a specific type of extraneous variable that is related to both of the main variables that we are interested in CORRELATIONAL DESIGN
    • are those that investigate relationships between variables. Ex. Personality factors that related to being good at computer programming. (openness, conscientiousness and introversion) “One of the golden rules of correlational designs is that we cannot infer causation from correlations .”

MEASURES OF CENTRAL TENDENCY

  • Measures of central tendency give us an indication of the typical score in our sample.
  • It is effectively an estimate of the middle point of our distribution of scores

GRAPHICALLY DESCRIBING DATA

Frequency histogram

- Is a graphical means of representing the frequency of occurrence of each score on a variable in our sample. The x-axis contains details of each score on our variable and the y- axis represents the frequency of occurrence of those scores. The frequency histogram is a good way for us to inspect our data visually. Often we wish to know if there are any scores that might look a bit out of place.

  • discover important characteristics (mode, distribution) Stem and leaf plots Are similar to histograms but the frequency of occurrence of a particular score is represented by repeatedly writing the particular score itself rather than drawing a bar on a chart. Box plots Enable us to easily identify extreme scores as well as seeing how the scores in a sample are distributed.

BOX PLOTS

Outliers or extreme scores are those scores in our sample that are a considerable distance either higher or lower than the majority of the other scores in the sample Ways of dealing with extreme scores:

  1. Check that you have entered the data correctly.
  2. Check that there is nothing unusual about the outlying (extreme) score. If you have a good reason then you can remove the participant (case) from the analysis.
  3. If there is nothing unusual about the participant that you can identify apart from their extreme score, you should probably keep them in the analyses. It is legitimate, however, to adjust their score so that it is not so extreme and thus doesn’t unduly influence the mean
  4. If you make such adjustments to the scores, you need to report exactly what you have done when you come to write up the research Scattergrams A scattergram gives a graphical representation of the relationship between two variables. The scores on one variable are plotted on the x-axis and the scores on another variable are plotted on the y-axis.
  • The variance is the average squared deviation of scores in a sample from the mean.
  • The standard deviation is the degree to which the scores in a dataset deviate around the mean. It is an estimate of the average deviation of the scores from the mean. The standard deviation is the square root of the variance.
  • Useful in inferential statistics Kurtosis – Peak of Distribution
  • The kurtosis of a distribution is a measure of how peaked the distribution is.
  1. Platykurtic – flat distribution
  2. Mesokurtic- distribution between /normal
  3. Leptokurtic- very peaked distribution SKEWNESS
  • Skewed distributions are those where the peak is shifted away from the center of the distribution and there is an extended tail on one of the sides of the peak.
  • Negatively skewed distribution is one where the peak has been shifted to the right towards the high numbers on the scale and the tail is pointing to the low number (or even pointing to the negative numbers). A positively skewed distribution has the peak shifted left, towards the low numbers, and has the tailed extended towards the high numbers Bimodal distributions
  • A bimodal distribution is one that has two pronounced peaks. It is suggestive of there being two distinct populations underlying the data.