SPSS MADE SIMPLE: 2.0

INTRODUCTION to EXPERIMENTAL DESIGNS & STAT TESTS: SPSS MADE SIMPLE: 2.0

PUT SIMPLY: this blog post is more for...

..."PSYCHOLOGY STUDENTS". 

• Before you can even input any DATA from an experimental design... you MUST FIRST establish the TYPE of EXPERIMENTAL DESIGN. 

• This MUST BE DONE in order to understand HOW to input your data within any STAT PACKAGE e.g. SPSS.

• WHY BOTHER to know the BASICS of EXPERIMENTAL DESIGNS within SPSS?

• ANSWER: TO understand if a study's findings rings any truth. Just because it may use brain scans etc does not make it valid! 

• There are TWO KEY TYPES of EXPERIMENTAL (EXP) DESIGNS, confusingly enough though it has a number of names for the same type! 

• REPEATED MEASURES is also known as MATCH PAIR SAMPLE or WITHIN SUBJECTS DESIGN! 

• INDEPENDENT MEASURES is also known as BETWEEN SUBJECT DESIGN or INDEPENDENT SAMPLE. 

• WITHIN SUBJECTS DESIGN is called this because the study focuses on the subjects' data differences within the same condition e.g. PRE/POST data differences, and therefore repeated measures. 

• BETWEEN SUBJECT DESIGN is because the study focuses on the subjects' data differences in DIFFERENT CONDITIONS e.g. CONTROL GROUP versus EXPERIMENTAL GROUP, therefore each group is independent of each other.  

• INDEPENDENT SAMPLE has no relation to each other e.g. one person eats fruit  while the other does not. 

• MATCH PAIR SAMPLE has a possible relation to each other e.g. all people eat fruit then all fast.

• The main INDEPENDENT VARIABLE [IV] investigated can have 1+ levels depending on the complexity of the experiment. 

• Simple experiments will have only one [IV] but more complex experiments will have 1+ [IV]. 

• The number of LEVELS within the [IV] will impact the types of STAT TEST you can use on any data obtained from an experiment. 

• So what makes EXPERIMENTAL DESIGNS so scientific, valid, reliable and measurable to REAL LIFE QUESTIONS????? 

• You should understand some KEY STAT terms: "a population" can be just your small sample or alternatively referring to the WIDER WORLD

• The AIM of ANY STUDY is to CHARACTERISE a study's DATA. 

• PUT SIMPLY, how to infer from an experimental data to the wider world... about ANYTHING we are interested in! 

• POPULATION PARAMETERS can be an expressed STAT in 1+ way as per below.

• ALL suggests the level of significance of the data and therefore the CHARACTERISTICS of the DATA in a numerical manner. 

• IF SIGNIFICANT this means we can have a high level of probability that a study's findings - whatever it may be - can be applied to the wider world! 

• LOTS of SYMBOLS are applied in STATS to represent the CHARACTERISTICS within the complex statistical formulas SPSS uses, as per below. 

BUT WHY SHOULD WE BOTHER knowing this at all? 

Can you think of any REAL LIFE IMPLICATIONS? 

• There are TWO MAIN CATEGORIES for STAT TESTS: they can fall into either PARAMETRIC or NON-PARAMETRIC. 

• NON-PARAMETRIC tests are the alternative when PARAMETRIC TESTS are not suitable. 

• AN EASY BREAKDOWN to figure out which one is most appropriate depends on the experimental design & suitable assumptions about the data.  

• There are PRO'S & CON'S of either approach, but the most salient aspect is what is the most suitable method to use for your experiment.

• 1+ STATISTIAL TESTS one can use, however, this blog post will focus just an overview of the T-TEST. 

• Later blog posts will detail the summary table below. 

T-TESTs can FALL in to 2 MAIN TYPES: 

BUT WHY SHOULD WE USE A T-TEST when trying to infer probable conclusions from a population? 

WHAT ABOUT the Z-TEST? Can you think of a condition when it would be more suitable? 

• Z-TESTs are only used when the population parameters are KNOWN within the experiment. 

• T-TESTs are ONLY USED when an experimenter DOES NOT know the POPULATION PARAMETERS!

• T-TESTs can be classed as INDEPENDENT (refer to previous slides if you have forgotten the multi+ names for these two main EXPERIMENTAL DESIGNS). 

• When you finally get around to actually using STAT PACKAGES e.g. SPSS it will look like the box below: 

• The 'ANALYSE' tab on SPSS shows all of the possible STAT TESTS  a person can do to the data obtained. 

• T-TESTs can be classed as DEPENDENT (refer to previous slides if you have forgotten the multi+ names for these two main EXPERIMENTAL DESIGNS). 

• SIGNAL/NOISE within a study refers to things that make it hard to see what our data is really telling us. 

• IN/DEPENDENT T-TESTs will have TWO STANDARD DISTRIBUTIONS = DATA SETS. 

• T-TESTs can be classed as DEPENDENT, in which both data sets are linked and dependent on each other. 

• For example, this could include measuring everyone's IQ after exposing everyone to a new super pill. The difference BEFORE/AFTER will be the two sets of data! 

• There are a few stages that SCIENTISTS SHOULD follow when establishing an EXPERIMENTAL DESIGN towards selecting a STAT TEST.  

• DONT WORRY if you don't understand all of the stages below as this post focuses on introducing the T-TEST & EXP DESIGN. 

• T-TESTs are a very basic measurement that is very useful to compare ANY TWO SETS of DATA.... many experiments can use them to answer real life questions. 

• T-TESTs can only be conducted on DATA if it follows a certain criteria and, therefore, certain assumptions.

• PUT SIMPLY, it can be broken down into 3 STAGES or RULES, which are as follows: 

• T-TESTs MUST SHOW HOMOGENEITY of VARIANCE, aka they must have similar standard distributions.

• There are MANY ALT TESTS people can use - not just a T-TEST.  But this will be blogged about at a later date... baby steps people. 

• The whole rationale for DIFFERENT STAT tests is kinda obvious..... 

• There are 1+ EXP DESIGNS, HYPOTHESES, METHODS, SAMPLES and WAYS in general to measure questions in the world. 

• T-TEST are just one of the simplest measures, whilst ANOVA'S are more complex. 

• For instance, you could use a "Regressional Analysis" which allows you to create a MATH MODEL for predictions based on correlational alone. This is goos for marking masses of information and seeing if the data demonstrates any possible links. 

OVERALL, by learning the RULES of STAT TESTs you can independently value a study's findings for yourself. 

It may seem long winded, but in the end it does benefit you a lot to think more critically.