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Chapter 02 - the research in psychology.

AP Psychology Outline

Chapter 2: The Research in Psychology

Red – Definition

Blue - Important Points

Green - Important People & Contributions

• The Scientific Approach assumes that events are governed by laws.
• Psychologists assume Behavior is governed by laws. (Like the Earth is governed by the law of Gravity.)
• 3 Goals of Scientific Enterprise

                                                               i.      Measurement & Description – Develop Measurement techniques that describe behavior clearly and precisely.

                                                              ii.      Understanding & Prediction – Make and Test predictions called Hypothesis.

1.        Hypothesis – Statements about the relationship between two or more variables.

2.        Variables – Any measurable characteristics or behaviors that are controlled or observed in a study.

                                                            iii.      Application & Control – Apply research findings to help practical problems.

1.        Theory – The system of related ideas used to explain a set of observations. Must be testable.  Based upon experiments and evidence. Is always subject to revision.

• Step 1 : Formulate a Testable Hypothesis
• Scientific Hypothesis must be formulated precisely, and variables under study must be clearly defined.
• Operational Definition – Describes the action or operation used to measure or control a variable.
• Step 2: Select Research Method & Define Study
• Put Hypothesis in an Empirical Test
• Empirical – Knowledge should be acquired through Observation.
• Research Method.
• Define the Study by collecting Participants/Subjects.
• Participants/Subjects – Persons or Animals whose behavior is observed in a study.
• Step 3 : Collect the Data
• Data Collection Techniques – Procedures for Making Empirical Observation and Measurements.
• Examples include (Direct Observation, Questionnaire, Interview, Psychological Test, Physiological Recording, or Examination of Archival Records.)
• Step 4: Analyze the Data & Conclusion
• Use Statistics to analyze data and find if Hypothesis is supported.
• Conclude upon the Findings.
• Step 5 : Report the Findings
• Give the findings to the public so it can be tested. Such as a journal.
• Journal – Periodical that publishes scholarly material, in a narrow field.
• Clarity and Precision
• Small amount of Error
• Experiment – Research Method where a variable is manipulated and changes to the second variable is observed.

                                                               i.      Independent & Dependent Variables

1.        Independent Variable – Variable that is controlled by the Experimenter to see its impact on the other Variable.

2.        Dependent Variable – Variable that is affected by the Independent (Controlled) Variable.

• Experimental Group – Subjects who receive special treatment in regard to the Independent Variable.
• Control Group – Similar Subjects who do not receive special treatment given to experimental group.
• The Differences between the two Groups are the findings.
• Extraneous Variables – Any variables other than the Independent variable that seems likely to influence the Dependent variable in a study.
• Confounding of Variables – When two Variables are linked together in a way that makes it harder to sort their specific effects. Causes great harm to Experiments.
• Random Assignment – Occurs when all subjects have an equal chance of being assigned to any group in the study.
• There can be numerous Independent or Dependent Variables.
• It is sometimes smarter to use only 1 group of students, who serve as their own Control Group.
• Interaction – Effect of one variable depends on the Effect of another.
• Experiments are often artificial, and the decisions based practically might be different of the subjects.
• Ethical concerns prohibit some experiments.
• Some manipulations of variables are nearly impossible.
• Used when Psychologists cannot control the variables they want to study.
• Includes Naturalistic Observation, Case Studies, and Surveys.
• Method permits investigators to only describe patterns of behavior and discover links or associations between variables.
• Naturalistic Observation – A researcher engages in careful Observation of behavior without directly intervening with the subjects.
• Case Study – In-Depth investigation of an individual Subject.
• Generally do not conduct Empirical Data.
• Is Highly Subjective to debate.
• Survey – Researchers use Questionnaires or Interviews to gather information about specific aspects of participant’s background and behavior.
• They depend on Self-Report data, which a variety of factors can distort the true data.
• Investigators cannot control events to isolate cause and effect.
• Cannot factually demonstrate the link between 2 Variables.
• Statistics – Use of Math to interpret, organizes, and summarizes numerical data.
• 2 types: Descriptive Statistics, and Inferential Statistics.
• Descriptive Statistics – Used to Organize and Summarize Data.
• Central Tendency (Typical Score)

                                                               i.      Median –The score that falls exactly in center of distribution of scores.

                                                              ii.      Mean – Arithmetic average of scores.

                                                            iii.      Mode – Most frequent score.

• Variability

                                                               i.      Variability – How much the scores in a data set vary from each other and from the mean.

                                                              ii.      Standard Deviation – index of amount of Variability in a set of data.

• Correlation

                                                               i.      Correlation – When two variables are related to each other.

                                                              ii.      Correlation Coefficient – Numerical index of the degree of relationship between two variables.

1.        Indicates Direction (positive or negative) of relationship

2.        Indicates how strongly the two Variables are related.

                                                            iii.      Positive vs. Negative Correlation

1.        Positive Correlation – the two variables co-vary in the same direction.

2.        Negative Correlation – The two variables co-vary in the opposite direction.

                                                            iv.      Strength of Correlation

1.        Strength related between 1.00 and -1.00.

2.        Closer to 1.00 or -1.00, the stronger the Correlation.

                                                             v.      Correlation & Prediction

1.        As the Corollary increases in strength, the ability to predict one variable based on the other increases.

                                                            vi.      Correlation and Causation

1.        Corollary is not equivalent to Causation

2.        The Corollary could be affected by a third unknown variable that really is the reason for the interaction.

• Inferential Statistics

                                                               i.      Inferential Statistics – Used to interpret data and draw conclusions.

                                                              ii.      Statistical Significance – Exists when the probability that the observed findings are due to chance is very low.  (Less than 5%)

• Replication – Repetition of the study to see whether the earlier results happen again.
• Sampling Bias

                                                               i.      Sample – Collection of subjects selected for observation.

                                                              ii.      Population – Much larger collection of Animals or People from where Sample is drawn.

                                                            iii.      Sampling Bias exists when a sample is not representative of the population from which it was drawn.

• Placebo Effect

                                                               i.      Placebo Effect – When participant’s expectations lead them to experience some changes even though they receive not actual treatment.

                                                              ii.      Is assessed by the inclusion of a fake version of experimental treatment in a study without telling the subject.

• Distortions in Self-Report Data

                                                               i.      Social Desirability Bias – The tendency to give socially approved answers to questions about oneself.

                                                              ii.      Response Set – Tendency to respond to questions in a particular way that is unrelated to the content of the questions.

1.        Some people tend to agree with everything on a questionnaire.

• Experimenter Bias

                                                               i.      Experimenter Bias – When a Researcher’s expectations or preferences about the outcome of a study influence the results obtained.

                                                              ii.      Double-Blind procedure – Research strategy in which neither subjects nor experimenters know which subjects are in the experimental or control group. To combat Experimenter Bias.

• The major Ethical dilemmas reflect upon use of deception, and the use of animals.

                                                               i.      Lying is immoral, so shouldn’t be used in experiments.

                                                              ii.      Honesty vs. Knowledge

• Animal Research

                                                               i.      Most research upon animals done because not allowable with Humans.

                                                              ii.      Most controversy around using animals as subjects in pregnancy and birth defects.

                                                            iii.      PETA is leading group against Animal Research.

• Ethical Principles in Research

                                                               i.      People’s participation in research should be voluntary, and they can withdraw at any time.

                                                              ii.      Participants should not be subjected to harmful or dangerous treatments.

                                                            iii.      If deception is used in a study, participants need to be debriefed as soon as possible.

                                                            iv.      Subject’s right to privacy should never be violated.

                                                             v.      Harmful or painful procedures on animals must be thoroughly justified by potential benefits of research.

                                                            vi.      Research animals are entitled to decent living conditions.

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5.11 Components of Language and Language Acquisition

4 min read • november 11, 2020

Sadiyya Holsey

This whole time we've been talking about memory and bias, but what about language ? Language is the foundation of all thinking and knowledge and it is made by humans. Isn't it crazy to think that we created language to communicate?

Language is a system of spoken🗣️ and written communication✍️ and varies culture to culture.

Components of Language

Syntax refers to the ordering of words when making a sentence.  Every language has their own way of ordering words into a sentence.

For example, in English, we say "my mom's house🏠" or "my sister's pencil✏️" but in Spanish and other romance languages, they say "the house of my mom" or the "pencil of my sister."

Using the proper tense is also an example of syntax .

Grammar refers to the rules of a language and how words should be combined to communicate meaning 🧠

Semantics refers to the study of understanding the meanings of words and word combinations.

Lexicon is the general store of vocabulary for people. For instance, every occupation has “ lexicon ” specific to the field. A chef👨‍🍳 has a different lexicon than a surgeon👨‍⚕️

Phonemes (like phonics) are the basic sound units of language .   

The word "chat" has three phonemes - ch-a-t.

Morphemes are the smallest meaningful units of speech. Remember morphemes = meaning . It may be part of a word, like a prefix or suffix, but it could be a full word as well. Most morphemes combine 2-3 phonemes .

Gif Courtesy of Giphy

Language Acquisition

Language Acquisition Device (LAD)

Language Acquisition Device states that humans are born with the capacity to acquire and produce language . It states that we are all born with an understanding of language .

LAD is used to explain how children can learn languages so well. Children understand that sentences should have a structure before they are able to speak in full sentences.  

Critical Period

Noam Chomsky says that childhood is the critical period for language development and without exposure, it is impossible to learn a language .

Babbling stage 

An early stage of speech that occurs around the age of 3-4 months when children produce spontaneous meaningless sounds (ex. ah-goo). It's basically when they use phonemes that aren't from your language .

At about 10 months old, babbling begins to resemble household language 🏘️.

One-word Stage

At about 12 months old, the child will begin to speak in one word statements that communicate meaning. For example, if they see a cat, they might say "Kitty!" in excitement.

Two-word Stage

At about 18 months old, children begin to speak in two-word statements, like "Get ball⚽," "Want food," and "I tired😴."

Telegraphic speech 

The two-word stage of speech when the child speaks like a telegram. These statements usually consist of one verb and one noun.

At about 24 months old, language develops into full sentences very rapidly.

Overgeneralization

Using grammar rules without proper use and exceptions. For example, a young child might say “I goed to the park,” because they think they can add -ed to anything in the past tense; however, that is an overgeneralization of the rule because there are exceptions.  

Linguistic Relativity Hypothesis

Benjamin Whorf's hypothesis is that language controls the way an individual thinks about their world. People that speak different languages have different perspectives on life depending on how complex their language is. Limitations on vocabulary create limitations in how individuals see the world😲.

In other words, people that are bilingual might describe themselves differently, depending on the language they are speaking in. The more languages you speak, the more word power you have. It's very good for your brain and really expands your capabilities.

Some believe that there are two main parts responsible for acquiring language :

Broca's area 🗣️ - helps with the production of language and language expression. It is in the left frontal lobe and if it were to be damaged, we would have trouble speaking.

Wernicke's area 🧠 - helps with the understanding of language . It is located in the left temporal lobe and if it were to be damaged, we would have trouble understanding.

Aphasia is the impairment of language that occurs when either the Broca's area ( expressive aphasia ) or Wernicke's area ( receptive aphasia ) is damaged. Depending on which type of aphasia one has, one could be able to speak language but not understand it and vice versa. Isn't that weird to think about?

🎥Watch: AP Psychology - Cognition + Language

Key Terms to Review ( 20 )

Babbling Stage

Broca's Area

Expressive Aphasia

Receptive Aphasia

Telegraphic speech

Wernicke's Area

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P-Value And Statistical Significance: What It Is & Why It Matters

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

The p-value in statistics quantifies the evidence against a null hypothesis. A low p-value suggests data is inconsistent with the null, potentially favoring an alternative hypothesis. Common significance thresholds are 0.05 or 0.01.

Hypothesis testing

When you perform a statistical test, a p-value helps you determine the significance of your results in relation to the null hypothesis.

The null hypothesis (H0) states no relationship exists between the two variables being studied (one variable does not affect the other). It states the results are due to chance and are not significant in supporting the idea being investigated. Thus, the null hypothesis assumes that whatever you try to prove did not happen.

The alternative hypothesis (Ha or H1) is the one you would believe if the null hypothesis is concluded to be untrue.

The alternative hypothesis states that the independent variable affected the dependent variable, and the results are significant in supporting the theory being investigated (i.e., the results are not due to random chance).

What a p-value tells you

A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e., that the null hypothesis is true).

The level of statistical significance is often expressed as a p-value between 0 and 1.

The smaller the p -value, the less likely the results occurred by random chance, and the stronger the evidence that you should reject the null hypothesis.

Remember, a p-value doesn’t tell you if the null hypothesis is true or false. It just tells you how likely you’d see the data you observed (or more extreme data) if the null hypothesis was true. It’s a piece of evidence, not a definitive proof.

Example: Test Statistic and p-Value

Suppose you’re conducting a study to determine whether a new drug has an effect on pain relief compared to a placebo. If the new drug has no impact, your test statistic will be close to the one predicted by the null hypothesis (no difference between the drug and placebo groups), and the resulting p-value will be close to 1. It may not be precisely 1 because real-world variations may exist. Conversely, if the new drug indeed reduces pain significantly, your test statistic will diverge further from what’s expected under the null hypothesis, and the p-value will decrease. The p-value will never reach zero because there’s always a slim possibility, though highly improbable, that the observed results occurred by random chance.

P-value interpretation

The significance level (alpha) is a set probability threshold (often 0.05), while the p-value is the probability you calculate based on your study or analysis.

A p-value less than or equal to your significance level (typically ≤ 0.05) is statistically significant.

A p-value less than or equal to a predetermined significance level (often 0.05 or 0.01) indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis.

This suggests the effect under study likely represents a real relationship rather than just random chance.

For instance, if you set α = 0.05, you would reject the null hypothesis if your p -value ≤ 0.05. 

It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).

Therefore, we reject the null hypothesis and accept the alternative hypothesis.

Example: Statistical Significance

Upon analyzing the pain relief effects of the new drug compared to the placebo, the computed p-value is less than 0.01, which falls well below the predetermined alpha value of 0.05. Consequently, you conclude that there is a statistically significant difference in pain relief between the new drug and the placebo.

What does a p-value of 0.001 mean?

A p-value of 0.001 is highly statistically significant beyond the commonly used 0.05 threshold. It indicates strong evidence of a real effect or difference, rather than just random variation.

Specifically, a p-value of 0.001 means there is only a 0.1% chance of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is correct.

Such a small p-value provides strong evidence against the null hypothesis, leading to rejecting the null in favor of the alternative hypothesis.

A p-value more than the significance level (typically p > 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.

This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis; we can only reject it or fail to reject it.

Note : when the p-value is above your threshold of significance,  it does not mean that there is a 95% probability that the alternative hypothesis is true.

One-Tailed Test

Two-Tailed Test

How do you calculate the p-value ?

Most statistical software packages like R, SPSS, and others automatically calculate your p-value. This is the easiest and most common way.

Online resources and tables are available to estimate the p-value based on your test statistic and degrees of freedom.

These tables help you understand how often you would expect to see your test statistic under the null hypothesis.

Understanding the Statistical Test:

Different statistical tests are designed to answer specific research questions or hypotheses. Each test has its own underlying assumptions and characteristics.

For example, you might use a t-test to compare means, a chi-squared test for categorical data, or a correlation test to measure the strength of a relationship between variables.

Be aware that the number of independent variables you include in your analysis can influence the magnitude of the test statistic needed to produce the same p-value.

This factor is particularly important to consider when comparing results across different analyses.

Example: Choosing a Statistical Test

If you’re comparing the effectiveness of just two different drugs in pain relief, a two-sample t-test is a suitable choice for comparing these two groups. However, when you’re examining the impact of three or more drugs, it’s more appropriate to employ an Analysis of Variance ( ANOVA) . Utilizing multiple pairwise comparisons in such cases can lead to artificially low p-values and an overestimation of the significance of differences between the drug groups.

How to report

A statistically significant result cannot prove that a research hypothesis is correct (which implies 100% certainty).

Instead, we may state our results “provide support for” or “give evidence for” our research hypothesis (as there is still a slight probability that the results occurred by chance and the null hypothesis was correct – e.g., less than 5%).

Example: Reporting the results

In our comparison of the pain relief effects of the new drug and the placebo, we observed that participants in the drug group experienced a significant reduction in pain ( M = 3.5; SD = 0.8) compared to those in the placebo group ( M = 5.2; SD  = 0.7), resulting in an average difference of 1.7 points on the pain scale (t(98) = -9.36; p < 0.001).

The 6th edition of the APA style manual (American Psychological Association, 2010) states the following on the topic of reporting p-values:

“When reporting p values, report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001.

The tradition of reporting p values in the form p < .10, p < .05, p < .01, and so forth, was appropriate in a time when only limited tables of critical values were available.” (p. 114)

• Do not use 0 before the decimal point for the statistical value p as it cannot equal 1. In other words, write p = .001 instead of p = 0.001.
• Please pay attention to issues of italics ( p is always italicized) and spacing (either side of the = sign).
• p = .000 (as outputted by some statistical packages such as SPSS) is impossible and should be written as p < .001.
• The opposite of significant is “nonsignificant,” not “insignificant.”

Why is the p -value not enough?

A lower p-value  is sometimes interpreted as meaning there is a stronger relationship between two variables.

However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).

To understand the strength of the difference between the two groups (control vs. experimental) a researcher needs to calculate the effect size .

When do you reject the null hypothesis?

In statistical hypothesis testing, you reject the null hypothesis when the p-value is less than or equal to the significance level (α) you set before conducting your test. The significance level is the probability of rejecting the null hypothesis when it is true. Commonly used significance levels are 0.01, 0.05, and 0.10.

Remember, rejecting the null hypothesis doesn’t prove the alternative hypothesis; it just suggests that the alternative hypothesis may be plausible given the observed data.

The p -value is conditional upon the null hypothesis being true but is unrelated to the truth or falsity of the alternative hypothesis.

What does p-value of 0.05 mean?

If your p-value is less than or equal to 0.05 (the significance level), you would conclude that your result is statistically significant. This means the evidence is strong enough to reject the null hypothesis in favor of the alternative hypothesis.

Are all p-values below 0.05 considered statistically significant?

No, not all p-values below 0.05 are considered statistically significant. The threshold of 0.05 is commonly used, but it’s just a convention. Statistical significance depends on factors like the study design, sample size, and the magnitude of the observed effect.

A p-value below 0.05 means there is evidence against the null hypothesis, suggesting a real effect. However, it’s essential to consider the context and other factors when interpreting results.

Researchers also look at effect size and confidence intervals to determine the practical significance and reliability of findings.

How does sample size affect the interpretation of p-values?

Sample size can impact the interpretation of p-values. A larger sample size provides more reliable and precise estimates of the population, leading to narrower confidence intervals.

With a larger sample, even small differences between groups or effects can become statistically significant, yielding lower p-values. In contrast, smaller sample sizes may not have enough statistical power to detect smaller effects, resulting in higher p-values.

Therefore, a larger sample size increases the chances of finding statistically significant results when there is a genuine effect, making the findings more trustworthy and robust.

Can a non-significant p-value indicate that there is no effect or difference in the data?

No, a non-significant p-value does not necessarily indicate that there is no effect or difference in the data. It means that the observed data do not provide strong enough evidence to reject the null hypothesis.

There could still be a real effect or difference, but it might be smaller or more variable than the study was able to detect.

Other factors like sample size, study design, and measurement precision can influence the p-value. It’s important to consider the entire body of evidence and not rely solely on p-values when interpreting research findings.

Can P values be exactly zero?

While a p-value can be extremely small, it cannot technically be absolute zero. When a p-value is reported as p = 0.000, the actual p-value is too small for the software to display. This is often interpreted as strong evidence against the null hypothesis. For p values less than 0.001, report as p < .001

Further Information

• P-values and significance tests (Kahn Academy)
• Hypothesis testing and p-values (Kahn Academy)
• Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond “ p “< 0.05”.
• Criticism of using the “ p “< 0.05”.
• Publication manual of the American Psychological Association
• Statistics for Psychology Book Download

Bland, J. M., & Altman, D. G. (1994). One and two sided tests of significance: Authors’ reply.  BMJ: British Medical Journal ,  309 (6958), 874.

Goodman, S. N., & Royall, R. (1988). Evidence and scientific research.  American Journal of Public Health ,  78 (12), 1568-1574.

Goodman, S. (2008, July). A dirty dozen: twelve p-value misconceptions . In  Seminars in hematology  (Vol. 45, No. 3, pp. 135-140). WB Saunders.

Lang, J. M., Rothman, K. J., & Cann, C. I. (1998). That confounded P-value.  Epidemiology (Cambridge, Mass.) ,  9 (1), 7-8.

Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

AP® Psychology

The ultimate guide to neurotransmitters for ap® psychology.

• The Albert Team
• Last Updated On: March 1, 2022

Are you getting ready to take the AP® Psychology exam? Are you nervous about keeping all those pesky neurotransmitters straight? Have no fear, because the ultimate AP® Psych guide to neurotransmitters is here.

First Things First: What are Neurotransmitters?

A neurotransmitter is a chemical messenger inside the body. Neurotransmitters carry messages between neurons. They are produced only in the neurons, and because they are a rarer chemical in the body, neurons will recycle the neurotransmitters through a process called re-uptake.

Remember: neurons are the nerve cells that create a giant communication network in our nervous system. There are two major types of neurons, motor neurons and sensory neurons , that allow us to (you guessed it) move around and feel things.

But how do these neurons talk to each other? That’s where the neurotransmitters come in. They are contained in a part of the neuron called the axon terminal button until they are sent to another neuron. Neurons never touch each other, so to get to that other neuron, the neurotransmitter has to cross a small gap called the synapse . The neurotransmitter then crosses over to the neighboring neuron and signals it to activate with an electrical impulse.

When a neuron is not “firing,” it is at its resting potential. When a neuron is signaled by a neurotransmitter to “fire,” leading to an action potential. This means that a neuron sends information down the axon of the neuron – the part that looks like a tail – away from the cell body. An action potential is sometimes referred to as an impulse.

Another important part of the neuron to remember when you’re thinking about neurotransmitters is the myelin sheath . The myelin sheath is a layer of fatty cells – also called glial cells – that surround the axon fibers of the neuron. The myelin sheath is important because it acts as a conductor and insulator, which makes the electrical impulse triggered by the neurotransmitters travel faster down the neurons.

In terms of neurotransmitters, the most important part of the neuron is the synapse. The synapse, or synaptic gap, is where the end of one neuron meets the beginning of another neuron. At the synaptic terminal, vesicles containing neurotransmitters connect to the synaptic membrane , releasing the neurotransmitters into the synaptic cleft . Then, the neurotransmitter binds to receptors on the postsynaptic side of the synapse – the dendrites of the next neuron. That receptive neuron then becomes more or less likely to fire an action potential, depending on the excitatory or inhibitory function of the neurotransmitter.

So that’s how the neurons use neurotransmitters to send messages to the brain. So far, researchers have identified about 15 to 20 neurotransmitters. In general, neurotransmitters can be divided into two types: excitatory and inhibitory. These categories are based on how a neurotransmitter behaves at the synapse – what it signals the next neuron to do. Excitatory neurotransmitters send signals that stimulate the brain. Inhibitory neurotransmitters  send signals to calm the brain down and create balance. If they become overactive, excitatory neurotransmitters can easily overshadow the inhibitory neurotransmitters and reduce their effect.

Important Neurotransmitters to Know for the AP® Psych Exam

Agonists and antagonists.

Neurotransmitters don’t always act on their own. Drugs can affect the degree of a neurotransmitter’s impact. This effect on the neurotransmitter occurs at the synapse.

If a drug increases the effect of a neurotransmitter, it is called an agonist . So if an agonist acts on an excitatory neurotransmitter, the excitatory effect will increase. Here are some examples of common agonists:

• Caffeine: agonist for ACH.
• Selective Serotonin Reuptake Inhibitors (SSRIs): agonists for serotonin. SSRIs increase the amount of serotonin available to the brain, and are commonly prescribed for depression.
• Adderall, methamphetamine, cocaine, and speed: agonists of norepinephrine. When these drugs increase the excitatory effects of norepinephrine, they create feelings of euphoria and extreme alertness.
• Benzodiazepines and alcohol: agonists of GABA.
• Opiates (morphine, oxycodone, heroin, etc.): agonists of endorphins.

If a drug decreases the effect of a neurotransmitter, it is called an antagonist . So if an antagonist acts on an excitatory neurotransmitter, the excitatory effect will decrease. Here are some examples of common antagonists:

• LSD: antagonist for serotonin.
• PCP: antagonist of glutamate. PCP causes a dissociative state that inhibits memory and learning.
• Some drugs that are dopamine antagonists are used to treat psychosis, schizophrenia, and bipolar disorder.

Be careful: agonists and antagonists do not change the type of change a neurotransmitter causes. An antagonist will not change an excitatory neurotransmitter into an inhibitory one; it will just lower the degree of the excitatory response.

Reuptake Mechanisms

Sometimes, there are extra neurotransmitters left in the synapse. To avoid wasting these precious chemicals, the axon terminal will suck up the excess neurotransmitters to be recycled.

Some drugs are  re-uptake inhibitors . These drugs do exactly what their name suggests – they prevent the axon terminals from engaging in the re-uptake of neurotransmitters. Cocaine, for instance, is a re-uptake inhibitor for dopamine. Its stimulating effects are caused by extra dopamine leftover in the synaptic gap.

What You Need to Know for the AP® Psychology Exam

Biological bases of behavior, including the function and types of neurotransmitters, make up about 8-10% of the total exam. According to the College Board’s AP® Psych course description , AP® Psych students should be able to talk about not only the different types of neurotransmitters, but also about the effects of drugs on their effects. This includes agonists, antagonists, and re-uptake mechanisms.

Neurotransmitters can also come into play on the AP® Psychology exam in discussions about sensation and perception, memory and learning, motivation and emotion, and abnormal behavior. Because of the wide variety of ways to think about neurotransmitters, it is important to understand both their functions and the problems associated with their surplus or deficit.

The College Board does not release multiple-choice questions from past AP® Psych exams. However, consider the following sample multiple-choice question from the AP® Psych course description:

Treating a patient for Parkinson’s disease includes administering a chemical that will lead to increases in the patient’s

(a) monoamine oxidase inhibitors (MAOIs)

(b) acetylcholine

(c) norepinephrine

(d) dopamine

(e) serotonin

The correct answer choice is D, dopamine. If you have studied our neurotransmitter chart, then you know that insufficient dopamine production is associated with Parkinson’s disease. However, your knowledge of other neurotransmitters, and the effect of drugs on their messages, can also help you narrow down possible answers in this kind of multiple-choice question.

Answer choice B is incorrect. Deficits of ACH are associated with Alzheimer’s disease, not Parkinson’s – dopamine is not associated with memory.Answer choices C and E, norepinephrine and serotonin, are both associated with mood disorders.

Now that you know norepinephrine and serotonin are not the correct answers, you also know answer choice A cannot be correct. Monoamine oxidase inhibitors, or MAOIs, are antidepressants that function by increasing the amounts of serotonin and norepinephrine, as well as blocking MAO, which breaks down many neurotransmitters.

Your knowledge of neurotransmitters may also be important on the Free-Response Section of the AP® Psych exam. Neurotransmitters are most likely to appear in a discussion of abnormal behavior, psychological disorders, and their treatment.

For instance, here is a past AP® Psych FRQ that asked students to discuss neurotransmitters:

Often misunderstood, schizophrenia is a psychological disorder affecting one percent of the population. In addition to treating the disorder, psychologists work to identify its nature and origins.

• Identify two characteristic symptoms used to diagnose schizophrenia.
• Discuss a research finding that supports a genetic basis for schizophrenia.
• What is the dopamine hypothesis regarding the origins of schizophrenia?
• Describe how medications used to treat schizophrenia affect the actions of neurotransmitters at the synapses.
• Identify a risk inherent in using medications in the treatment of schizophrenia.
• People sometimes confuse schizophrenia with dissociative identity disorder (DID). Identify two key characteristics that differentiate DID from schizophrenia.

You will need to know about more than just neurotransmitters to completely answer all parts of the prompt, but in this crash course review we will focus on the importance of neurotransmitters in understanding and treating schizophrenia.

The third point of this prompt asks you to explain the dopamine hypothesis. The dopamine hypothesis is that schizophrenia is caused by an over activity or excess of dopamine. A more specific way to answer this question is to explain that drugs that block dopamine decrease symptoms, and drugs that increase dopamine increase symptoms.

To answer the next point of the FRQ, you must explain that schizophrenia medications work by reducing dopamine activity. You can say this in any of the following ways: the medications lower levels of dopamine, prevent the release of dopamine, block dopamine receptors, or are dopamine antagonists. Just choose the explanation that makes the most sense to you. Remember to be clear and specific, and answer the question asked of you.

In other types of FRQs, you could be asked to connect the function of a specific neurotransmitter to its physical location. Here is another example FRQ :

For each of the following pairs of terms, explain how the placement or location of the first influences the process indicated by the second.

• Rods, peripheral vision
• A list of unrelated words, word recall
• Serotonin, reduction of depression
• Retinal disparity, depth perception
• Motor cortex, body movement
• Presence of others, performance
• Proximity, perception

Notice how the prompt asks you to explain how the placement of serotonin, not just its function, impacts the reduction of depression. It isn’t enough to say that serotonin is in the body. To answer this part of the prompt completely, you must indicate that increased amounts of serotonin in the brain are linked to reduced depression. You could also indicate that serotonin is located in the nervous system, neurons, synapses, receptors, or other neuron-related locations.

Phew – now you’ve made it through our crash course review of neurotransmitters. It’s a lot of information to take in, but we’ve given you all the tools you need to build a knowledge of neurotransmitters and apply your skills to multiple-choice questions and FRQs on the AP® Psychology exam.

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AP Psychology Study Resource: Definition Of Activation Synthesis Theory

It’s no secret that, as a species, humans are obsessed with the process of dreaming.

We write poems and songs about it. We even have movies about it. A simple google search about dreaming results in thousands of websites where people share their dreams or talk about what their dream interpretations could be.

There are even certain self-proclaimed psychics who claim dreams are a way to see the future.

What’s odd is that there are common dreams, such as teeth falling out or somehow going to work and forgetting to wear clothes. We have all struggled with these silly dreams at one point or another, but how many of us actually understand why?

This is something scientists are continuously trying to figure out. There are a variety of theories about what causes the process of dreaming, although they are all, as of yet, unproven.

One of the most common theories behind dreaming is the Activation Synthesis Theory.

Had Any Good Dreams Lately?

The activation-synthesis theory is a theory based on neurobiological studies into the reasons why we dream.

Since the beginning of time, people have been confused by the process of dreaming. At one point, dreams were alleged to be the chosen method of communication with people from angels or the gods.

Over time, as scientific advancements were made, people began to look at the process of dreaming more skeptically.

Among the people that began to look at dreaming more skeptically were a couple of Harvard neuroscience students named Allan Hobson and Robert McCarley.

Allan Hobson and Robert McCarley were the scientists that first proposed the Activation Synthesis Theory. In 1977, they released a hypothesis that dreaming is caused by the brain trying to make sense of the activity that is still taking place in the brain during sleep.

The brain is the only part of our body that does not rest when we are sleeping. In fact, the brain is always acting at a remarkably high level. The difference when we sleep, according to the hypothesis from Hobson and McCarley, is that the parts of the brain that normally control bodily functions like walking and chewing are now free to take over some of the responsibility of thinking.

How Does Activation Synthesis Work?

The activation synthesis theory is the suggestion that our dreams are caused by these enhanced processes of the brain, which occur when our brain is working entirely on the process of thought. People used to think that sleeping meant we were in a completely passive state. We now know that sleeping is probably the most active period of the day for our brains.

In fact, the brain is almost in overdrive during the time we are sleeping. Our brains work almost like a computer during this time. They are sorting through the activities of the previous day, “filing away” the things we have learned, and making sense of the parts of the day that might have been confusing or overly stressful for us. You might find yourself asking, though, how any of this relates to dreaming.

According to Hobson, when our brains hit the REM cycle of sleeping, our brain begins to sort through the limbic system, which is responsible for emotions, memories, and other such sensations. This is when the process of “making sense” of our thoughts and feelings begins.

Another suggestion from Hobson was that there are five characteristics to dreams that result from this process.

Dreams Are Illogical

Have you ever dreamed you were at dinner with a variety of people that otherwise would not be joining you for dinner? Have you ever dreamed that you went to visit a relative who has passed away? Have you ever dreamed that you were driving a car underwater or flying? Most people have experienced these kinds of ridiculous alternate “realities” while in a dream state.