Browse Tag: Quantitative Research Design

Quantitative research design related academic materials which include essays, research papers, articles summaries, movie and book reviews and much more.

Using Articles To Compare And Contrast Qualitative And Quantitative Research Designs

Identify two articles in the University Library: one in which the business problem is researched using a qualitative design and the other using a quantitative design. Summarize each of the research designs.

Write a 350- to 700-word paper in which you compare and contrast the two approaches:

  • What are the strengths and weaknesses of each approach?
  • How can they be used most effectively in a combined approach?
  • Which method is more appropriate for research in your own business and functional area?

When Multivariate Analysis Is Appropriate For A Quantitative Study

Multivariate analysis deals with the observation and analysis of more than one variable at a time this technique is utilized in performing trade studies in design and analysis across a number of dimensions and at the same time taking into account the effect that the variable has on the responses of interest(Hair,2010).This type of analysis has several uses. These uses include; Capability-based design, inverse design, alternatives analysis, etc.

Multivariate analysis can be used in quantitative studies in various different ways. These include:

  • Organizing and counting of the data that is surveyed.

All social researcher find the raw data as being invariable. This is because it is impossible for them to collect all the data from all the regions. Organization of the data is however very important for the detection of any unknown factors, verifications of the assumptions made and much more. For quantitative analysis, organization of data is very important especially for numerical processes that have to be done such as to simplify on the explanation of the phenomenon (Hair, Black, Babin, Anderson, & Tatham, 2006).

The data thus has to be standardized before analysis is done. Open questions needs some criteria to be set for categorizing the answers. The data can be summarized by conducting some cross tabulation and some statistics.

  • Summarizing of data by multivariate analysis

Using the basic analysis, it might be quite hard to understand the tendency of what is being surveyed when the raw data contains a lot of information and questions. Basic analysis becomes problematic once someone has to deal with more than two variables. In this case, multivariate analysis can be used to analyze complicated information which the human mind cannot adequately comprehend. Its calculation is very intricate though this type of analysis has popularized as computers developed. (Hairet al 2006).Some of the major methods of this type of analysis include;

  • The principle component analysis- it summarizes multivariate information into simpler values.
  • The multiple linear regression analysis- it estimates other variables basing on some of the fixed variables.
  • Factor analysis- uses multivariate data to estimate the potential data
  • Discriminant analysis-it determines which group a certain data belongs basing on some fixed variables(Johnson, & Wichern, 1992)

Multivariate regression works on deriving a formula that describes how some variables change in relation to change in other variables. General linearmodels can be used for the linear relations which makes used of different matrixes with the formula written as;

Y= XB+U

Y represents a matrix which contains a series of multivariate measurements, X represents a matrix which can be a design matrix, B is also a matrix with parameters which can be estimated and U represents a matrix which contains noise or errors(Morrison,1990). The general linear model can used a number of statistical models such as Analysis of Variance (ANOVA), ordinary linear regression, the T and F-test and many more. Multiple linear regression can also be used. According to (Morrison, 1990), is a generalized form of linear regression which considers more than one independent variable and restricts the dependent variable to one. These are used when the errors (matrix U), input in the equation do not follow a multivariate normal distribution. This type of multivariate statistical test may be useful in future research as it will aid in monitoring the changes of variables especially the numeric variable.

 

Prepare a critical analysis of a quantitative study focusing on protection of human

Prepare a critical analysis of a quantitative study focusing on protection of human participants, data collection, data management and analysis, problem statement, and interpretation of findings. The quantitative research article can be from your previous literature review or a new peer-reviewed article.

Each study analysis will be 1,000-1,250 words and submitted in one document. As with the assignments in Topics 1-3, this should connect to your identified practice problem of interest

Refer to the resource entitled “Research Critique Part 2.” Questions under each heading should be addressed as a narrative, in the structure of a formal paper. You are also required to include an Introduction and Conclusion.

Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.

Sampling Strategy and Sample Size for a Quantitative Research Plan

Population

Notably, the population of a given research is constituted by all possible individuals who could be subjects of the research. The target population for the forthcoming study will comprise of all the salaried persons in a given locality defined by high crime incidence. Specifically, the population will comprise of all those have resided in the locality for at least six months and have been getting salaries for at least 12 months prior to being interviewed by the researcher.

Population Size

There is no census that has been conducted in the locality in the recent past. That means that the precise size of the population at present cannot be established. Even then, it is estimated that in the locality, there are 3,500 who will have resided in the locality for at least six months and been getting salaries for at least 12 months prior to being interviewed by the researcher.

Sampling

Samples are subsets of the proposed populations for particular researches. The subsets are deemed to be representing their larger populations according to Bartlett, Kotrlik and Higgins (2001). Notably, the data gathered from the subsets is referred to as statistics, which are used in making specific inferences regarding the populations represented by the subsets. Sampling is the processes of selecting the subsets from given research populations. In the forthcoming research, the sampling will be probability based owing to various reasons.

First, the target population is already well-defined and known. Second, probability sampling will be done since the researcher has a detailed sample-frame, or list, of the population already. Third, the research will entail statistical analysis and only probability sampling methods are well-suited for the analysis. Lastly, the methods are less susceptible to bias than non-probability sampling approaches.

Sampling Type

The forthcoming research will entail the usage of a random sampling approach, simple random sampling (SRS). Notably, the specific sampling techniques adopted by researchers are largely dependent on the forms of interviews to be executed according to Bartlett, Kotrlik and Higgins (2001).  In the forthcoming research, the researcher will carry out structured interviews. SRS is well-suited for such interviews.

As noted before, there are other reasons why SRS will be suitable for the research. First, the target population is already well-defined and known. Second, the researcher has a detailed sample-frame, or list, of the population already (Emmel, 2013). Third, the research will entail statistical analysis and SRS is well-suited for the analysis. Fourth, SRS is less susceptible to bias than non-probability sampling approaches. In the research, the SRS will be executed by computer-based programs. Fifth, the sample will be assembled easily and fairly. Sixth, SRS will be highly representative of the research’s target population. Owing to SRS’ representativeness, it allows for the making of generalizations from sample results to the target populations (Brewerton & Millward, 2001).

How the Sample Will Be Drawn

Given that SRS will be employed in the upcoming research, every member of the research’s target population will stand the same likelihood of being chosen to be one of the research’s subjects. The sampling will be executed in a lone step, will each of the possible subjects chosen independently of all the other possible subjects (Brewerton & Millward, 2001).

Specifically, data on the possible subjects, those who will have resided in the locality for at least six months and been getting salaries for at least 12 months prior to being interviewed by the researcher, will be obtained from the labor office in the locality. The names of the possible subjects and their telephone contacts will be extracted from the labor office’s data base. The names and the corresponding telephone contacts will be saved in a computer. The computer will be used in aiding the random choosing of the forthcoming research’s sample.

Sample size

When executing studies, researchers are keen on the number of responses that they really require according to Bartlett, Kotrlik and Higgins (2001). The following formula has been used in computing the required size of the upcoming research’s sample.

Required Sample Size = StdDev * (Z-score)²  * (1-StdDev)  /  (error margin)²

The error margin, or confidence interval, that will be allowed in the research will be ±5%. The standard of deviation (StdDev) expected in the responses that will be given by the selected subjects will be 0.5. Notably, the 0.5 StdDev will make certain that the sample will be sufficiently large. The expected confidence level in the research will be 95%. The Z-score relating to that confidence level is 1.96. Thus, the required sample size in the research will be computed as:

Required Sample Size = 0.5 * (1.96)²  * (1-0.5)  /  (0.05)²

=  384.16

=  385 respondents

In the light of the research’s 3,500-person target population, the 385-respondent sample will be proper. The sample will ensure that the allowed error margin, or confidence interval, will be minimal. The sample will allow the researcher to be highly confident that the real mean will fall within the interval (Brewerton & Millward, 2001). As well, the sample size will ensure that only a highly limited variance will be expected in the responses. Overall, the sample size will yield markedly precise results as demonstrated by Bartlett, Kotrlik and Higgins (2001).

Which Research Method is Better? – Qualitative Or Quantitative

Generally, the two research techniques have their own advantages and disadvantages. You can read about Qualitative Vs Quantitative Research And Their Strengths And Weaknesses here.  For instance, in quantitative research the use of larger samples and presence of less contact between the researcher and the interviewees makes its findings less biased, (McCusker & Gunaydin, 2014). Whereas the great deal of contact in qualitative research gives information that is subject to bias owing to the in-depth exploration. However, the findings in quantitative research are conclusive and can apply to the rest of the study population, while the presence of in-depth exploration in qualitative research, provide a better understanding of the phenomena. Therefore, a combination of both study techniques, other than choosing one based on its merits or demerits, provides a better method of conducting a study.

Two Major Ways in Which Qualitative Research Differs From Quantitative Research

Generally there are two types of research that one can use to conduct study and these are qualitative and quantitative. The choice of a particular study depends on the goals and objectives for which the study is conducted, (Polit & Beck, 2010). In qualitative research study, special interest is placed on use of the sensory methods like observation and listening in gathering of data. Qualitative research has found a great deal of application in nursing, especially in evidence-based research and is increasingly being accepted in medicine. On the other hand, quantitative research is an investigation that relies on numbers to explain phenomena.

There are major differences that exist between these two forms of research and this touch most on the flexibility of the two methods. The two major differences between qualitative and quantitative research methods occur in their methodologies. While qualitative research seeks to study perspectives in individuals or phenomena, quantitative research on the other hand seeks to prove a hypothesis.

            Tools in Qualitative and Quantitative Research

            Qualitative researcher uses a more structured approach in collecting its data. The major difference between qualitative research and quantitative is that in quantitative research, the researcher employs structured questionnaires, surveys and observation. The questionnaires are often detailed and these are then presented to the participants in the field. However, in qualitative research, the questionnaires are semi-structured in nature and include some questions that guide the respondents.

The structured questionnaires in quantitative research provide data, which is then expressed in numbers for analysis. Since numbers in quantitative research are often numeric in nature, it provides a way in which statistical tests can be applied to test such data, a feat that is absent in qualitative research. Statistical tests used in quantitative research include mean, median, variance and deviations. These descriptive statistics are very useful in determining differences between groups and preference trends among other statistical facts.

However, in qualitative research, participant observation, in-depth interviews and focus groups are often commonly used. Data obtained from qualitative research are often used to describe characteristics or qualities of phenomena. Although encoding provides a way in which the data can be reduced into numbers in qualitative research, this is often not employed. Questionnaires in qualitative research are semi-structured in nature and are mainly used to get qualitative measurements and as such no measurements are done like in quantitative research. Moreover, there are no statistics used in qualitative research, unlike in quantitative research, instead descriptive words are used to explore phenomena.

Sampling

Sampling techniques provides another major difference between quantitative and qualitative research methods. In quantitative research, large samples are used. Often, the study population in quantitative research is large and this is divided into smaller samples using random sampling. In order to get unbiased and reliable findings the samples are given equal chance of occurrence and various strategies of random sampling which include stratified, systematic, cluster and stratified are used. Sampling procedure involves dividing study population into groups and the samples are then selected randomly from the population. The use of larger samples in quantitative research provides a better way of making generalizations using the statistical tests.

However, the focus of qualitative research is on smaller samples of the population. This often takes a form of focus groups, and the main sampling strategies common are snowball, quota and purposive sampling. The main interest in qualitative research is to explore and to explain phenomena. This study design is often concerned more on the process than the outcomes, which is not the case in quantitative research.

Sources of Bias for Quantitative and Qualitative Research

In research, bias can be defined as the process of introducing a systematic error into the sampling and thus encouraging one outcome over the other, (Arnold, 2010). This can be attributed to many reasons, for instance due to experimental error, where the researcher fails to take into account all the research variables. There are many types of biases and all these depend on the research design employed. The following are the possible research biases for both qualitative and quantitative research.

Bias in Qualitative Research

In qualitative research, bias is defined in terms of the validity and reliability of the research findings, (Polit & Beck, 2012). If the findings are not reliable and valid, then they shall be termed as biased. The main disadvantage of bias is that it leads to distortion of truth in addition to producing skewed data.

Bias from Moderator

            The moderator is the individual who is responsible for collecting the research data. The moderator can be a source of bias based on his/her facial expressions, mode of dressing, tonal expressions and style of language among other factors. Although this type of bias is hard to eliminate, it can be minimized through maintenance of neutrality in tonal expression, mode of dressing and language.

Biased Questions

            The way in which questions are asked can influence the answers given by respondents. There is need for the interviewer to determine biased questions and rephrase them. A question forms the main basis in which information is collected and this can lead invalid findings if they are biased. The following are the major ways in which questions can lead to biased research findings.

            Leading Questions

The most common form of bias in asking of questions is the tendency of the field interviewer to ask questions that suggests possible answer, (Green, 2013). Leading questions give slanted answers from respondents. An example of a leading question can take the form of, “Doctors have found that sugars are actually responsible for excess fat in our bodies. What do you think?”

            Misunderstood/ Unanswerable Questions

            Another form of bias with regard to asking of questions is asking the respondent a question, which he/her cannot understand. The respondent will be forced give his/her answer based on the perceived understanding and this can give biased information.

Biased Answers

 Biased answers arise from statements that are generally untrue or partially true. The common occasions where such answers are obtained are when conduction interviews on focus groups. The presence of dominant respondents in focus groups may influence other respondents and this may create skewed answers. Additionally, another source of bias can arise from inconsistent answers especially in questions where one leads to the other question.

  Biased Sample

Sample refers to subgroup of the target population where research is conducted. If the sample is not screened well, one may interview wrong people. Interviewing respondents who do not form the subgroup constitutes bias. Another form of bias in sample selection is failure to use random sampling in the selection of the sample. Random sampling ensures that the study samples have equal chance of selection.

Biased Reporting

 Sometimes, bias can arise from reporting of the results. This source of bias may arise because of personal beliefs, customs, attitude, culture and errors among many other factors. If the person reporting analyses the research information based on his/her beliefs other than the view perceived by the respondents, the findings shall be compromised and hence biased.

Bias in Quantitative Research

            Whereas in qualitative research an effort is made to understand the source of bias, in quantitative research, the researcher tries to eliminate bias.

Design Bias

 Design bias encompasses bias that may arise when conducting the experiments, as well as during analysis of the results. Design biases are always common, mostly due to the failure of the researchers to take into account the likely impact of the bias in the research they conduct.

Sampling Bias

Sampling biases occurs in quantitative research when a researcher compromises with the selection of the study subgroup. For instance, a researcher may decide to omit a certain group in the study sample, or include only a specific group. For example, a study on breast cancer that includes only male participants is said to be biased and its results cannot be extrapolated to cover entire study population that include females. Similarly, when a study is done outside a recreation centre on students in a psychological study is biased since it is not inclusive. The major source of sampling bias occurs in systematic and random sampling.

Random and Systematic Bias

Random and systematic sampling can be a source of bias if the researcher fails to select a representative sample. For, example, if a research is done on a population of around 10,000 students and the researcher takes a sample size of 40 students, then the research would be deemed biased since it is not representative. Additionally, if on selection process, the researcher picks specific group of students then it would give biased results since the chosen study sample should always possess an equal chance of being studied and this should be randomized.

Quantitative Research Article Summary – The Long-term Consequences of Parental Divorce for Children’s Attainment of Education

Bernardi & Radl authored the article and was subsequently published in the journal article of Demographic Research in 2014. The selection of this article was based on its quantitative design of quasi-experimental. The summary of the research model that this journal article employed can be found on page 1659. This journal article is a good example of quasi-experimental design owing to the fact that although the study conducted a comparison of various countries, there was no effort made to collect primary data through random sampling of the study groups. Additionally, this article employed experiment in its study since the groups were investigated without randomization.

Problem Statement

            The problem statement that this article was designed to investigate was the impact that divorce has on the attainment of the tertiary education by the children. Additionally, the other secondary problem was whether the society had any part to play in the parental break up.

Study Purpose

            The main purpose of this research article was to investigate the impact of the divorce on the attainment of tertiary education by the affected children. The other purpose of the research article was to find the extent to which the society had a role to play in the parental divorce.

Research Questions/ Hypothesis

            Research Question

            This research article developed one main research question that was used to develop the research hypotheses for the study. The study employed the following research question:

  • Does parental separation have any harmful consequences for the attainment of education by children with highly educated parents than those of children with less educated parents?

Research Hypotheses

From above main research question, the researchers developed four research hypotheses. The following are the four research hypotheses that were developed, (Bernardi & Radl, 2014).

H1: Divorce among the highly educated parents has more terse consequences on the achievement of post-secondary education by their children than in the children whose parents’ posses less educational levels.

H2: Children whose parents were less educated did not experience any significant impact of their parents’ divorce, on their education.

H3: Stratified educational systems offer the greatest consequences for attainment of post-secondary education for divorce.

H4: In the societies in which there is a lot of divorce, those children whose parents divorce, are less affected.

Study Methods

The study employed surveys of the Generations and Surveys that covered 14 countries, in collecting its data. Data was collected between 2003-2008, and owing to variation in divorce over countries; the study used hierarchical designs and a total sample population of 83,048, aged 25 years and above was used, (Bernardi & Radl, 2014).

Key Findings

The study found that divorce negatively affects the attainment of tertiary education by the affected children. The study further found out that in the 14 countries studied, those children whose parents separated achieved a university degree but their grades were seven percent lower compared to those of children whose parents were not separated. The other finding was that the penalty for tertiary education was high for the parents who had higher education, compared to the ones with less education.

Order a quantitative research article summary of an article of your choice at an affordable price. 

Qualitative Vs Quantitative Research And Their Strengths And Weaknesses

Assignment Instructions

Write a 2- to 3-page narrative essay in which you address the following items:

  • discuss what constitutes a research problem
  • compare and contrast the strengths and weaknesses of quantitative research and qualitative research

Sample Answer

Introduction

A qualitative research is concerned with the investigative methods that are the participant observer, field, and anthropological, naturalistic and ethnographic research. Qualitative research focusses primarily on the data in the field. It is like the research method that explores the various topics given. It aims to give an understanding of various motivations, opinion and reasons regarding a particular topic. Qualitative research uses data collection methods that may be semi-structured or unstructured techniques. Some data collection methods include observations, interviews and focus groups.

An example of a qualitative research article is “Breaking down the barriers to cancer immunotherapy” (Puré, Allison & Schreiber, 2005).

Quantitative research involves variables that can be precisely and accurately measured. In quantitative research, the problem is viewed in terms of data, which is quantified numerical terms in solving the problem. Quantitative research quantifies variables, behaviors, opinions, and attitudes to name a few. It uses data that is measurable for fact formulation. The data collection methods have more structure. They include systematic observations, online polls, website interceptors, longitudinal studies and paper surveys to name a few.

An example of quantitative research in business is “By the Numbers: Total unaided awareness” (Hellebusch, 2006). Qualitative research focusses on the use of words while quantitative focusses on using numbers.

 

Qualitative research strengths

  1. The case can be used to provide an in-depth explanation of a certain phenomenon to the targeted audience.
  2. Can be used to determine the causes of a particular event.
  3. The case information concerning an individual can be provided by the study.
  4. Is effective in the in-depth study of phenomena especially when the number of cases is limited.
  5. Provides description and understanding of various experiences that people have regarding certain phenomena

Weaknesses

  1. The findings may only relate to a few people and not everyone in general
  2. Quantitative prediction is difficult to make
  3. When using many participants, it becomes difficult to test theories and hypothesis
  4. Much time is used when collecting data than when one uses quantitative methods
  5. It takes a lot of time to analyses data

Quantitative research strengths

  1. Data collection is done quickly
  2. Data analysis is fast
  3. Generalization of data can be done if eh random samples are of a sufficient size.
  4. Provides precise numerical quantitative data.
  5. The analysis of data takes a shorter time
  6. It is efficient when the sample sizes are large.

Weaknesses

  1. The researcher can miss confirmation bias by focusing more on testing his theory or hypothesis.
  2. The results may be too general and abstract or application, in particular, various individuals, situation or contexts.

Combining both in concurrent mixed design methods, help one type of data validate the other and hence increase effectiveness and precision of the study. Mixed research, in this case, involves the collection of data by using all procedures concerned with both qualitative and quantitative data. Since some disadvantages can be dealt with by the other method of research, using the two methods enhance the accuracy and precision the data collected.  Therefore, one form of data is validated using the other. The concurrent mixed method provides an avenue where data can be transformed and compared with the relevant questions.

Quantitative research will give more information that is needed in dealing with a business problem like finding new markets. Qualitative research enables one to access data that involves a lot of businesses especially in marketing that will help in determining the problem at hand. Going into the field may not provide enough information on the competition facing the business and the segments in the market. I would, therefore, focus on using the available data and coming up with various conclusions that will help find a concrete solution.

Measurement And Instruments For A Quantitative Research Plan

Levels of measurement used in the research

When carrying out research in social sciences, one needs to be reliable and accurate. Data collection in social sciences takes so many forms. These includes measurement of cognition, perception, opinions and others that cannot be measured directly. In the quantification of perceptions, events and people, there are four types of measurements that are majorly used. (Miller & Salkind, 2002)

The following are the four main levels of measurements that I found to be relevant for my study. I found these methods important because they match the type of data I collected and how I will use them in the analysis and finding the results. These thus goes hand in hand.

The four levels of measurements.

The nominal scale

This is also referred to as dummy codding. This method works by placing people, perceptions, events and many others into categories basing on some common traits. Some of the data naturally suits in the nominal scale. Examples of such include Americans vs. Asians, male vs. females, redheads vs. blondes and many more. The nominal scale is the basis in which analyses such as Analysis of Variance (ANOVA) are formed as they require comparison between two categories. The nominal scale in this case falls into the lowest form measurement as it does not capture any information related to a focal object but basically groups the objects into categories. Coding in this case is done by use of numbers, labels or any symbol that can best represent the category that an object or a person belongs.(Miller & Salkind, 2002)

 

Ordinal Scale

This type of scale has got one major advantage over the nominal scale. It has all the details that are captured in the nominal scale but then goes ahead to rank the data collected from the lowest to the highest. They give an idea of where the data lies in relation to one another. The ordinal scale is evidently richer than the nominal scale but suffers information loss as it only ranks without giving more info on how far apart the ones ranked are. (Trochim & Donnelley, 2001)

Interval Scale

Unlike the two discussed scales of measurement, interval scale provides richer information about an object being studied. It denotes the distance one object is from the other thus providing more information about it. (Isaac & Michael, 1971)

Ratio Scale

This is the scale that provides the richest information about the object. This type of scales has all the information that all the previous three scales have but also contains an additional absolute zero point.(Trochim & Donnelley, 2001)

These four levels of data collection discussed above have an effect on how data is collected and analyzed later. Data collected wrongly will cause an adjustment to the analyses, design and basically the whole research. As I mentioned before, this is the main reason why I chose the above mentioned levels of measurements as they matched my levels of data collection.

Content, empirical and construct validity

Validity is the determinant to whether or not a design is well designed or not well designed and gives the outcomes that seen to be suitable to generalize the population of interest. (Cozby, 2001)

Construct validity refers to the degree to which a particular test measures what it is required to measure. It is very essential to the recommended validity of a test. (Bagozzi, Yi & Philips, 1991)

Empirical validity also known as predictive or statistical validity illustrates how close the scores in a particular test correlate with the behavior as studied and measured in other contexts.(Cozby, 2001)

Content validity also referred to as logical validity describes the extent to which a particular measure stands in place of all the other facets in a particular social construct. (Cronbach, 1971)

How to ensure the three types of validity in a study

It is normally assumed that the study is valid just because the study carried out is scientific. This is normally not true. The researchers who carry out the scientific study are normally pushed by external forces such as the desire to get some certain results. Due to unreliability of the scientist, it is important to ensure that the results are reliable and conclusive. A reliable study avoids biases, utilizes the recommended sample size and majorly use random sampling procedure to collect data.(Cronbach, 1971)

 

To ensure validity, the following has to be undertaken;

  • Randomization

This is critical in ensuring validity of any research. It may be by the use of a random number generator or by use of a computer to collect data. This ensures that there is no bias. It does so by producing the comparable groups such as in the terms of age, gender, the participant characteristics and many more key factors.(Cronbach, 1971)

  • Sample size

The desired population should be able to carry out the study to the conclusion of it.A sample population is thus taken to represent the population. It is thus very important to get a reliable sample size so as to achieve reliable and statically significant results. (Moskal & Leydens, 2000)

  • Bias in results

This involves the production of findings that should not be produces due to alteration of methods. The most common forms or types of biases include; intervention biases which occurs when there is a difference in how the subjects were prevailed to the matter of interest, measurement biases which may be caused by social desirability where people favors themselves and thus may fail to provide honest responses and finally selection biases which occurs when a certain sample is omitted purposively.(Cronbach, 1971)

To improve the validity the following has to be ensured:

  • There should be clear definition of the goals and the objectives
  • The assessment measure should be matched to the goals and the objectives
  • Comparison of data should be made to ensure accuracy (Cozby, 2001)

How to ensure the reliability of the measurements in the study

Reliability is basically the level to which a particular assessment too is capable of providing consistent and stable results.(Cronbach, 1971)

The following will be used to ensure reliability in this text;

  • The test-retest reliability. This is basically a type of reliability achieved when one administers the same type of test twice to the same group of individuals over a certain period of time.
  • Parallel forms reliability. This is achieved by issuing the different versions of a particular assessment to the very same group of people. The different scores can thus be correlated so as to determine how consistent the results are. (Morse, Barrett, Mayan, Olson & Spiers, 2008)
  • Inter-rater reliability. This is used to assess the level in which the different raters or judges agree in their assessment. This is beneficial in that different people or judges in this case will have different views thus making it very reliable as different interpretations are made. (Moskal & Leydens, 2000)

All the above mentioned types of reliability are capable of determining the reliability of a measurement in a study.

Strengths and limitations of measurement using questionnaires

Strengths

  • Very effective way of measuring people’s behavior, preferences, attitudes and opinions thus reliable
  • They enable replication thus making it easy to check for reliability
  • Questionnaires are distributed to several people in an area thus making it easy to gather opinions from different groups of people. (Munn & Drever, 1990)
  • They can be given to the same people twice so checking for reliability is easier.
  • The different versions of the questionnaires can be made and the correlations made to assess the consistency of the answers given. (Kimberlin & Winterstein, 2008)

Weaknesses

  • Respondents may end up lying due to social desirability as they want to portray a positive image
  • People may answer wrongly due to language barrier or misunderstanding the language.
  • The closed ended questionnaires are not detailed thus creating a lesser scope for the assessment which makes it unreliable.(Munn & Drever, 1990)
  • The open ended questionnaires are not suitable for data collection and analysis as they need the researcher to read them in detail.
  • They are not suitable for the less educated as they may end up giving wrong answers. This is because it requires superior writing skills and understanding of the questions so as to express the answers well.(Kimberlin & Winterstein, 2008)
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