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Data Analysis in Psychological Research Cheat Sheet by

data analysis techniques and methods in psychological research


Data Analysis means examinig, sorting, catego­rising, comparing and evaluating the coded data
Types of Analysis
Descri­ptive Statistics-techn­iques used to summarize and display numerical data.This provides a general unders­tanding of trends in the data
These include: central tendency,
canonical analysis
Processing of Data
Preparing data for analysis
Editin­g-p­roc­essing raw data, detect errors
Coding-assigning symbols to responses to be put into categories
Classification- reduce to groups
Tabulation-arranging in logical order
Using percen­tages
Infere­ntial Statistics or Statis­tical Analysis- draw conclu­sions, generalize results from sample to the popula­tion, find meaningful relati­onship from data, and reduce possib­ility of error
These include: hypothesis testing (param­etric, nonpar­ametric tests),
estimaition of parameter values

Measures of Central Tendency

arithmetic average of distri­bution of numbers
middle score in an ordered distri­bution
most frequently occuring score ina distri­bution

Distri­bution of Data

Normal Probab­ility Curve (NPC/NDC)
special type of density curve that is bell shaped
describes tendency of most data to normally cluster around the middle
non symmet­rical data
collection of data on either side of the curve
peaked or flat distri­bution of data






Measures of Relati­onship

Univariate (one variable(
Bivariate (two variables)
Multiv­ariate (more than two variables)
one way ANOVA- analysis of variance which is one direct­ional, x - y
Index Number - measure of relative change in magnitude of a variable (change in price of commodity in the span of a year)
Time series analysis - observ­ationof a phenomenon over a period of time (trend analysis)
Simple Correl­ation- determine the strength and direction of relati­onship between two variables
Simple Regression-study cause and effect relati­onship, determ­ination of statis­tical relati­onship between two/more variables, used for prediction of future values
Two way ANOVA
Multiple Regression and Multiple correl­ation
Multiple discri­minant analysis-tech to distin­guish datasets obf particular charac
Canonical Analysis-deter­mining relati­onship between two sets of variables simult­ane­ously
The strength of the relati­onship will always range between +1.00 and -1.00 If the number is closer to +1.00 or -1.00, it indicates a strong correl­ation between the variable. The closer the number is to 0, the weaker the relati­onship becomes
bivariate contd.
Coeffi­cient of Associ­ation- indicates strength of relati­onship between variables
Coeffi­cient of Contin­gency- indicates whether the IV and DV are dependent or indepe­ndent of each other
mulitv­ariate contd.
Factor Analysis-data reduction system
Cluster Analysis-used to classify objects into groups where objects in one group are more similar to each other and different from objects in other groups
correl­ation does not prove causation
correl­ation can be studied through: Charles Spearman's coeffi­cient OR Karl Pearson's coeffi­cient

Statis­tical Signif­icance

Used to determine whether the differ­ences in the data set are signif­icant or not, i.e whether the differ­ences are real and not caused due to random variations of the experi­ment. It gives us a probab­ility that the results were caused by chance and not by experi­mental manipu­lation
Type I error-we accept Ho when it is false
Type II error- we reject Ho when it is true
Probab­ility is denoted by p indicating the difference due to chance
For ex. If p < 0.05, it means that there is a 5 out of 100 probab­ility of result being due to chance OR 95% certain that results were real and not due to chance

Measures of Variab­ili­ty/­Dis­persion

measure of how much values in a dataset differ from the mean
the amount of dispersion of scores
difference between values of extreme items(­highest and lowest scores)
Standard Deviation
average distance between the scores and the mean OR
avergae squared deviations from the mean scores in a distri­bution

Infere­ntial Statistics

Point estimate- a single value, best estimate of a parameter
Interval estimate-a range of plausible values of a parameter
Parametric test-specifies certain conditions about parameter of popula­tion, stronger than nonpar­ametric tests
normally distri­buted data
ex. z test, t test, F test
Nonpar­ametric tests-does not specify any condit­ions, distri­bution free statistics, data does not fall under NPC
ex. Man Whitney U test, Kendall's tau, chi-square test


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