Time Series Analysis and Its Applications: With R Examples (Third edition)

A time series is a sequence of numerical data points in successive order. In investing, a time series tracks the movement of the chosen data points, such as a security’s price, over a specified period of time with data points recorded at regular intervals. There is no minimum or maximum amount of time that must be included, allowing the data to be gathered in a way that provides the information being sought by the investor or analyst examining the activity.

Time Series learning

An interrupted time-series design is a quasi-experimental approach and a variant of the pretest-posttest design. In this methodology, a researcher takes a time series—multiple measurements of a dependent variable at regular intervals—both before and after a treatment is introduced. The treatment effectively 'interrupts' the ongoing measurements. Unlike the simpler pretest-posttest design, which relies on a single before-and-after measurement, the inclusion of multiple measurements allows researchers to better distinguish the actual effect of the intervention from normal variation.

Interrupted Time-Series Design

Which of the following best describes the defining characteristic of a time series in psychological research?

A time series is a set of measurements of a quantitative variable taken at sequential intervals over a continuous period of time. This approach allows researchers to track trends and ongoing changes within a specific domain, such as recording a factory's weekly worker productivity over an entire year.

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- Supervised statistical learning
- Unsupervised statistical learning
- Time series learning
- Semi-Supervised learning

Types of statistical learning

A dependent variable is the factor in an experiment that represents the presumed effect within a causal relationship. It is the variable that researchers measure to evaluate if it changes as a direct result of the manipulation of the independent variable.

Dependent Variable

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KPU Research Methods in Psychology - 4th American Edition

Unsupervised machine learning methods find structure in unlabeled data.
They are used when we DON'T have prior knowledge about the categories.

Unsupervised statistical learning

Semi-supervised learning lies somewhere between supervised and unsupervised learning. It takes small amount of labeled data to start learning and then moves on to huge quantity of unlabaled data to develop further.

Semi Supervised learning

Differences about the Supervised vs Unsupervised Machine learning

Training a model using labeled data and using this model to predict the labels for new data is known as __________.

Modeling the features of an unlabeled dataset to find hidden structure is known as _____.

Time Series

Supervised learning is a type of machine learning problem where the objective is to predict a designated unknown label based on known inputs. This approach requires a training dataset consisting of examples for which the ground truth labels are already known, allowing the model to learn the mapping from inputs to classifications.

Supervised Learning

A social psychologist wants to determine if the number of bystanders present during an emergency affects how long it takes for an individual to help. The psychologist stages an emergency in a public space and systematically varies whether there are one, three, or ten other people present. The researcher then measures the time it takes for a participant to intervene and offer assistance. In this experiment, what is the dependent variable?

An experiment can investigate how technology use in the classroom affects learning by comparing note-taking methods. In this scenario, the independent variable is the type of note-taking by participants, such as using a laptop versus writing by hand. The dependent variable is the learning exhibited by participants, measured by their performance on an assessment to evaluate the impact of the independent variable.

Example of an Experiment: Note-Taking Method and Learning Performance

In experimental research, an extraneous variable is any factor that varies within the context of a study other than the specific independent and dependent variables being investigated. These variables pose a significant problem because many of them are likely to influence the dependent variable. If these outside factors fluctuate, they introduce competing influences that make it difficult to separate the true effect of the independent variable from the effects of the extraneous variables.

Extraneous Variable

A confounding variable is an extraneous variable that differs on average across levels of the independent variable, meaning it varies systematically alongside it. Because this variable differs systematically across conditions just like the independent variable, it provides a plausible alternative explanation for any observed difference in the dependent variable, making it difficult to establish a clear cause-and-effect relationship. For instance, if an extraneous variable like intelligence is substantially lower in one group than another, it becomes a confounder that confuses the results.

Confounding Variable

Internal validity indicates the degree to which an empirical study's design justifies the conclusion that changes in the independent variable directly caused the observed differences in the dependent variable. Experimental research designs typically achieve high internal validity because they involve deliberately manipulating an independent variable while rigorously controlling for extraneous variables, thereby eliminating alternative causal explanations. For instance, in the Darley and Latané experiment, the rigorous manipulation of the number of perceived bystanders isolated this independent variable as the direct cause of differences in helping behavior. In contrast, non-experimental correlational designs exhibit low internal validity; for example, a statistical relationship between regular exercise and happiness cannot confirm that exercise causes happiness, as other factors or reverse causation might explain the link.

Internal Validity

In experimental research, a null result occurs when the collected data reveal no statistically significant effect of the manipulation of the independent variable on the dependent variable.

Null Result

Non-experimental research is a broad category of research methodologies defined by the lack of manipulation of an independent variable. Instead of controlling factors, researchers simply measure variables as they naturally occur in real-world or laboratory settings. Although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. Despite this inability to draw causal conclusions, non-experimental research is still considered extremely important and is utilized in cases where experimental research cannot be carried out.

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