psychology statistics homework help

psychology statistics homework help

The Importance of Statistics in Psychology: A Comprehensive Guide for Students

1. Introduction to Statistics in Psychology

Knowing statistics is not enough, of course, to ensure that you will succeed. You also need a sincere willingness to learn. You should be willing to examine and memorize, albeit for a short time, the major concepts and their statistical equations which are presented here. Although you will undoubtedly be exposed to Greek letters, mathematical symbols, and mathematical equations soon after you begin your study of statistics, we want you to know that you can overcome your aversion to these most essential tools and their most essential symbols. Whatever fear, dread, or hatred that you or others might have acquired from statistics can and should be eliminated. Let our guide show you the way.

Does statistics strike fear into your heart? If, indeed, your first reaction to statistics and statistics classes consists of extreme fear or even dread, your feelings are neither unusual nor unrecognizable. But, we would venture to argue, if you learn the basics of statistical concepts, and especially their statistical equations, well, it can be comforting. Beyond this introduction, you will find ten major chapters in your course of basic and applied statistical concepts and techniques. These ten chapters will teach you almost everything you could conceivably want to know about basic and applied statistics as they relate to psychology.

2. Descriptive Statistics: Measures of Central Tendency and Variability

While the measures of central tendency describe the peak of the data, measures of variability describe its spread or distribution. The main ways to measure variability are by using the range, interquartile range, variance, and standard deviation. The range describes the difference between the largest and smallest numbers. The interquartile range measures the middle 50% of the data. The variance represents the average distance from each data point to the mean of the data. The standard deviation adds interpretation to this variance. Like the arithmetic mean, the standard deviation is the measure of variability most often used in statistics. These concepts play a very important role in statistics and are always present in inferential and experimental statistics.

In descriptive statistics, the main task is to summarize data. Two fundamental statistical concepts in this regard are measures of central tendency and variability. Measures of central tendency aim to identify the most typical case in the data. In other words, they describe the central location of the data. The mean, the median, and the mode are the main measures of central tendency. The arithmetic mean is the most widely used measure of central tendency. In simple terms, the mean is the sum of all the scores divided by the total number of scores. The median represents the score that divides the distribution into two equal halves. Finally, the mode is the score that appears most often in a distribution.

3. Inferential Statistics: Hypothesis Testing and Statistical Significance

This chapter will describe three techniques for studying the relationship between two variables: cross-tabulation, the chi-square test of independence, and measures of association. Cross-tabulation is a method for examining the relationship between two categorical (nominal) variables. The chi-square test is a method for assessing the strength of the relationship between the variables in a cross-tabulation table. It determines whether the observed relationship in the table is unlikely to have occurred by chance alone (where such a relationship is said to exist) or is likely to have occurred by chance alone (where such a relationship is said not to exist). Measures of association provide a way of describing the strength and direction of the relationship between two categorical (nominal) variables. They are useful for cross-tabulation tables. This webinar will describe each technique and how to perform it with IBM SPSS software.

In studying statistics, a student learns about the entire process of a research investigation. Additionally, they study the various ways in which data is collected and the role of probability in selecting research strategies. This knowledge is important because conducting a study in a haphazard way makes the conclusions of the study concerning how one variable is related to another variable suspect. Statistics helps us derive conclusions concerning variable relationships. This is important because without formal training in statistics, studying the different facets of how relationships between variables are assessed in research can be very confusing.

4. Common Statistical Tests in Psychology Research

– Factor analysis: This is used to investigate the relationship between two or more latent variables. For example, researchers may use factor analysis to explore whether the characteristics of mood include four separate dimensions, such as anxiety and happiness.

– Chi-squared test: This is used to investigate the relationship between two categorical variables (for example, gender and weight loss group).

– Multiple regressions: This is used to assess the relationship and prediction strength between one dependent and two or more independent variables.

– Correlation analysis (e.g., Pearson correlation, Spearman correlation): These are used to measure correlation strength and direction between two or more continuous variables. For example, researchers may use the Pearson correlation to assess the strength and direction of the relationship between the participants’ mood and hours spent in front of the computer.

– Analysis of variance (ANOVA): This is used to compare means across more than two groups. Similar to the t-test, there are different variants of ANOVA including one-way ANOVA (used to compare means across more than two groups), repeated measures ANOVA (used when participants’ performance is measured at various time points), and factorial ANOVA (used to study the effects of two or more independent variables).

– T-test: This is used to compare the means between two groups. There are different variants of the t-test ranging from the independent sample t-test to the paired samples t-test. For example, researchers may use the independent samples t-test to compare the weight loss of people following diet A and diet B. Researchers may use the paired sample t-test to compare the participants’ happiness levels before and after the holiday.

1) Descriptive statistics 2) Inferential statistics 2.1) Common statistical tests used in psychology research

Below is a brief overview of the types of statistical tests used in psychology research, including:

Types of statistical tests used in psychology research

5. Practical Application and Interpretation of Statistical Findings in Psychology

Throughout the research textbook, students were asked to perform all of the basic analytical procedures using the most commonly used statistics programs in the behavioral sciences so they would develop more familiarity with the data collection procedures that produced all of the findings presented in the text. After presenting the results of basic behavioral research, hand-coded to ensure that research data could be used to perform the statistical analyses during a statistics course in which students would learn to identify which statistics procedures would be most appropriate for addressing specific research hypotheses, instructors may wish to have students repeat some or all of these analyses to ensure that students had acquired proficiency. If either new data must be collected specifically for educational purposes or research data had been collected for diagnostic purposes that prohibited performing the same statistical tests presented in the text, we were concerned that using the data available to us to illustrate certain procedures could lead to a lack of generalizability.

An essential aspect of research is that the sample data researchers gather should help them answer the research question they are interested in. Throughout their years of study, students in psychology may learn about many different findings generated by many different studies, but they are considerably less likely to hear many of the specific details of how other researchers actually used data to answer their questions about human behavior. Without more detailed information, it is often easy for students to forget that these findings represent research, produced by someone who followed specific procedures during data collection and analysis to produce these results. To move students beyond focusing on graphs and, instead, to focus on the data and analyses that produced the results, we describe some of the most common hypotheses that are likely to be tested in multiple behavioral research courses and illustrate how to collect and analyze the necessary data using either the procedures built into the most commonly used statistics programs or by applying common formulas manually.