Research Methods Analysis Essay Example
Research Methods Analysis Essay Example

Research Methods Analysis Essay Example

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  • Pages: 11 (2979 words)
  • Published: June 18, 2018
  • Type: Analysis
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Executing research necessitates a variety of decisions, including the choice of method, structuring the investigation, pondering aspects related to the plan, enhancing reliability and validity through measurement and optimization, picking participants, handling relations between investigator and participant, tackling researcher impacts, data analysis and ethical deliberations. When investigating if high noise levels interfere with memory task performance efficiency, running a lab experiment could be beneficial due to its command over variables and possibility for repetition. This testing involves altering an independent factor while assessing a dependent one. Furthermore, intricate happenings in the natural environment can be simplified by deconstructing them into more elementary components to effortlessly exclude interfering elements.

There are several benefits to conducting laboratory experiments, such as the ability to discern cause and effect relationships. However, they also come with a


set of drawbacks. Achieving absolute control is an impossibility in reality, and the outcomes could be swayed by irrelevant variables, biases from experimenters or volunteers, sample bias, and demand characteristics. Moreover, the lab environment is synthetic and may not accurately mirror real-world circumstances. This absence of ecological validity and external validity may result in limited generalizability because there might be other confounding factors that influence the results. Also, tasks executed during a lab experiment might not truly capture the complexity of everyday life outside of it. Subjects' reactions in a lab setting can range from conforming to perceived expectations or displaying contrived behavior due to fear of judgement. The knowledge that they are under observation can lead participants to guess what the experimenter's hypothesis is and modify their behavior accordingly; this forms demand characteristics.

The participants in an experiment are aware that they are being

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observed, a phenomenon known as evaluation apprehension. When considering the ethical implications of this, it may not always be possible to obtain informed consent from participants, and they may not have the true ability to withdraw from the study. Additionally, participants should not be exposed to stressful or negative manipulations. One alternative to traditional lab experiments is field experiments, which offer greater ecological validity because they minimize experimenter bias and evaluation apprehension by keeping participants unaware that they are part of an experiment. This method focuses on observing behavior in its natural environment and reduces the likelihood of demand characteristics occurring. Field experiments involve direct control of the independent variable and direct allocation of participants to different conditions, allowing for the determination of causal relationships.

Field experiments hold the benefit of having adequate control, permitting duplication. The conduct noticed in these types of experiments closely mirrors standard behavior, making it less fabricated than laboratory experiments. However, despite lab trials usually possessing strong internal validity, they often lack external validity. Conversely, field experiments have high external validity but may be weaker in terms of internal validity. This methodology does come with its setbacks such as issues with managing extraneous variables and potential disruptions to the cause-and-effect correlation. Issues like biased sampling might also persist, making this method more costly and time-consuming than lab tests. Moreover, gathering large quantities of data can pose a challenge and it's not easy to eliminate confounding factors.

Moral considerations - Obtaining informed consent and conducting debriefing is unfeasible in this study, potentially leading to discomfort for participants unknowing of the simulation. Moreover, a natural experimental approach is employed to analyze causal relationships under

certain circumstances. This strategy provides enhanced ecological validity and prevents experimenter prejudice by ensuring participants are oblivious to the fact they're undergoing observation. Consequently, their actions remain authentic, facilitating the examination of independent variables without any immoral tampering.

This method also has some disadvantages: - It is unable to confidently establish the cause and effect relationship due to the influence of numerous other factors on the dependent variable. The lack of control reduces internal validity and may result in a lack of a suitable control group. Replicating such studies is challenging and can only be done when conditions naturally vary, which may not always be possible to find. Additionally, participants being aware of being studied may exhibit improvements solely because of this awareness. The independent variable is not directly manipulated, and participants are not randomly assigned to different conditions.

In the context of ethical factors, a potential scenario might involve executing a natural experiment in which one set of students does not receive access to an innovative learning program for research reasons. Under these circumstances, participants may not be aware they're part of the experiment, sparking concerns around informed consent. Researchers should also consider the difficulties encountered by disadvantaged individuals, especially with respect to their behavior being studied - such as impoverished children. Other non-experimental research methods encompass naturalistic observation, correlational studies and interviews or questionnaire surveys.

Naturalistic observation consists of monitoring behavior in its natural environment with minimal interference and allowing variables to fluctuate naturally. There is no manipulation of any independent variable in this method. This approach has benefits regarding ecological validity since it circumvents problems like demand characteristics and evaluation apprehension. It additionally permits a

broad range of behaviors to be observed, offering a more accurate portrayal of authentic behavior.

Moreover, if the observer remains unnoticed, this technique can elude most experimental biases. However, there are disadvantages that need consideration too. The method doesn't permit inferring cause-and-effect relationships and replication could be complex leading to uncertainty on whether results are consistent or merely singular occurrences.
Controlling external factors and eliminating observer prejudice can be quite challenging, as observers may perceive what they wish to perceive. Moreover, the knowledge of being observed might change participants' behavior.

Revealed observations may lead people to act in an artificial manner. This issue brings up ethical questions since concealed observations do not permit informed consent, while revealed observations might cause anxiety for the people being observed.

Correlation is a statistical method used to gauge the intensity of connectivity between two variables. Correlational analysis investigates whether there exists any relation between two variables, such as aggression and the quantity of violent television viewed. Nonetheless, a correlational study is unable to establish causality.

In this approach, a numerical figure is computed to signify the level of correlation between two groups of data. The phrases "independent variable" (IV) and "dependent variable" (DV) are not applied in correlational studies because they are independent from each other. Rather, these variables are known as co-variables.

When a perfect positive correlation is signified by +1.00, it means that both variables escalate together. On the other hand, -1.00 represents a perfect negative correlation which happens when one variable grows while the other shrinks. This approach brings numerous benefits such as recognizing possible connections between co-variables and igniting future research notions that delve into potential cause-and-effect associations. Moreover, this technique

provides room for estimating one variable based on another – an extremely useful function in scenarios where it's impossible to manipulate the variable. The ability to confirm the absence of any relationship between variables can also aid in disproving cause and effect.

Experimental designs are useful for fast and efficient collection of a large amount of data on various variables. Yet, this method has its disadvantages. Primarily, it doesn't provide solid grounds for cause-effect relationships. Moreover, the interpretation of results can be tricky, with the causality direction being unclear. Additionally, other unrelated variables might affect the experiment outcomes. For example, in studies examining diet and IQ relations, parental intelligence could influence results as smarter parents might provide better diets to their children. Ethically speaking, making causal claims should be avoided even though misinterpretations are common occurrences—this is especially crucial when handling socially sensitive matters such as IQ that often depend on correlational data. Interviews and questionnaires can come in multiple formats varying in structure and delivery method (either face-to-face or written). Among these forms, non-direct interviews have minimal structure thereby giving interviewees freedom to discuss nearly any subject.

In non-directive interviews, the interviewer's responsibility is to steer the conversation and encourage further openness from the interviewee. This interviewing style is often employed in mental health treatment due to its ability to yield invaluable in-depth insights that can't be reproduced. In casual interviews, the focus of the interviewer is on patiently listening and motivating the interviewee to delve into a more profound dialogue. Specific broad themes are probed for comprehensive information, yet duplication isn't achievable. A slightly more structured approach is taken in casual guided interviews than in

regular informal ones, as the topics of discussion are predetermined by the interviewer.

During the interview process, decisions are made regarding how and when to address these issues. The provided information is highly detailed and challenging, yet it can be replicated to some extent. Structural open-ended interviews involve a formal approach where all interviewees are asked the same questions in a specific order. This procedure prevents the interviewee from deviating from the topic and taking control of the interview. It allows for replication and facilitates comparison between individuals. Clinical interviews, on the other hand, are utilized to evaluate patients with mental disorders. Similar questions are posed to all interviewees, but the follow-up questions are influenced by the responses given.

The approach referred to as fully structured interviews, utilized by Paiget during interactions with children, mandates asking a predetermined set of queries consistently in an unchanging sequence for all individuals being interviewed. The responses from participants are confined to a limited array of choices. This method of data collection can be executed face-to-face, through telephone calls or mail and provides a rapid and simple way to accumulate data. An alternative means of gathering data is through questionnaire surveys where respondents fill out written questionnaires. This technique delivers voluminous amounts of data quickly and economically and can also be duplicated easily. However, it's worth mentioning that regardless of the interview type, issues related to interviewer prejudice could arise along with the likelihood that respondents may not reveal their true feelings or thoughts due to social desirability bias.

Principles of ethics:
Respect for confidentiality is paramount, particularly in situations dealing with personal issues. Merely concealing an individual's name might not be

sufficient to safeguard their identity. Researchers conducting interviews must exercise extreme caution to prevent research data from getting into the wrong hands. Interviewees should never feel pressured to respond to uncomfortable questions and they should consistently be reminded that there is no obligation for them to answer any undesired questions.

Designing the research:
In the process of planning a scientific investigation, numerous aspects need consideration so as ensure its effectiveness. A critical step in developing an experimental study involves defining the research goals. These objectives embody the rationale behind formulating questions which will later be answered by this study. The objective is wider than a hypothesis, denoting why we are conducting the study while hypothesis points towards specific elements that this investigation aims to test or verify. For example, one such goal could involve investigating if long-term memory gets affected by varied processing methods used during learning.

A hypothesis refers to a clear, formal declaration of what one considers to be accurate. It acts as an anticipation or expectation in a particular scenario. Numerous hypotheses are regularly made, like predicting the victory of your basketball team. Psychologists articulate their hypotheses for specifying what they intend to confirm or refute through their studies. Hypotheses could be more detailed, such as anticipating that there is greater free recall from long term memory when semantic processing takes place during learning compared to non-semantic processing. Furthermore, there's a category of hypothesis known as the null hypothesis, which claims that no disparity or connection exists among the populations under examination. This hypothesis encapsulates the forecasted effect of an independent variable on a dependent variable. For example, a suitable null hypothesis might state that

loud noise doesn't affect people's capacity to absorb information.

Central to research is the null hypothesis, due to its precision and the fact that it counters the impossibility of proving something - only disproof is possible. The concept of variables, defined as elements with alterable values, plays a crucial role in experimental design where they are usually classified into independent, dependent, and extraneous categories. In this setting, the independent variable (IV) is intentionally adjusted to gauge its effect on the dependent variable (DV), which typically undergoes measurement or evaluation. For instance, a study investigating performance under stress may assign participants either an arduous or simple puzzle task. Herein lies an example of IV being represented by puzzle difficulty while DV symbolizes task performance. Controlled variables are considered confounding variables.

Non-experimental research approaches like interviews and observations are often improved by having a hypothesis. Yet, these techniques won't confirm any possible cause-and-effect relationship. For example, when observing cow feeding habits in an observational study, the researcher collects data throughout the research design process. In contrast, experimental research involves two kinds of hypotheses: one-tailed or directional hypotheses anticipate how an independent variable (IV) might influence a dependent variable (DV).

For instance, the assertion that "a loud noise can impair a person's learning ability" can be bolstered by a bidirectional or non-specific hypothesis. Such a hypothesis assumes that the independent variable (IV) will influence the dependent variable (DV), albeit without defining the exact direction of this effect. To illustrate, we could say "loud noise will impact a person's learning capacity." Moreover, there are three kinds of experimental setups: an independent design, matched participants design and repeated measures design.

With an independent design,

each participant is allocated to just one group. Similarly in a matched participants approach, individuals are also assigned to solely one group but they're paired with another participant from another group based on pertinent factors like age and sex. Meanwhile in repeated measures setup, each individual participates in both groups which ensures consistency in participant presence across all groups.

The benefits of an independent groups set up include: firstly, avoiding sequence effects; secondly ensuring no loss of participants between trials; and finally its applicability when repeated measures setup isn't suitable. However this setup has its shortcomings too such as potential significant variations among individuals at the start of the study.

The advantages of using a matched participants design include controlling for certain individual differences between participants and being applicable in cases where a repeated measures design is not appropriate. However, matching pairs can be challenging and more participants are required compared to a repeated measures design. On the other hand, a repeated measures design controls for all individual differences and requires fewer participants. However, it cannot be used in situations where participation in one condition influences responses in the other.

The comprehension of the study's objective by participants can lead to demand characteristics, resulting in potential issues. Additionally, using a repeated measures design might give rise to order effects. These are observed when participants repeat the same task multiple times and perform differently each time due to various factors such as practice or fatigue. To mitigate this problem, a method known as counterbalancing is employed where half of the participants are given condition A first and then B while for the other half it's reversed.


considerations need to be made for an effective research design. It is essential that all participants receive standard instructions consistently and standard procedures are used for data collection to ensure uniformity across all researches. Equal treatment should be accorded to all participants throughout the duration of the study.

Managing variables that might conceal the effect of an independent variable (IV) is referred to as control of variables. It's crucial to regulate confounding variables to prevent persistent errors. Variables that could fluctuate between conditions, such as participants feeling more fatigued or driven in one scenario compared to another, pose challenges for control. These troublesome confounding variables also need regulation. Some variables, such as performance, have a wide scope and hence it's necessary to define them operationally in a precise manner. For example, long-term memory (LTM) performance can be quantified by the count of words recalled. The advantages of operationalization encompass offering a lucid and objective explanation even for intricate variables.

The text discusses the disadvantages of using operational definitions, the importance of conducting a pilot study, and the significance of measuring and improving reliability in research. Operational definitions are criticized for being circular and incomplete, and there may be disagreements about their accuracy. A pilot study is conducted before the main study to address any issues and make necessary adjustments, saving time and money. The goal of research is to design studies that can be replicated, requiring measures with good reliability. Internal reliability refers to the consistency of a method within itself. The split half technique is used to establish internal reliability by comparing scores from two halves of the same participant's data collected at the same time.


concept of inter-rater reliability is used to ascertain the dependability of observations. Enhancing this reliability necessitates the use of more than one observer, who must be accurate and utilize well-defined behavioral categories. Moreover, every observer should undergo training in this system's application. The method to gauge inter-rater reliability involves a correlational analysis.

External reliability pertains to a method's consistent measure over time when repeated. For instance, there shouldn't be significant variations in scores from an IQ test when taken on different occasions. One can assess this through the test-retest technique, where participants take identical tests at different times to check if their results remain approximately the same.

In terms of evaluating and augmenting validity - it questions whether a methodology can fulfill its intended purpose. Validity implies truthfulness. In experiments, it checks whether measurements accurately represent what they are designed to measure. A considerable challenge in establishing validity within psychological research lies in that with more precision and control over experimental conditions, there is a lesser chance for these measures to apply broadly or generalize in real-world scenarios.

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