Quantitative research report

Notes 01/08/2022


Assessment 3: Quantitative research report










Some students of Psychology indicate that creating datasets in SPSS makes them nervous. However, the very confident business students (Who some think are arrogant)

What is the IV? The students?
What is the DV? Level of anxiety scores, who is high on them?

What kind of design is this? Independent

Design your data set and enter the scores via SPSS

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What are statistics?

Numbers: Yes and no?

Code numbers and label 1 could be Facebook 2 snap etc

Percentage is of what age group use it

Summary data provides only a snapshot of information

Cannot provide answers to research questions

Inferential statistics

Population statistics is a small group of population

Sample majority groups to get a sample invective of itself

Bivariate regression/ correlation

Multiple regression

Numbers to know what is going on with data: More complicated than mean mode median:


Read field, section 6.4, pp 229-230 “Overview of assumptions” – Vu Collaborate

Assumptions for statistics
Data Types of data categorical or continuous?

Assumptions to be researched rather called conditions

IV, DV. Levels of measurement: Nominal ordinal interval ratio

Mean is the average based on the data provided

Standard deviation where the score deviates from the mean

E sum of x Individual scores n= number of data

The conditions “Assumptions”.. Continuous data looks a bit wrong then you can get the median or mode to bring it back to normal

Statistical models

Model of data is basically like a formula or set of numbers that describes your data. We want what fits the data best, not a poor fit but the best

Outcome= (model) + error Measurement error is maybe the scales are off, not what you expected. Error of 1kg

A simple statistical model: The model doesn’t need to be real, its an idea of what is happening

The mean is the value from which the scores deviate least

The mean as a model

Outcomei + (b) + error i (Confusing but you wanna try and get the understanding)

Outcomei + (b) is the model then add the error in the research i (Confusing but you wanna try and get the understanding)

little e reversed (3) is for error

Type I error is the male one, they go off when they shouldn’t
– Occurs when we believe that there is a genuine effect in our population, when in fact there isn’t
The idea is that the data is significant, there is an effect but there isn’t

Type II error – female doesn’t go off, when they should
– Occurs when we believe that there is no effect in population when in reality there is


Misconceptio 1: A significant result means that the effect is important.



[email protected] Educator

Data view and variable view

Measure: goes under the right measurement

Nominal could be measuring a number of things for example we tell SPS what those numbers mean

SPSS allows for rapid analysis of large volumes of data

Data VIEW and Variable view

Data view where you enter data

Variable view
Where you define the type of data variables you have


Creating variables

Scales of measure meant are important ordinal etc etc

Recap t test is 2 groups or more
Anova is 3 groups or more

ID variable is