8- XP- Factorial Design

Factor

independent variables in an experiment, especially those that include two or more independent variable

Factorial design

is a research design that includes two or more factors

Single-factor design

research study with only one independent variable

Levels

factorial designs use a notation system that identifies both the number of factors and the number of values or - that exist for each factor

two-factor design; 6

2X3 factorial design would represent a - with two levels of the first factor and three levels of the second, with a total of - treatment conditions

three-factor design; 12

2x3x2 design would represent a - with two, three, and two levels of each of the factors, respectively, for a total of - conditions.

main effect

The mean differences among the levels of one factor are called the - of that factor.

Interaction between factors/ interaction

occurs whenever two factors, acting together, produce mean differences that are not explained by the main effects of the two factors

interaction

An - exists between the factors when the effects of one factor depend on the different levels of a second factor.

nonparallel lines

When the results of a two-factor study are graphed, the existence of - (lines that cross or converge) is an indication of an interaction between the two factors

Mixed design

factorial study that combines two different research designs.

Combined strategy

uses two different research strategies in the same factorial design.

Combined strategy

One factor is a true independent variable (experimental strategy) and one factor is a quasi-independent variable (nonexperimental or quasi-­ experimental strategy).

Sampling

procedure of identifying a representative group from the target population from which data shall be obtained

1. defining a sampling universe

2. deciding on a sample size

3. devising a sampling strategy

4. sourcing the sample

Sampling steps

Inclusion criteria

attributes that participants must posses

Exclusion criteria

attributes that disqualify a participant from a study

• Demographic

• Geographical

• Physical

• Psychological

• Life history

Source of deciding on homogeneity

Demographic homogeneity

commonality such as a specific age range, gender, ethnic or socio-economic group

Geographical homogeneity

Refers to sample that is all drawn from the same location

Physical homogeneity

Occurs in a sample who must share a common physical characteristic

Psychological homogeneity

Similarity within a sample imparted when participants are selected based on the possession of a particular trait or ability

Life history homogeneity

Occurs in a sample if individuals share a past life experience in common

Nomethic

Interview studies that have a - aim to develop or test general theory are to a degree reliant on a larger sample size to generalise

Idiographic

Interview research that has an - aim typically seeks a sample size (3-16) that is sufficiently small for individual cases to have a locatable voice within the study, and for an intensive analysis of each case to be conducted

• Probability

• Non-probability

Main types of sampling

Probability sampling

is when you select a smaller group from a larger population using a randomized process.

Non-probability

involves selecting your sample, rather than leaving it to chance.

• Simple random

• Systematic random

• Stratified random

• Cluster

Probability sampling methods

Simple random sampling

ensures every member of a larger population has an equal probability of being selected for the study.

Systematic random sampling

each person is assigned a number and then participants are selected at regular intervals.

Stratified random sampling

each member of the larger population is categorized into another subset based on characteristics. For example, age, gender, income and so on.

Cluster sampling

rather than randomly choosing participants from every subgroup, you simply choose an entire subgroup to form the final sample.

• Quota

• Purposive

• Snowball

• Self-selection

• Convenience

Non-probability methods

Quota sampling

In this method, the population is split into segments (strata) and you have to fill a quota based on people who match the characteristics of each stratum.

Purposive sampling (judgemental/selective/subjective sampling)

sampling where you make a conscious decision on what the sample needs to include and choose participants accordingly

Snowball sampling

sampling type that mimics a pyramid system in its selection pattern. You choose early sample participants, who then go on to recruit further sample participants until the sample size has been reached.

Self-selection sampling

uses volunteers to fill in the sample size until it reaches a specified amount.

Convenience sampling

sampling where you choose participants for a sample, based on their convenience and availability.

1. Randomization

2. Fair chance

3. Full knowledge of population

4. Objectivity

5. Harder to sample

Characteristics of probability sampling

1. Deliberate choice

2. Stacked chance

3. Varying knowledge of population

4. Depth

5. Faster to sample

Characteristics of nonprobability sampling