Steps of Research MCQ Quiz in मल्याळम - Objective Question with Answer for Steps of Research - സൗജന്യ PDF ഡൗൺലോഡ് ചെയ്യുക

Key Points 

• Experimenter bias effect, also known as experimenter effect, occurs when the researchers' expectations, preferences, or other characteristics inadvertently influence the participants of a study, thus affecting the study's outcome.
• This bias can lead to the subtle communication of expectations to participants in ways that might not be immediately obvious, such as through body language, tone of voice, or the manner in which questions are asked or experiments are conducted.
• As a result, the behavior or responses of the participants may be influenced not solely by the manipulation of the independent variables intended by the study but also by the researchers' non-verbal cues or biases.
• This can obscure or distort the true effect of the independent variable because the observed outcomes might reflect the experimenter’s expectations rather than the natural response to the experimental manipulation. For example, if a researcher expects a certain result from a specific group of participants, they might unconsciously interact with them in a way that encourages those results, even if the interaction seems neutral on the surface.
• Experimenter bias is a significant concern in psychological and other types of research, as it threatens the internal validity of the experiment.
• To mitigate this bias, researchers can implement double-blind procedures, where neither the participants nor the experimenters know the critical aspects of the experiment, such as who belongs to the control group and who belongs to the experimental group. This approach helps to ensure that expectations do not influence the results of the study.

The correct answer is  (B), (C), (D) only.

Key Points 

Explanation: Remote data collection procedures refer to methods of collecting data from individuals or groups who are not physically present in a common location.

• (B) Pop-ups: These are forms or ads that appear over the content of websites. In the context of data collection, they could be used for various purposes like capturing email addresses, understanding user preferences, collecting customer feedback, and more. As these interactions happen online, without face-to-face contact, pop-ups are considered a remote data collection process.

• (C) Database E-mail: In the context of data collection, this could refer to strategies where information is gathered through emails. This can range from sending out surveys via email to simply analyzing user responses from their emails. As this collects data remotely without physical interactions, it is considered a remote data collection method.

• (D) Instant Messaging: This could refer to several types of synchronous written online communication like Slack, WhatsApp, or Facebook Messenger. These can be used for conducting interviews, group discussions, or surveys where the participants can respond in real time but without any face-to-face interaction. This makes instant messaging a remote data collection method.

The other options, (A) Third party interview, and (E) Panel discussions are not typically considered remote data collection procedures as they require a physical presence in a common location.

(A) Third-party interview: This is a research method where someone other than the primary researcher conducts the interview.

• This could be brought into the remote data collection category if the interview is carried out remotely via video conferencing or phone interviews. However, it can also be conducted face-to-face, which precludes it from being purely a remote data collection method.
• Therefore, whether it belongs to remote data collection methods or not depends on the specific context in which it's conducted.

(E) Panel discussions: This refers to a discussion format where a group of people gathers to discuss a topic, often in front of an audience.

• This can be conducted both face-to-face or remotely (like webinars or video conferences). However, it's not typically, from a traditional standpoint, considered remote as they often occur in person.
• Because of this, it typically wouldn't fall into the category of a remote data collection method unless it is specified that it is conducted remotely.

Additional Information

There are several methods of data collection, which can be broadly categorized into two categories: primary data collection and secondary data collection. 

1. Primary Data Collection:

   • Surveys: Surveys can be conducted through various mediums such as mail, phone, online, or in-person interviews. Surveys are a popular method of primary data collection as they allow researchers to gather large amounts of data from a diverse group of people.

   • Observations: Observational research involves observing and recording data systematically. It can be conducted in a natural setting or a controlled laboratory environment.

   • Experiments: Experiments are designed to test causal relationships between variables. In an experiment, researchers manipulate one or more independent variables and observe the effect on the dependent variable.

   • Focus groups: Focus groups are moderated group discussions designed to gather information about a specific topic. They are often used to collect qualitative data and to gain a deeper understanding of attitudes and behaviors.

2. Secondary Data Collection:

   • Literature review: This method involves collecting data from existing sources such as journal articles, books, and reports. The data collected from this method is often used to build a theoretical foundation or to provide background information.

   • Database: A database is a collection of data organized in a specific way. Data can be collected from databases maintained by government agencies, private organizations, or individuals.

   • Archives: Archives refer to collections of historical records and documents that can be used to gather data. These records can be stored in physical archives or in digital archives.

Hypothesis testing use sample data to make inference about the properties of a population study.

• Samples used are typically a minute proportion of the population thereby leading to misrepresenting the population to cause the hypothesis test to make an error.

There are two types of errors in hypothesis testing. They are Type 1 and Type 2 errors. Both relate to an incorrect conclusion about the null hypothesis.

Therefore option 1 is the correct answer.

• The term hypothesis means prediction or tentative statement.
• We form a hypothesis before doing research.
• A hypothesis is tested on various parameters in order to know the relation between the variables.
• A good hypothesis should be clear and precise.
• It should be capable of being tested. It must be stated in simple terms.
• It makes the research more meaningful by giving direction to the research.
• There are two types of hypothesis one is the Null Hypothesis and the other is the Alternate hypothesis.

Key Points

Null Hypothesis (H0):

• Null hypothesis means there is no difference between the two situations.
• It is denoted by the symbol  (Ho ).
• Null Hypothesis statements are not proved by the researcher.
• It is assumed that there is no observed effect between two null hypothesis statements.
• Example - In an analysis, if we compare Method A and Method B, we proceed with the assumption that both methods are equally good then this assumption is termed as the Null Hypothesis.

Alternate Hypothesis (H1):

• The alternate hypothesis is also known as a substantive research hypothesis.
• It is denoted by the symbol (H1) or (Ha).
• It means a statement in which there is a statistically significant difference between two measured phenomena.
• Alternate Hypothesis statements are proved by the researcher.
• It is assumed that there is some observed effect between two alternate hypothesis statements.
• Example - In an analysis, if we found that boys are more good in math than girls. This tentative statement is known as an Alternate hypothesis as there exists a difference between the two variables.

Important PointsTypes of Error - There are two types of error i.e. Type I error and Type II error

Thus, Both Statement I and Statement II are true.

Probability sampling:

• Probability sampling methods are those that clearly specify the probability or likelihood of inclusion of each element or individual in the sample.
• These are free of bias in selecting sample units.
• They help in the estimation of sampling errors and evaluate sample results in terms of their precision, accuracy, and efficiency, and hence, the conclusions reached from such samples are worth generalization and comparable to the similar populations to which they belong.
• Major probability sampling methods are simple random sampling, stratified random sampling, and Cluster sampling, and Systematic sampling.

Hence, only the snowball sampling method is not a probability sampling method.

Snowball Sampling:

• It is also called network or chain referral sampling.
• To start with, the researcher identifies a small number of respondents having a set of characteristics of interest to the researcher.
• After collecting the required data from those respondents, the same respondents are asked to identify others having the same characteristics set.
• E.g., collecting data from drug addicts, rape victims, etc.

Random/Probability Sampling: In this type, each element in the population has an equal and independent chance of selection in the sample.

2. Stratified Random Sampling: It combines randomization with stratification. Here, the population is divided into strata, the population within each stratum is homogeneous with respect to the characteristic based on which it is being stratified and such characteristics must be identifiable in the study population (e.g. age, income, sex, etc.).

2. Systematic sampling is a statistical method that researchers use to zero down on the desired population they want to research. Researchers calculate the sampling interval by dividing the entire population size by the desired sample size.
3. Cluster Sampling: It is based on the ability of the researcher to divide the sampling population into groups, called clusters and then to select elements within each cluster, using the simple random sampling technique. It is appropriate when the population is large.

The correct answer is: - (I), (L), (J), (K)

Key Points

Understanding Coefficient of Variation (CV):

• Definition:
  • The Coefficient of Variation (CV) is a statistical measure of the relative variability of a data set.
  • It is calculated as the ratio of the standard deviation (σ) to the mean (x̅), then multiplied by 100 to express it as a percentage.
  • Formula: CV=(σ / x̅ )×100% 
• Importance:
  • CV provides insight into the extent of variability in relation to the mean of the data set.
  • It is particularly useful for comparing the degree of variation between data sets with different units or widely differing means.
  • A lower CV indicates less variability relative to the mean, while a higher CV indicates greater variability.

Calculation of CV for Each Data Set:

• (I) " id="MathJax-Element-1-Frame" role="presentation" style="position: relative;" tabindex="0"> x̅ = 30, σ = 3
  $CV = \left( \frac{3}{30} \right) \times 100\% = 10\%$

• (J) " id="MathJax-Element-3-Frame" role="presentation" style="position: relative;" tabindex="0">  x̅ = 48, σ = 12
  $CV = \left( \frac{12}{48} \right) \times 100\% = 25\%$

• (K) " id="MathJax-Element-5-Frame" role="presentation" style="position: relative;" tabindex="0">  x̅ = 18, σ = 9
  $CV = \left( \frac{9}{18} \right) \times 100\% = 50\%$

• (L) " id="MathJax-Element-7-Frame" role="presentation" style="position: relative;" tabindex="0"> x̅ = 30, σ = 6
  CV=(630)×100%=20%  CV = \left( \frac{6}{30} \right) \times 100\% = 20\%" id="MathJax-Element-8-Frame" role="presentation" style="position: relative;" tabindex="0">CV=(630)×100%=20%  $CV = \left( \frac{6}{30} \right) \times 100\% = 20\%$ $CV = \left( \frac{6}{30} \right) \times 100\% = 20\%$

Ranking Based on CV:

• Comparing the CVs:
  • (I): 10%
  • (L): 20%
  • (J): 25%
  • (K): 50%
• Ranking in increasing order of CV: (I), (L), (J), (K).

Additional Information

• The CV is particularly useful when you need to compare variability between data sets that do not share the same units or when the means are substantially different.
• In practical applications, the CV is often used in fields such as finance (to measure investment risk), biology (to assess experimental results), and quality control (to evaluate process consistency).
• While CV is a valuable measure of relative variability, it may not be appropriate for data sets with a mean close to zero, as it can lead to misleadingly high values.

According to Croach and Housden, a sample is a limited number taken from a large group for testing and analysis, on the assumption that the sample can be taken as representative for the whole group.

There are broadly two types of sampling: i) Probability sampling ii) Non-probability sampling

A) Probability Sampling: 

• Probability sampling is defined as a sampling technique in which the researcher chooses samples from a larger population using a method based on the theory of probability.
• For a participant to be considered as a probability sample, he/she must be selected using a random selection.
•  The most critical requirement of probability sampling is that everyone in your population has a known and equal chance of getting selected. 

Probability methods include simple random sampling, systematic sampling, cluster sampling, and stratified sampling.

1. Stratified Sampling: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata are formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally.

2. Cluster Sampling: Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection and data analysis.

Thus, option 3 is the correct answer.

B) Non-Probability Sampling:

• Such a sample is also called a purposive sample. 
• Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. 

There are many types of non-probability samples, including snowball sampling, convenience, purposive/ judgment, quota sampling, dimensional sampling, etc.

1. Quota Sampling: In quota sampling, the population is categorized into several strata which consist of the expected size, and the samples are considered to be important for the population they represent. The advantages of a quota sample are that it involves a short time duration, is less costly, and gives moderate representation to a heterogeneous population. 

2. Snowball Sampling: This is one of the important types of non-probability sampling. In snowball sampling, the investigator encourages the respondents to give the names of other acquaintances and it continues growing in size and chains until the research purpose is achieved. It is also, therefore, known as networking, chain, or referred sampling method. It is very useful in the study of networking and is less costly.

3. Dimensional Sampling: Dimensional sampling is an extension of quota sampling. The researcher takes into account several characteristics e.g. gender, age, income, residence, and education. The researcher must ensure that there is at least one person in the study representing each of the chosen characteristics.

The correct response is the Independent variable.

Key Points

• Independent variable:
  •  This is the variable that is manipulated or controlled by the researcher to see its effect on the dependent variable. It is the cause in a cause-and-effect relationship.

Additional Information

•  Dependent variable: 
  • This is the variable affected by the changes in the independent variable. It is the outcome you are measuring in an experiment or observation.
• Confounding variable: 
  • This is an external variable that interferes with the causal relationship between the independent and dependent variables.
  • It makes it difficult to discern whether it's the independent variable causing the observed changes in the dependent variable, or if it's due to the confounding variable.
• Extraneous variable: 
  • These are variables, other than the independent variable, that could potentially affect the outcome (dependent variable) in a study or experiment.
  • Unlike confounding variables, they do not necessarily mix with the independent variable but can still impact the results.

Therefore, in this case, the variable with a causal impact on another variable is rightly called the independent variable.

The correct response is H1.

Key Points

• A hypothesis is a statement or explanation that is put forward as a possible explanation for a phenomenon.
• The alternative hypothesis can take different forms, including a two-tailed alternative hypothesis (i.e., the mean difference could be either positive or negative) or a one-tailed alternative hypothesis (i.e., the mean difference could only be in one direction).
• In a hypothesis test, the null hypothesis is typically rejected if the evidence supports the alternative hypothesis. The alternative hypothesis provides the basis for making inferences about the population based on the sample data.
• For example, if the null hypothesis is "there is no difference in mean height between Group A and Group B," the alternative hypothesis might be "there is a difference in mean height between Group A and Group B."

Therefore, Hypothesis, that is alternative to null hypothesis, is represented as H1.

Important Points

• In scientific research, a hypothesis is a testable prediction about the relationship between variables.
• It provides a basis for conducting further research to test its validity.
• A hypothesis is usually based on prior knowledge, observations, and theories, and is designed to be falsifiable, meaning it can be tested and potentially disproved. 
• The process of testing a hypothesis involves collecting data, analyzing the data, and determining whether the evidence supports or rejects the hypothesis.
• The alternative hypothesis is a statement about the population parameter that is opposite or different from the null hypothesis.
• The alternative hypothesis is represented as H1 or Ha, where H1 represents the hypothesis of interest and Ha represents the alternative hypothesis.
• The alternative hypothesis is a statement of what the researcher believes is true and what they are testing in a study.
• The alternative hypothesis typically reflects the research question and the direction of the relationship between the variables being studied.

Additional Information 

The answer is  (A) and (C) only.Key Points 

T-test is a statistical method used to determine if there is a significant difference between the means of two groups

 T-Test: 

• The t-test tells you how significant the differences between groups are; In other words it lets you know if those differences (measured in means) could have happened by chance.
•  The t-test is one of many tests used for the purpose of hypothesis testing in statistics.
• There are two types of statistical inference: parametric and nonparametric methods.
• Parametric methods refer to a statistical technique in which one defines the probability distribution of probability variables and makes inferences about the parameters of the distribution.
• In cases in which the probability distribution cannot be defined, nonparametric methods are employed.
• T-tests are a type of parametric method; they can be used when the samples satisfy the conditions of normality, equal variance, and independence.
• It is a two-tailed test

Important Points

• (A) MS Excel: Microsoft Excel provides basic statistical analysis tools, including t-tests. This means that users can perform t-tests using Excel's built-in functions and formulas.

• (C) SPSS: SPSS (Statistical Package for the Social Sciences) is a software package that provides advanced statistical analysis tools, including t-tests. This means that users can perform t-tests using SPSS's advanced features and functions.

Both MS Excel and SPSS can be utilized to perform t-tests, which makes options 3) (A) and (C) only the correct answer.Key Points 

Here is a table explaining the use and definition of the software programs mentioned: