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# Data Handling and Analysis

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Data handling and analysis are used by psychologists to interpret the data they collect from their studies. When conducting research, psychologists gather, record, and present information, for example, using graphs and charts, so that other people can interpret it.

## What are the different types of data?

How psychologists conduct data handling and analysis depends on the type(s) of data collected. The different types of data are as follows:

• Quantitative data: usually numerical data that can be counted.
• Qualitative data: non-numerical data expressed in words.
• Primary data: information collected directly from the participants by the researcher.
• Secondary data: information not collected by the researcher but which already existed before they began their research.

## What are descriptive statistics?

Descriptive statistics are graphs, tables, and summaries used to identify trends and analyse research data.

The descriptive statistics used in A-Level psychology are measures of central tendency and graphs.

### Measures of central tendency

Measures of central tendency are measures of average values in data sets. They include:

• Mean: the most common measure of central tendency, this is calculated by adding up all the values and dividing them by the number of values.
• Median: the middle value when a data set is arranged from lowest to highest.
• Mode: the most common value.

### Graphs

Graphs can be used to summarise quantitative data. There are a number of ways you can represent data using graphs:

#### Tables

 Control Drug condition Mean 119 86 Standard deviation 23 98

Tables are used to show contrasts between a few sets of data. For instance, the table above shows the difference between control and drug conditions according to mean and standard deviation measurements.

#### Bar charts

Figure 1. A bar chart displaying data. Source: StudySmarter.

Bar charts show the results of different conditions (or variables) using bars of different heights.

#### Scattergrams

Figure 2. A scattergram displaying data. Source: StudySmarter.

Scattergrams present the strength and direction of a relationship between co-variables. They show results using coloured dots across a graph. Researchers may choose to join the dots to show the correlation between different variables.

### Distribution

Data distribution can tell us much about the outcomes of research. Two important types of distribution are the following:

#### Normal distribution

Normal distribution forms a bell-shaped curve, as most data points are clustered towards the central values, while fewer data points are at the extremes.

Figure 3. Graph showing a normal distribution. Source: StudySmarter.

#### Skewed distributions

In skewed distributions, the data set skews to the positive (right) or negative (left) side of the graph.

Figure 4. Graphs showing skewed distributions. Source: StudySmarter.

## Levels of measurement

According to psychologist Stanley Smith, there are four levels of measurement (ways of measuring data). They are:

• Nominal: data that can only be categorised.
• Ordinal: data that can be categorised and ranked.
• Interval: data that can be categorised, ranked, and evenly spaced.
• Ratio: data that can be categorised, ranked, evenly spaced, and has a natural zero so that there cannot be any negative values.

Other ways to measure data include:

• Percentages.
• Decimals.
• Fractions.
• Ratios.

## Case studies

Case studies are a type of psychological study in which researchers investigate a singular, real-life case instead of using artificial studies. Case studies are particularly useful when the psychological phenomenon studied is rare and unusual.

One of the most famous case studies in psychology is Phineas Gage’s brain injury (1848). When Gage suffered damage to his prefrontal cortex, his personality completely changed. This has often been cited as evidence that different areas of our brain are responsible for different things. The prefrontal cortex has been found to be responsible for decision-making and emotional processing.

## Thematic analysis

One of the most common forms of analysis of qualitative research, thematic analysis involves closely analysing research to identify common themes.

For example, a researcher interviewing schizophrenics might notice that hallucinations are a common theme.

Figure 5. A notebook showing notes from schizophrenia research, with ‘hallucinations’ and ‘obsessions’ highlighted. Source: StudySmarter.

## Inferential testing

Inferential testing uses inferential statistics, which is data that allows you to make predictions or inferences.

### Probability and significance

One aspect of inferential testing involves probability. The accepted level of probability in psychology is 0.05 (5%), which means that there is a less than a 5% chance that the results occurred due to extraneous variables (by chance) and that the hypothesis can, therefore, be accepted.

### Statistical tests

The above measure of probability is an example of a statistical test. Other important tests and values when we evaluate our research are:

• Sign tests: statistical tests used to investigate the difference between related items, such as the same participant tested twice.
• Critical values: values that can be compared with the calculated value to see if the result is significant or not.

### Parametric vs non-parametric tests

In parametric tests, the use of averages to interpret data, for instance, implies a knowledge of the existing distribution within the population. It is assumed that the data forms a normal distribution.

Non-parametric tests, on the other hand, don’t make any such assumptions about data distribution among the population.

## Data Handling and Analysis - Key takeaways

• Data handling and analysis are used by psychologists to interpret the data they collect from their studies.

• There are different types of data: qualitative, quantitative, primary, and secondary.

• Descriptive statistics are graphs, tables, and summaries used to identify trends and analyse research data.

• Descriptive statistics include measures of central tendency, graphs, bar charts, and distributions.

• Levels of measurement include ordinal, ratio, interval, and nominal.

• We can use case studies to investigate psychological hypotheses.

• Thematic analysis is used to analyse qualitative data by finding common themes.

• Inferential testing includes probability and significance, statistical tests, and parametric vs non-parametric probability tests.

Data handling and analysis are used by psychologists to interpret the data they collect from their studies.

Data analysis is drawing conclusions from data that has been gathered and presented, using methods, such as graphs and charts.

To interpret data so that it can be employed in useful ways.

There are many types of data analysis, including measures of central tendency, graphs, case studies, levels of measurement, inferential testing, (non-) parametric tests, probability and significance, thematic analysis, and more.

Thematic analysis. This is when a researcher looks at qualitative data and looks for common themes.

## Final Data Handling and Analysis Quiz

Question

Define data handling and analysis.

Data handling and analysis are used by psychologists to interpret the data they collect from their studies.

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Question

Give two examples of data handling.

Graphs and case studies.

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Question

What is quantitative data?

Numerical data.

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What is qualitative data?

Data that uses words.

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Question

Give an example of qualitative data.

A diary entry.

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Question

Which of these is not a measure of central tendency?

Medium.

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What do measures of central tendency aim to find?

Averages.

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Question

Name two types of graphs.

Bar chart and scattergram.

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Name three distribution types.

Normal, positive skew, negative skew.

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In which direction does a positive skew go?

To the right.

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In which direction does a negative skew go?

To the left.

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Question

Analysis that aims to find common themes is known as _____ analysis.

Thematic.

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Give an example of a case study used in psychology.

Phineas Gage.

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Question

What is inferential statistics?

Inferential statistics is data that allows us to make predictions or inferences.

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Question

What is the difference between parametric and non-parametric tests?

Parametric tests assume knowledge of the population, while non-parametric tests do not.

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What are two examples of statistical tests?

Sign test and critical value.

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What is the accepted level of probability in psychology?

0.05 or 5%.

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What are inferential tests?

Inferential tests are various tests such as hypothesis testing that help understand if data collected can be used to make predictions/inferences concerning generalisability to the population.

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Question

Give examples of experimental and sampling errors that may influence inferential tests.

Small sample size, confounding variables that affect the dependent variable, inaccurate or lack precision when conducting research.

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Question

How are alpha scores used as an inferential measure of analysis?

If the alpha level is analysed to be lower than 0.5, then the alternative hypothesis can be accepted. This indicates that the results are unlikely to be due to chance or a Type 1 error and can be generalised to the population.

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Question

How are p scores used as an inferential measure of analysis?

If an appropriate p-value is indicated, then the null hypothesis can be rejected, and the data is indicative of being suitable to be extrapolated to the general population.

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Question

How are confidence intervals used as an inferential measure of analysis?

Confidence intervals can give guidelines of how much the sample deviates from the population. If the data vastly differs, then it is unlikely that the data can be extrapolated to the population.

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What does an 83% confidence interval indicate?

An 83% confidence interval indicates that researchers can be 83% confident that the sample consists of the mean population. If the sampling method were repeated multiple times, 83% of the intervals analysed would represent the population mean.

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Question

Give an example of an alternative hypothesis.

There will be a significant difference between patients who received drug therapy treatment and those randomly assigned to the placebo group.

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Give an example of a null hypothesis.

There will be no observed difference between the day of an exam and time spent studying.

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Why do researchers need to form a null hypothesis when carrying out the hypothesis test inferential analysis?

To identify if there is a relationship between the variables, if the null hypothesis is accepted, then results are likely to be a result of chance.

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After carrying out hypothesis testing, a significance level of .07 was indicated. Should the researchers accept or reject the null hypothesis?

The researchers should reject the null hypothesis and accept the alternative hypothesis. This means the independent variable does affect the dependent variable, and the results are unlikely due to chance or other variables. Therefore, the results are considered appropriate to generalise to the population.

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Define sample errors.

Sampling errors are the difference expected between the sample and general population, as it is challenging to obtain a truly representative sample.

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Why does hypothesis testing take into account sampling errors?

To inhibit errors of accepting or rejecting the hypothesis and decrease the likelihood of type 1 and type 2 errors occurring.

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What are descriptive statistics?

Descriptive statistics are a form of statistical analysis that is utilised to provide a summary of a dataset. These can be summaries of samples, variables or results.

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What are the benefits of measuring descriptive statistics?

These can be beneficial as they provide researchers with information about potential relationships between variables and statistical tests that could be appropriate to test the hypotheses proposed.

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Question

Where can you find data concerning the N of males and females in a sample?

Frequency table

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What statistical information do tests measuring central tendency tell us?

They give a single value that summarises an average representing the entire dataset.

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Question

Here is an example dataset, calculate the mean, median and mode: 2, 7, 5, 3, 9, 12, 3

Mean - 5.86 (2 d.p), Median - 5, Mode - 3

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Question

Which is the most commonly reported central tendency measurement and how is it reported?

Mean (M = x).

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What are the statistics used to measure variability/dispersion?

Range, interquartile range, standard deviation and variance.

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How is the interquartile range calculated?

The interquartile range is calculated by subtracting the difference between the median value in the first half and second half of a dataset.

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Question

A study recruited 10 participants, and the descriptive analysis indicated the mean as 22.8 and the standard deviation as 8.12. How would this correctly be reported in psychology research?

'There were a total of 10 participants recruited for this study (M = 22.8 & SD = 8.12)'.

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What are percentiles?

Percentiles are when data is split into 100ths and data points are observed within the different sections of the percentiles. For instance, if you are trying to identify the data point at 36%, then the values would be placed in ascending order and the value that is representative of 36% of the data would be identified.

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What tests can researchers carry out to identify if parametric tests can be used?

Researchers can identify if parametric tests can be used for statistical analysis if a normally distributed chart is plotted. For instance, if the bell curve is not skewed and if q-q plots show data to be normally distributed.

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What is the purpose of inferential statistics?

The purpose of inferential statistics is to identify if a sample or procedure used is appropriate to generalise to the general population.

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Question

What are the principles of hypothesis testing?

Hypothesis testing requires researchers to formulate a null and alternative hypothesis. The null hypothesis is then tested using an appropriate statistical test and if found to be significant then the null hypothesis can be accepted. This means that the results are likely due to chance or confounding variables rather than the intended independent variable. From these findings, it can be inferred that results observed from research are inappropriate to be generalised to the population.

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Question

What is the definition of a non-parametric test?

Non-parametric tests are also known as distribution-free tests, these are statistical tests that do not require normally-distributed data for the analysis tests to be employed.

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Question

When is it appropriate to use non-parametric tests?

• Data is nominal (data assigned to groups, these groups are distinct and have limited similarities eg responses to "What is your ethnicity?")
• Ordinal (data with a set order / scale eg “rate your anger from 1-10”), there are outliers within the data,
• If data has been collected from a small sample.

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Question

What is the criterion of non-parametric tests?

The following criterion is required for non-parametric tests:

• At least one violation of parametric tests assumptions,
• Non-normally distributed data
• Data is random (taken from random sample)
• Data values ​​are independent from one another (no correlation between data collected from each participant)

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Question

What is the definition of nominal and ordinal data?

Nominal data is when data is assigned to groups that are distinct from each other. An example of nominal data is the response from “What is your ethnicity?”. Whereas, ordinal data is defined as data with a set scale / order. For example the response from "Rate your anger from a scale of 1-10".

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Why does data need to be ranked prior to carrying out non-parametric data analysis?

Data needs to be ranked prior to statistical analysis as these ranked values ​​are used as data points for the analysis rather than the raw values ​​obtained from the experiment / observation.

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Question

What is the 'reference value'?

The reference value is where the researchers predict / hypothesise where the median value is expected to fall.

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What do '+' and '-' ranked values ​​indicate?

Data is assigned as '+' if it is greater than the reference value and data that is '-' is lower than the reference value.

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Question

Rank the following data values and assign them with the correct sign.

Researchers hypothesised that the reference value would be 13. The dataset is: 3, 5, 3, 19, 16, 21, 14.

-3, -3, -5, +14, +16, +19, +21

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