Chapter 1 - Key Science Skills

Chapter 1B-1G

What is a controlled experiment?

establishes a cause-and-effect relationship by testing how the independent variable (IV) affects the dependent variable (DV).

Extraneous variables

Extraneous variables are additional factors that can affect the measured data. For example, in a study about whether studying more leads to higher grades, extraneous variables could include natural intelligence, your teacher, or sleep.

What are qualitative and quantitative data, and how are they used together?

Qualitative data includes descriptive, written observations, while quantitative data consists of numerical values. When combined, they create a mixed-method design, allowing for both numerical and written forms of research in one study.

Quantitative data

Quantitative data consists of numerical values.

Mixed-method design

is an experiment that incorporates both numerical data and written commentary.

Case studies

are detailed studies that do not include experimentation and often have very small sample sizes. A weakness is that their findings cannot be generalized.

What are observational studies (fieldwork)?

involve observing people in their natural environment. A strength is that the data may be more accurate, but a weakness is that assumptions can easily be made.

Correlation studies

involve observing the relationship between two variables to determine if they are related.

Literature review

involves using secondary data to gather research for comparison before conducting primary data investigations.

Modeling/simulation

involves using a small or large prototype to understand and investigate an object or concept.

Product, process, or system development

It involves designing and developing something using technological applications.

Classification and identification

involve organizing things into similar sets based on shared characteristics.

Population

the group that is the focus of study and which the sample is drawn from

Sample

a subset of the research population who participate in a study

Convenience Sampling

Selecting a sample

The first consideration for eliminating bias and error in research.

Increasing sample size advantage and disadvantage

It increases accuracy but also affects time and cost.

Selecting a representative sample

To ensure it is characteristically representative of the population.

Factors impacted by increasing sample size

Time and cost.

Trade-off when increasing sample size

Higher accuracy versus greater time and cost.

Convenience sampling

A sample selected in the easiest and quickest way possible.

Advantages of convenience sampling

Very quick and easy.

Limitations of convenience sampling

Not representative of the population.

Random sampling

Every member of the population has an equal chance of being selected.

Advantages of random sampling

Free from bias; quick and easy.

Limitations of random sampling

May not be representative of the population.

Stratified sampling

The population is divided into subgroups, and participants are selected based on their proportions in the population.

Advantages of stratified sampling

More representative of the population.

Limitations of stratified sampling

Time-consuming.

When is stratified sampling used?

In important research where characteristics could skew data.

Example of convenience sampling

Surveying people at a shopping mall because they are easily accessible.

Example of random sampling

Assigning each student in a school a number and using a random number generator to select participants.

Example of stratified sampling

Dividing a city's population into age groups (e.g., teens, adults, seniors) and selecting participants proportionally from each group.

Random allocation

When every member of the sample has an equal chance of being in either the control or experimental group.

Control group

The group used as a basis for comparison; they are not exposed to experimental conditions (independent variable).

Experimental group

The group exposed to the experimental conditions or manipulated independent variable.

Can experiments have more than one control group

No, typically only one control group is needed as a baseline for comparison against experimental groups.

More than one experimental group

Yes, experiments can have multiple experimental groups.

Controlled variables

things that are controlled and kept the same in an experiment. 

The Hawthorne Effect/The Placebo Effect

The placebo effect is improvement due to belief in a treatment, and the Hawthorne effect is behavior change from being observed.

What is the one thing that should be different between the experimental and controlled group?

The IV

What forms can extraneous variables take? Name 5.

• Non standardised instructions

• individual participant differences

• Order effects

• Experimenter effects

• Placebo Effects

What is the description, advantage, and limitation of a between-subjects design (random allocation)?

Description: Every member of the sample is randomly allocated to either the control or experimental group.
Advantage: Cost and time efficient.
Limitation: There may be individual participant differences.

What is the description, advantage, and limitation of a between-subjects design (paired allocation)?

Description: Each member of the sample is paired based on a particular characteristic, and one member of each pair is randomly allocated to the control and the other to the experimental group.
Advantage: Few individual participant differences between the groups.
Limitation: Time-consuming, as a pre-test is involved.

What is the description, advantage, and limitation of a within-subjects design?

Description: Each member of the sample is involved in both the control and experimental conditions.
Advantage: No participant differences.
Limitation: Order effects.

Confounding variables

are external factors that interfere with the relationship between the independent and dependent variables.

Personal Errors

Mainly a individual error of the researcher, for instance an experimenter effect

Random Errors

like an extraneous variables, something we didn’t control for and are unpredictable

Systematic Errors

as in the case of confounding variables they are errors in measurements that are all consistently shifted from the true value (predictable)

How to overcome random errors

by repeating the measurements

How to overcome systematic errors

adjust the measurer to the correct use

How to overcome personal errors

dependent on specific error

What does a random error affect in the experiment?

the precision of the data (how close to each other the data is)

What does a systematic error affect in the experiment?

the accuracy of the results (how close to the true value the results are)

What does a personal error affect in the experiment?

dependent on specific error

Difference between extraneous and confounding variables

Extraneous variables can affect the dependent variable, while confounding variables specifically influence both the independent and dependent variables. All confounding variables are extraneous, but not all extraneous variables are confounding.

How to prevent extraneous variables from occurring?

• Controlled variables

• Look at experimental design

• Look at procedures used

What is the experimental design: Independent Groups?

Participants are randomly allocated to either the control or experimental group, and each participant experiences only one condition.

What is the experimental design: Matched Participants?

Participants are paired based on similar characteristics, and one from each pair is placed in the control group and the other in the experimental group.

What is the experimental design: Repeated measures?

The same participants take part in both the control and experimental conditions, experiencing all levels of the independent variable.

Single blind study and how does it control extraneous variables

participants don't know which treatment they are receiving, but researchers do. This method controls extraneous variables like an participants expectations.

Double blind study

neither the participants nor the researchers know. These studies help minimize biases from both sides.

Order effects

Order effects is an extraneous variable and occurs when the sequence of tasks or conditions in an experiment influences the results. This can include practice effects (better performance later) or fatigue effects (worse performance later). t

How to avoid the confounding variable of order effects?

Counterbalancing is a method used to control for order effect by varying the order of conditions across participants.

Situational variable

an external factor in an experiment that can affect participants' behavior, such as the environment, time of day, or equipment used.

True or False: Extraneous variables are controlled by control variables

True

Primary Data

Data that is sourced first hand

Secondary Data

Data that is sourced through someone else’s research

Advantages of Primary data

• More specific and detailed

• Can inform future research

• It will be current data as you are finding it for your own purpose

Disadvantages of Primary Data

Advantages of Secondary Data

• Cheap and easy

Qualitative

Data that is descriptive (Why did this happen?)

Qantatitative

Data in numerical and categorical form (How much or how many?)

Disadvantages of Qualitative Data

Hard to compare and scale

Disadvantages of Quantitative Data

Doesn’t tell the whole story

Subjective Data

Data that relies on assumptions or personal experience, dependent on the participant eg. mood

Objective Data

Data that can be directly observed or measured, eg. speed, score

Subjective Data Advantages

• Cannot collect it any other way

• Gives us insight to what makes us human

Subjective Data Disadvantages

• How do we compare between two people

• How do we trust their answer

Advantages of Objective Data

• Can see it

• Can compare it

Disadvantages of Objective Data

• Why did it happen

• Can’t see the extraneous variable

Descriptive Statistics

Mathematical calculations that describe, organise and summarise data (DOS).

Descriptive statistics include

• Mean, mode and median

• Range and standard deviation —> how far spread the scores are from the mean

• Presentation of data: frequency distribution tables, bar graphs, line graphs, percentages, etc

What is a benefit of descriptive data?

Allows us to compare and make observations between other data

Mean

average score, find by adding all scores and dividing by number of pieces of data

Median

a middle score

Mode

the most frequent score within the data

What are the three measures of central tendency?

• Mean

• Mode

• Median

Range

difference between highest and lowest scores in distribution

Standard deviation

on average, how far each score lies from the mean, how much variability there is in the distribution of the data

Frequency distribution table

a table of data that can be presented in a different way like a bar graph, histogram, plot graph, etc

Histogram vs Bar graph

The bar graph is the graphical representation of categorical data, bar graph’s bar’s don’t touch each other. A histogram is the graphical representation of quantitative data, the bars do touch each other.

Discrete data vs continous data

Discrete data represents exact figures you can count, such as the numbers of students in a class. In contrast, continuous data often includes measurable values representing a range of information, such as the extent of the difference between the shortest and tallest student in a class.

Does x axis represent IV or DV?

IV

Central tendency

is a way to describe the typical or middle value of a dataset, using measures like mean, median, and mode.

Uncertainty

the lack of exact knowledge relating to smth being measured due to potential sources of variation in knowledge

Why don’t we want human judgements to impact data?

since it could be wrong if not backed up with data

Inferential statistics

mathematical calculations used to make inferences, judgements and conclusions from data (IJC)

Examples of inferential statistics

• T-tests

• Chi-squared tests

• P-values