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1. Explain the concept of random assignment in experimental design and why it is important.

Random assignment is one of the best of the experimental control techniques. By forming groups randomly, the groups become probabilistically equated on all known and unknown variables at the start of the experiment. The aim of the random assignment is to take a sample, often a convenience sample and divide the sample randomly into two or more groups that represent each other. Every research subject has an equal chance of being assigned to the treatment or control group. Randomization is often achieved through the use of computer programs that can generate random numbers. In experimental research, random assignment is more important that random selection because the aim of an experiment is to establish cause and effect relations. Random assignment plays a role in producing internal validity. In addition, random assignment helps control for the influence of extraneous or confounding variables. Random assignment implies that the researcher is taken out of the loop of making decisions about who goes to the different groups. A mathematic theory of probability is utilized to conduct the random assignment. Random assignment “equates the groups” on all known and unknown extraneous variables at the beginning of the experiment. It makes it possible that any significant difference between the groups is due to the effects of the treatment or program. As such, random assignment increases the confidence of the research to conclude or report a cause and effect relationship. It is a valid and powerful tool for drawing valid inferences about cause and effect.

2. People are often reluctant to discuss personal behaviors, religious beliefs and the like. Suggest ways a survey might be designed so as to maximize respondents’ comfort with such questions.

Surveys are vital strategies for gathering information and sometimes involve the collection of sensitive personal information or opinions. In order to increase responses to such questions, it is important first to establish a rapport with the respondent and to start the interview with questions that do not make the respondent feel vulnerable. Interviews and surveys are based on mutual respect, trust, and rapport, which may be difficult to establish sometimes. In order to improve response to such questions, it is critical to providing confidentiality and anonymity. In the introduction section of the survey questionnaire, it is essential to assure the response that his or her responses will be anonymous or confidential. Inform him or her that responses will be used in combination with others to learn about the phenomenon of interest. In addition, it is vital to obtain consent from the respondent. It is also essential to consider the setting of the survey. Will the question be asked in private or in public?

3. A newspaper reports from a survey that college students study on average seven hours a week for their classes. Identify the statistics and any other information that you would want to know from this survey before making generalizations about the student population at large, and explain why

Researchers use inferential statistics to assess whether it is possible to generalize findings. The generalization of the finding or the external validity of the finding is affected by numerous factors. Most of the reasons for the lack of generalization have to do with the potential sampling errors. These factors help one decide whether to generalize the findings or not. First, individuals who were pretested for the study might be less or more sensitive to the experimental variables. Secondly, the selection of the subjects or participants determines the generalization of result findings. In order to support the generalization of the findings, the researcher ought to use probability or random method of sampling. Thirdly, the experimental procedures and arrangements have the effect on the subjects in the experimental setting. It is not feasible to generalize the findings to persons no in the experimental setting. The nature of the instrument used to collect the data may influence the generalization of findings. Statistical theory severely limits the generality of the results to the same subject population sampled, he same experimental conditions and the same level of the independent variables. Adherence to these requirements may make the research futile. The internal validity of the study is also influenced by the testing effect, instrumentation, and statistical regression. History of all the events that occur beside that treatment and the events in the experiment influences generalization. The psychological and physical changes in the participants determines the level at which it is possible to generalize findings.

4. What statistics covered in this chapter (Ch. 6 and Ch. 7) would you consider essential for reporting the results of your research project, and why?

Often, researchers use a combination of both descriptive and inferential statistics. However, the most important statistics are the inferential statistics that include t-test, ANOVA, correlation, and regression. These statistics enables the researcher to estimate the probability that a sample represent the population from which it is derived. Researchers use inferential statistics to determine the extent and the direction of association between variables of interest. A t-test is very important when a research want to compare two groups of sample.. For example, if a research wants to use a control and experiment group, a t-test would help determine whether the two are significantly different. When more than two groups are used, t test becomes ineffective, and the researcher use ANOVA to determine the probability the groups are significantly different. Correlation statistics is useful in determining the extent and the direction of relationship or association between different variables. ANOVA is important in comparing values of multiple groups to determine the probability they are statistically different.

Descriptive statistics is also essential in the research because they help summarize and describe the sample. Most researchers begin their analysis by giving an overview of their sample, which often constitutes descriptive data. The research may give the mean age of the participants, the gender distribution, education level, income level and other demographic valuables using descriptive statistics such as mean, median, and mode. The measure of dispersions such as standard deviation and variance are essential in describing the sample in a detailed way. In most instances, the descriptive analysis is presented in the form of tables, graphs, and other visual elements.

5. Discuss the advantages and disadvantages of using semantic differential scales as a way of measuring human communication.

Semantic differential scale usually uses rating if stimuli using bipolar scales. Each bipolar scale is defined by a pair of contrasting adjectives such as fast-slow, cheap-expensive and high-low. One of the greatest advantages of the methods is its simplicity while producing results as compared to other complex scaling methods. Semantic differential scaling is easy and faster to administer, but it is insensitive to small differences in attitude. It is also very versatile, reliable and valid. Semantic differential identifies specific favorable or objectionable aspects of a complex and often multi-faceted issues and concepts. It provides an overall response scale score for the entire concept. However, they are only useful when exploring issues involving bipolar opposites. In addition, the adjectives may have different meanings for different people. Semantic differential scales are difficult to construct. Words that form authentic opposite had to be fund and pretested for meaning before use. As such, constructing the differential scales take a time to establish the reliability and validity.

Introduction
The multistep process of developing and implementing an information system is referred to as the System Development Life Cycle. There are various SDLC models with each consisting of a series of defined phases or steps. This paper discusses two types of System Life Cycle Models: Seven steps model and the spiral model.

Seven step model
Planning
The objectives and requirements of the project are determined at the planning step. An estimation of resources including costs and personnel is also made in relation to the proposed project. The available information is analyzed, and alternative solutions are considered. When the most viable alternative is arrived at, the information is put together into a project plan. (Jeremy, 2008)

System Analysis
The end user requirements are determined at this phase. The project team determines the end-user requirements with the assistance of customer focus groups, which present their needs and expectations on the system and how it will perform. The needs and requirements are documented in this phase. (Jeremy, 2008)

System design
The design step is the architectural phase of system development. Charts are used to show the flow of data processing, and the project team establishes the most logical design. The operations and functions of the system under development are described in detail during this phase. Reviews on the design are also conducted to ensure the design addresses efficiency, practicality, cost, security, and flexibility. (Jeremy, 2008)

System development
During the system development phase, the system developers execute the requirements of the design step. Actual user interface screens and database are designed by the developers, the code for the data flow process are also generated in this phase. The system development phase entails the conversion of the detailed design into a finished product. (Jeremy, 2008)

Testing phase
The testing phase involves the testing of all aspects of the system for performance and functionality. The whole system is tested for integration with other products and other previous versions with which it requires interacting. Fundamentally, the main purpose of the testing step is to validate that the system includes all the end user requirements reflected in the analysis step. Additionally, the testing phase also ensures that all the functions are accurately functioning; that the system is aligned to the standards of the business and the end users and that the system works with all other systems including the previous systems. (Jeremy, 2008)

Implementation Phase
The implementation phase entails the deployment and installation of the system in end user’s premises, ready to become running. End user training may be required to ensure that they can effectively use the system. The length of implementation is dependent on the complexity of the system.

Maintenance Phase
The maintenance phase is carried out on a periodic basis to ensure that the system does not become obsolete. Maintenance involves continuous evaluation of system’s performance. It also entails providing latest updates for particular system components to ensure that it meets the right standards. (Jeremy, 2008)

Spiral model
The Spiral Lifecycle model is comparable to the Incremental model except that it incorporates a risk analysis process. A project passes through four phases repeatedly in sequence in spirals. Critical requirements are identified for the first spiral at the start of the process while the Subsequent spirals add functionality to the baseline spiral. (University of Maryland, 2007)

Planning Phase
The business clearly defines its high-level requirements and project goals during this phase. The need and purpose of the system are also established and documented during this step. Key rules are also identified here in the initiation phase. The planning phase entails defining timelines, resources and other project related information. Interviews are conducted to help in developing a comprehensive system that fits users’ requirements. (University of Maryland, 2007)

Risk Analysis
The risk analysis step is fundamental to assess both management and technical risks associated with the project. Risks are identified, and alternative solutions are developed to address factors that may deter the successful completion of the system. (Shelly & Harry, 2009)

Engineering step
A representation of the system is built at this phase. A prototype is tested against the risk evaluated based on the expectations of the end users. The prototype is refined and rectified until end user expectations are achieved.

Evaluation
The final system is thoroughly evaluated during this step. End user feedback is required on which to base the evaluation.

The figure shows spiral model representing four phases; planning, risk analysis, engineering, and evaluation. The radius component represents the project cost while the angular component represents the progress in the current spiral.

Comparing the two models
Spiral Life Cycle Model represents a very flexible system lifecycle model. The seven step model is a rigid life cycle model system on the other hand. The project manager in a spiral model can determine the development phases according to the complexity of the project. The spiral model is transparent as Project monitoring is very effective and easy given that each phase and each loop is reviewed by concerned people. The seven step model does not allow much interaction with the end users during its development. This makes it less transparent. Gary (Shelly & Harry, 2009)

The spiral model is more attractive compared to the seven step model as Risk Management is an inbuilt feature of the model. In a spiral model, alterations can easily be introduced later in the life cycle. Coping with such changes isn’t a difficult task for a spiral model project manager. The introduction of changes in the seven step model presents a difficulty.

Spiral models are appropriate for high-risk projects, where business requirements may be unstable. They are not suitable for low-risk projects. Seven step models suit low-risk projects where projects are not exposed to high risks to warrant detailed risk analysis.

Spiral Model usually involves high cost compared to the seven step model. Seven step models are cheaper since risk analysis is not fundamental to the development process. They do not also require expertise to carry out these steps. (Valacich et al, 2015)

Protocols and Rules need to be followed properly to successfully implement the spiral model. The factor makes it tough as they should be followed throughout the span of the project. Seven step model is easy to develop rules, and protocols are not detailed or complex. In the spiral model, using the same prototype in future presents a difficulty as a result of various customizations allowed from the client.